Audio Noise Reduction from Audio Signals and Speech Signals Using Wavelet Transform Matlab Project with Source Code

ABSTRACT

           Speech signal analysis is one of the important areas of research in multimedia applications. Discrete Wavelet technique is effectively reduces the unwanted higher or lower order frequency components in a speech signal. Wavelet-based algorithm for audio de-noising is worked out. We focused on audio signals corrupted with white Gaussian noise which is especially hard to remove because it is located in all frequencies. We use Discrete Wavelet transform (DWT) to transform noisy audio signal in wavelet domain. It is assumed that high amplitude DWT coefficients represent signal, and low amplitude coefficients represent noise. Using thresholding of coefficients and transforming them back to time domain it is possible to get audio signal with less noise. Our work has been modified by changing universal thresholding of coefficients which results with better audio signal. In this various parameters such as SNR, Elapsed Time, and Threshold value is analyzed on various types of wavelet techniques alike Coiflet, Daubechies, Symlet etc. In all these, best Daubechies as compared to SNR is more for Denoising and Elapsed Time is less than others for Soft thresholding. In using hard thresholding Symlet wavelet also works better than coiflet and Daubechies is best for all. Efficiency is 98.3 for de-noising audio signals which also gives us better results than various filters.

         Audio noise reduction system is the system that is used to remove the noise from the audio signals. Audio noise reduction systems can be divided into two basic approaches. The first approach is the complementary type which involves compressing the audio signal in some well-defined manner before it is recorded (primarily on tape). The second approach is the single-ended or non-complementary type which utilizes techniques to reduce the noise level already present in the source material—in essence a playback only noise reduction system. This approach is used by the LM1894 integrated circuit, designed specifically for the reduction of audible noise in virtually any audio source. Noise reduction is the process of removing noise from a signal.

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Palm Print Recognition System Using Image Processing Full Matlab Project with Source Code

ABSTRACT

                 Palm  print  authentication  is  one  of  the  modern  bio-metric techniques, which employs the vein pattern  in  the  human palm  to  verify  the  person.  The merits  of  palm  vein  on classical  bio-metric  (e.g.  fingerprint,  iris,  face)  are  a  low risk  of  falsification,  difficulty  of  duplicated  and  stability. In  this  Project,  a  new  method  is  proposed  for  personal verification  based  on  palm  Print  features.  In  the propose method,  the  palm  vein  images  are  firstly  enhanced  and then  the  features  are extracted  by  using  bank  of  Gabor filters. Bio-metric   technology   refers   to   a pattern   recognition system  which  depends  on  physical  or  behavioral  features for the  person  identification.

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Image and Text Steganography for Data Hiding Matlab Project with Source Code

ABSTRACT

                  One of the most important factors of information technology and communication has been the security of the information. For security purpose the concept of Steganography is being used. Imperceptibility and hiding capacity are very important aspects for efficient secret communication. In this paper a new steganography approach proposed based on LSB technique by using ALPHA channel on JPG cover images. for this method first the secrete image decomposed to bit streams and the data encrypted using an encryption method. On the cover side, an alpha channel is attached to the cover image and the data embedded into LSBs of RGBA channels.

                    Steganographic methods can be broadly classified based on the embedding domain, digital steganography techniques are classified into (i) spatial domain, (ii) frequency domain. In Spatial domain image steganography, cover image is first decomposed in to its bits planes and then LSB’s (Least Significant Bits) of the bits planes are replaced with the secret data bits. As LSB’s are redundant bits and contributes very less to overall appearance of the pixel, replacing it has no perceptible effect on the cover-image. Advantages are high embedding capacity, ease of implementation and imperceptibility of hidden data. The major drawback is its vulnerability to various simple statistical analysis methods.The most direct way to represent pixel's colour is by giving an ordered triple of numbers: red (R), green (G), and blue (B) that comprises that particular colour. The other way is to use a table known as palette to store the triples, and use a reference into the table for each pixel. For transparent images, extra channel called the Alpha value is stored along with the RGB channels. RGBA image stands for Red, Green, Blue, and Alpha. It extends the RGB colour model with the alpha value representing the transparency of pixels. The A value varies from 0 to 255, in which 0 means completely transparent while 255 means opaque. PNG images follow the RGBA colour model. Bit-plane slicing decomposition highlighting the contribution made to the total image appearance by specific bits. Assuming that each pixel is represented by 8-bits, the image is composed of eight 1-bit planes. Plane (0) contains the least significant bit and plane contains the most significant bit. Only the higher order bits (top four) contain the majority visually significant data. The other bit planes contribute the more subtle details.There are many researches in each of the steganography techniques, and a brief description of some of this research is presented. In this work an alpha channel is attached to a cover image with RGB colour system ( 24 bits depth ), the resulting image is a PNG (Portable Network Graphics ) image with RGBA colour system ( 32 bits depth ), on the other hand, using Bit-plane Slicing decomposition on the secrete image to compress it and transform the gray-level secrete image to a binary bit stream, then the secrete message bit streams will encrypted with a key and embedded in the four colour planes of the cover image.

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Video Steganography for Data Security Matlab Project with Source Code

ABSTRACT

            Information security has become the area of concern as a result of widespread use of communication medium over the internet. This paper focuses on the data security approach when combined with encryption and steganographic techniques for secret communication by hiding it inside the multimedia files. The high results are achieved by providing the security to data before transmitting it over the internet. The files such as images, audio, video contains collection of bits that can be further translated into images, audio and video. The files composed of insignificant bits or unused areas which can be used for overwriting of other data. This Project explains the proposed

algorithm using video steganography for enhancing data security.

                   The Steganography, Cryptography and Digital Watermarking techniques can be used to obtain security and privacy of data. The steganography is the art of hiding data inside another data such as cover medium by applying different steganographic techniques. While cryptography results in making the data human unreadable form called as cipher thus cryptography is scrambling of messages. Whereas the steganography results in exploitation of human awareness so it remains unobserved and undetected or intact. It is possible to use all file medium, digital data, or files as a cover medium in steganography. Generally steganography technique is applied where the cryptography is ineffective.

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Image Fusion Using Curvelet Transform Matlab Project with Source Code

ABSTRACT

                 This Image fusion is a data fusion technology which keeps images as main research contents. It refers to the techniques that integrate multi-images of the same scene from multiple image sensor data or integrate multi images of the same scene at different times from one image sensor. The image fusion algorithm based on Wavelet Transform which faster developed was a multi-resolution analysis image fusion method in recent decade. Wavelet Transform has good time frequency characteristics. It was applied successfully in image processing field. Nevertheless, its excellent characteristic in one-dimension can’t be extended to two dimensions or multi-dimension simply. Separable wavelet which was spanning by one-dimensional wavelet has limited directivity. This project introduces the Curvelet Transform and uses it to fuse images. The experiments show that the method could extract useful information from source images to fused images so that clear images are obtained. 

                Image fusion is the process of merging two images of the same scene to form a single image with as much information as possible. Image fusion is important in many different image processing fields such as satellite imaging, remote sensing and medical imaging. The study in the field of image fusion has evolved to serve the advance in satellite imaging and then, it has been extended to the field of medical imaging. Several fusion algorithms have been proposed extending from the simple averaging to the curvelet transform. The wavelet fusion algorithm has succeeded in both satellite and medical image fusion applications. The basic limitation of the wavelet fusion algorithm is in the fusion of curved shapes. Thus, there is a requirement for another algorithm that can handle curved shapes. So, the application of the curvelet transform for curved object image fusion would result in better fusion efficiency. 

            The main objective of medical imaging is to obtain a high resolution image with as much details as possible for the sake of diagnosis. MR and the CT techniques are medical imaging techniques. Both techniques give special sophisticated characteristics of the organ to be imaged. So, it is expected that the fusion of the MR and the CT images of the same organ would result in an integrated image of much more details. Due to the limited ability of the wavelet transform to deal with images having curved shapes, the application of the curvelet transform for MR and CT image fusion is presented.

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Image Compression Using DCT and DWT Matlab Project with Source Code

ABSTRACT

                  Image compression means reducing the size of graphics file, without compromising on its quality. Depending on the reconstructed image, to be exactly same as the original or some unidentified loss may be incurred, two techniques for compression exist. Two techniques are: lossy techniques and lossless techniques. This project presents DWT and DCT implementation because these are the lossy techniques .This project aims at the compression using DCT and Wavelet transform by selecting proper method, better result for PSNR have been obtained.

                     Compression refers to reducing the quantity of data used to represent a file, image or video content without excessively reducing the quality of the original data. Image compression is the application of data compression on digital images. The main purpose of image compression is to reduce the redundancy and irrelevancy present in the image, so that it can be stored and transferred efficiently. The compressed image is represented by less number of bits compared to original. Hence, the required storage size will be reduced, consequently maximum images can be stored and it can transferred in faster way to save the time, transmission bandwidth. For this purpose many compression techniques i.e. scalar/vector quantization, differential encoding, predictive image coding, transform coding have been introduced. Among all these, transform coding is most efficient especially at low bit rate. Depending on the compression techniques the image can be reconstructed with and without perceptual loss. In lossless compression, the reconstructed image after compression is numerically identical to the original image. In lossy compression scheme, the reconstructed image contains degradation relative to the original. Lossy technique causes image quality degradation in each compression or decompression step. In general, lossy techniques provide for greater compression ratios than lossless techniques i.e. Lossless compression gives good quality of compressed images, but yields only less compression whereas the lossy compression techniques lead to loss of data with higher compression ratio. The approaches for lossy compression include lossy predictive coding and transform coding. Transform coding, which applies a Fourier-related transform such as DCT and Wavelet Transform such as DWT are the most commonly used approach. JPEG is the best choice for digitized photographs. The Joint Photographic Expert Group (JPEG) system, based on the Discrete Cosine Transform (DCT), has been the most widely used compression method. Discrete Cosine Transform (DCT) is an example of transform coding. The DCT coefficients are all real numbers unlike the Fourier Transform. The Inverse Discrete Cosine Transform (IDCT) can be used to retrieve the image from its transform representation. DCT is simple when JPEG used, for higher compression ratio the noticeable blocking artifacts across the block boundaries cannot be neglected. The DCT is fast. It can be quickly calculated and is best for images with smooth edges. Discrete wavelet transform (DWT) has gained widespread acceptance in signal processing and image compression. In this paper we made a comparative analysis of two transform coding techniques DCT and DWT based on different performance measure such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Compression Ratio (CR).

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Breast Cancer Detection Using Image Processing Matlab Project with Source Code

ABSTRACT

                    Cancer is the major threat for human being health and its number of patients increasing word wide due to the global warming, even if there are new therapies and treatments proposed by research doctors, but level of cancer defines the ability of its cure. There are different types of cancers from which human being is suffering [male and female]. In this project we are focusing on breast cancer in women, rest allcancers are out of scope of this paper. Large number of women population is affected by the breast cancer. A different type of reasons causes the breast cancer such as X-Ray. For women’s, breast cancer is most common cancer, and it has been increasing since from last decade. The early detection of breast cancer helps to completely cure it through the treatment. The early detection is done by self-exam which can be done by woman in each month. This process is refereed as breast cancer early detection. However currently many hospitals and doctors uses the mammography test and resulted as effective technique for breast cancer early detection. The aim of this test is to perform early detection of breast cancer using characteristic masses detection as well as micro calcifications as these characteristics are considered as most important factor of breast cancer.

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Vehicle Number Plate Recognition Using Image Processing Matlab Project with Source Code

ABSTRACT

           This project presents Automatic Number Plate extraction, character segmentation and recognition for Indian vehicles. In India, number plate models are not followed strictly. Characters on plate are in different Indian languages, as well as in English. Due to variations in the representation of number plates, vehicle number plate extraction, character segmentation and recognition are crucial. We present the number plate extraction, character segmentation and recognition work, with english characters. Number plate extraction is done using Sobel filter, morphological operations and connected component analysis. Character segmentation is done by using connected component and vertical projection analysis. Automatic Number Plate Recognition (ANPR) system is an important technique, used in Intelligent Transportation System. ANPR is an advanced machine vision technology used to identify vehicles by their number plates without direct human intervention. It is an important area of research due to its many applications. The development of Intelligent Transportation System provides the data of vehicle numbers which can be used in follow up, analyses

and monitoring. ANPR is important in the area of traffic problems, highway toll collection, borders and custom security, premises where high security is needed, like Parliament, Legislative Assembly, and so on. The complexity of automatic number plate recognition work varies throughout the world. For the standard number plate, ANPR system is easier to read and recognize. In India this task becomes much difficult due to variation in plate model. 

                  The ANPR work is generally framed into the steps: Number plate extraction, character segmentation and character recognition. From the entire input image, only the number plate is detected and processed further in the next step of character segmentation. In character segmentation phase each and every character is isolated and segmented. Based on the selection of prominent features of characters, each character is recognized, in the character recognition phase. Extraction of number plate is difficult task, essentially due to: Number plates generally occupy a small portion of whole image; difference in number plate formats, and influence of environmental factors. This step affects the accuracy of character segmentation and recognition work. Different techniques are developed for number plate extraction. 

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Matlab Project With Source Code Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution Technique

ABSTRACT

                 One of the important techniques in digital image processing is to enhance images. Contrast enhancement is a method that is used to enhance images for viewing process or for further analysis of images. Main idea behind contrast enhancement techniques is to increase contrast and to preserve original brightness of images. In this paper a contrast enhancement technique is proposed that first segments histogram of image recursively and then applies Adaptive Gamma Correction with Weighting Distribution (AGCWD) Technique. The proposed technique is basically an improvement over AGCWD technique and aims to get better contrast enhancement and brightness preservation than AGCWD technique. The image enhancement is one of the significant techniques in digital image processing. It has an important role in various fields where images are to be understood and analyzed. Image enhancement is done on an image to improve its visual effects and quality or to make it more appropriate for further processing by another application. An image can have low contrast or bad quality due to a number of reasons like poor quality of imaging device, adverse external conditions at the time of image acquisition and many more. The contrast enhancement is one of the commonly used image enhancement method

                     Histogram equalization is the traditional technique for contrast enhancement. It basically maps gray levels based on probability distribution of input image. But image obtained by this method can produce undesirable effects in image and also original brightness of image is not preserved. Histogram equalization technique redistributes probability densities. Adaptive Gamma Correction with Weighting Distribution (AGCWD) technique is based on histogram modification method. This technique combines both gamma correction and histogram equalization techniques. Gamma correction is a transform based histogram modification technique that uses a varying parameter γ (gamma). Gamma correction method had problem that unvaried modification results for every image because a predefined value was used for all images. Histogram equalization had problem of under enhancement and over enhancement. So the AGCWD technique removed disadvantages of both gamma correction and Histogram Equalization techniques by combining both techniques and using a weighting function. In this technique gamma correction is applied using normalized cumulative density function (cdf). The AGCWD technique effectively enhances images. To further improve this technique to get better contrast enhancement and better brightness preservation an improvement is proposed in this project. Improvement proposed is based on recursive segmentation of histogram.

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Matlab Project with Source Code LSB based Audio Steganography for Enhancement in Security

ABSTRACT

                    Steganography is one of the best data hiding technique in the world which can be used to hide data without its presence felt. In today’s digital world most of us communicate via use of electronic media or internet. Most people among us remain unaware about the data loss or data theft which can happen on online transmission of data or message. Valuable information including personal data, messages transmitted through internet is vulnerable to hackers who may steal or decrypt our data or messages. This poject is about enhancing the data or message security with use of Audio Steganography using LSB algorithm to hide the message into multiple audio files. The message hidden by this application is less vulnerable to be stolen than other similar applications. This is due to following reasons: Firstly files are taken to hide high amount of message which enhance information hiding capacity. Secondly before being hidden, the message is broken into parts and shuffled randomly based on permutation generated at runtime so even if the LSB gets encountered the message is still unarranged and meaningless which enhances its security. This application is capable to carry large amount of information with greater security. 

                     As world is changing fast, people wants to save their time and resources to keep pace with the fast growing technology for the fulfillment of their needs. As internet has become a working need of the people like electronic banking, mobile banking, online shopping, transferring data from one place to another, gathering or retrieving of information. Data Security need is also increasing due to risk of theft, hacker, intruders, eavesdroppers, sabotage and unauthorized user. Security can be achieved using cryptography which encrypts message and make it unreadable from unauthorized people or watermarking technique provides copyright protection and the third one is steganography. Steganography is a uniquetechnique coming from old times which help user to hide their critical information without creating any suspicion. Information hiding can be done in various cover mediums like image, audio, video, text etc. Cover is chosen according to the need like audio steganography is an interesting medium because latest song or famous songs can be used to hide messages. Embedding techniques are chosen according to requirement. Some of the techniques are LSB coding, parity coding, phasecoding, spread spectrum and echo hiding. It can be used for hiding any information like secret formulas, images,private communicationand forensic authentication. As audio steganography uses audio as a cover medium,similarly this application too uses an audio as a platform for hiding the message. User provides input message in the form of text and chooses the audio wave file to hide the message. This application provides a smart and interactive interface for message hiding and its retrieval. Message is shuffled in random sequencebefore being hidden. Random sequence which is generated based on certain factors is used to shuffle the message before hiding it. This further enhances the data security.

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Matlab Project with Source Code Image Enhancement Using Histogram Equalization and Bi-histogram Equalization

ABSTRACT

               Image enhancement is one of the challenging issues in low level image processing. Contrast enhancement techniques are used for improving visual quality of low contrast images. Histogram Equalization (HE) method is one such technique used for contrast enhancement. It is a contrast enhancement technique with the objective to obtain a new enhanced image with a uniform histogram. In this paper, instead of using conventional image enhancement techniques, we proposed a method called genetic algorithm for the enhancement of images. This algorithm is fast and very less time consuming as compared to other techniques such as global histogram equalization by taking CDF and finding out the transfer function. Here in our work we are going to enhance images using histogram equalization of images by re-configuring their pixel spacing using optimization through GA (Genetic algorithm). We will get more optimized results with the use of GA with respect to other optimization techniques.

                Digital image enhancement is one of the most important image processing technology which is necessary to improve the visual appearance of the image or to provide a better transform representation for future automated image processing such as image analysis, detection, segmentation and recognition. Many images have very low dynamic range of the intensity values due to insufficient illumination and therefore need to be processed before being displayed. Large number of techniques have focused on the enhancement of gray level images in the spatial domain. These methods include histogram equalization, gamma correction, high pass filtering, low pass filtering, homomorphic filtering, etc. Image enhancement techniques are of particular interest in photography, satellite imagery, medical applications and display devices. Producing visually natural is required for many important areas such as vision, remote sensing, dynamic scene analysis, autonomous navigation, and biomedical image analysis.

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Automated Lung Cancer Detection in Medical Imaging Using Image Processing Matlab Project with Source Code

ABSTRACT

                   The most common cause of lung cancer is long‐term exposure to tobacco smoke, which causes 80‐90% of lung cancers. Cancer cells can be carried away from the lungs in blood, or lymph fluid that surrounds lung tissue. Lymph flows through lymphatic vessels, which drain into lymph nodes located in the lungs and in the center of the chest. Lung cancer often spreads toward the center of the chest because the natural flow of lymph out of the lungs is toward the center of the chest. As for the stages, in general there are four stages of lung cancer; I through IV. One of the major reason for non-accidental death is cancer. It has been proved that lung cancer is the topmost cause of cancer death in men and women worldwide. The death rate can be reduced if people go for early diagnosis so that suitable treatment can be administered by the clinicians within specified time. Cancer is, when a group of cells go irregular growth uncontrollably and lose balance to form malignant tumors which invades surrounding tissues. Cancer can be classified as Non-small cell lung cancer and small cell lung cancer. The various ways to detect lung cancer is by the use of image processing , pattern recognition and artificial neutral network to develop Computer aided diagnosis. In this project we use the techniques and algorithm used in image processing to detect cancer in three types of medical images. In this system first of all the medical images are recorded using a suitable imaging system. The images obtained are taken as input for the system where the image first go through the various steps of image processing like pre-processing, edge detection, morphological processing ,feature extraction.

                   Lung cancer which is among the five main types of cancer is a leading one to overall cancer mortality. Cancer is a serious health problem among various kinds of diseases. World Health Organization (WHO) reports that worldwide 7.6 million deaths are caused by cancer each year. Uncontrollable cell development in the tissues of the lung is called as lung cancer. Lung nodule is an abnormality that leads to lung cancer, characterized by a small round or oval shaped growth on the lung which appears as a white shadow in the CT scan. These uncontrollable cells restrict the growth of healthy lung tissues. If not treated, this growth can spread beyond the lung in the nearby tissue called metastasis and, form tumors. In order to preserve the life of the people who are suffered by the lung cancer disease, it should be pre‐diagnosed. The overall 5‐year survival rate for lung cancer patients increases from 14 to 49% if the disease is detected in time. So there is a need of pre-diagnosis system for lung cancer disease which should provide better results.

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Extraction of Red, Green and Blue Color from Color Images Matlab Project with Source Code

ABSTRACT

              A RGB image is a colorful image consisting of fixed values of color contents for each pixel. These color contents have different values ranging from 0 to 255.There are inbuilt functions and commands available in MATLAB to extract the required color content from a RGB image. If we required extracting a particular color from a RGB image, there are no integral commands that we use directly to do so. For such type of operations we required some algorithms. A simple algorithm is introduced having series of MATLAB commands and looping statements to extract a particular color from a RGB image. It is very helpful in image processing such as in pattern reorganization and mapping to find best equivalent used in many application fields. To extract a particular color from a RGB image or extract a particular area of interest for processing then we have no need to course the whole image. We have less number of values for processing further. It becomes easier to process the image for some other errands. So a simple algorithm or a simple method is introduced in this project to extract a requisite area of interest and a particular color from a RGB image.

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Seam Carving Using Image Processing Full Matlab Project with Source Code

ABSTRACT

                     Image Processing is an important technology for performing image operations. The analysis and manipulation on a digitized image helps to improve its quality. Image Processing offers a number of techniques to process an image such as Image Resizing, Image Enhancement etc. Image resizing is a key process for displaying visual media on different devices, and it has attracted much attention in the past few years. This paper defines preserving an important region of an image, minimizing distortions, and improving efficiency. Image Resizing can be more effectively reached with a better interpretation of image semantics. A new image importance map and a new seam criterion for image re-targeting is presented. Content-aware image resizing is a promising theme in computer vision and image processing. The seam carving method can effectively achieve image resizing which needs to define image importance to detect the salient context of images.

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Blood Group Detection Using Image Processing Matlab Project with Source Code

ABSTRACT

           Determining of blood types is very important during emergency situation before administering a blood transfusion. Presently, these tests are performed manually by technicians, which can lead to human errors. Determination of the blood types in a short period of time and without human errors is very much essential. A method is developed based on processing of images acquired during the slide test. The image processing techniques such as thresholding and morphological operations are used. The images of the slide test are obtained from the pathological laboratory are processed and the occurrence of agglutination are evaluated. Thus the developed automated method determines the blood type using image processing techniques. The developed method is useful in emergency situation to determine the blood group without human error.

         Before the blood transfusion it is necessary to perform certain tests. One of these tests is the determination of blood type. There are certain emergency situations which due to the risk of patient life, it is necessary to administer blood immediately. The tests currently available require moving the laboratory, it may not be time enough to determine the blood type and is administered blood type O negative considered universal donor and therefore provides less risk of incompatibility. However, despite the risk of incompatibilities be less sometimes that cause death of the patient and it is essential to avoid them. Thus, the ideal would be to determine the blood type of the patient. Secondly, the pre-transfusion tests are performed by technicians, which lead to human errors. Since these human errors can translate into fatal consequences, being one of the most significant causes of fatal blood transfusions is important to automate the procedure of these tests. Various blood type classification, diffusive reflectance, ABO Rh-D blood typing using simple morphological image processing.There is a scope for determining blood types using image processing techniques. Image segmentation algorithm for blood type classification and various image processing parameters are analyzed. Image features, such as color, texture, shape are analyzed. Low quality ancient document images and antibody agent analysis using image processing is explained. The slide test consists of the mixture of one drop of blood and one drop of reagent, being the result interpreted according to the occurrence or not of agglutination. The combination of the occurrence and nonoccurrence of the agglutination determines the blood type of the patient. Thus, the software developed in image processing techniques allows, through an image captured after the procedure of the slide test detect the occurrence of agglutination and consequently the blood type of the patient.

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Matlab Project with Source Code Currency Recognition Using Image Processing

ABSTRACT

                  The Reserve Bank is the one which issue bank notes in India. Reserve Bank, changes the design of bank notes from time to time. Reserve bank uses several techniques to detect fake currency. Common people faces many problems for the fake currency circulation and also difficult to detect fake currency, suppose that a common people went to a bank to deposit money in bank but only to see that some of the notes are fake, in this case he has to take the blame. As banks will not help that person. Some of the effects that fake currency has on society include a reduction in the value of real money; and inflation due to more fake currency getting circulated in the society or market which disturbs our economy and growth - an some illegal authorities an artificial increase in the money supply,a decrease in the acceptability of paper money and losses. Our aim is to help common man to recognize currency. Proposed system is based on image processing and makes the process automatic and robust. Shape information are used in our algorithm. Original Note Detection Systems are present in banks but are very costly. We are developing an image processing algorithm which will extract the currency features and compare it with features of original note image. This system is cheaper and can provide accuracy on the basics of visual contents of note.

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Target Detection Using Image Processing Matlab Project with Source Code

ABSTRACT

            Target detection in images is one of the most important applications of computer vision. Target detection in images is an approach by which we identify one or group of target objects in scene. It is very easy for a human to identify different objects in image but it’s difficult for a computer program to identify different objects. In target detection images are of different types such as visual, Aerial, IR, etc and they are under different categories (stationary target, moving target) and environments (atmospheric turbulences). It is difficult for a computer program to detect target in these restrictions. Till now so many researches are going on this approach. Some of those approaches are like using pattern recognition, wavelets, texture, connectivity component based approach, descriptors based methods and traditional thresholding methods so on. In developing a system, there are many difficulties like recognition accuracy, occupation of size (image) and execution time so on. These are the things that motivated me to solve some of those issues. My concentration is towards the approach that based on wavelet decomposition and wavelet coefficient features. 

             The approach of this project is to build a constructive approach for target detection in images that recognize the targets in different kinds of images. To make a system that can able to take images of any size, color or gray, and related to different environments. It should able to detect targets of any size and in any environment. Target detection in images can be done using several approaches; a simplified model. Wavelet transform is chosen as it is one of the novel techniques for solving target detection problem. 

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Brain Tumor Detection Using SOM Segmentation and K Clustering Matlab Project with Source Code

ABSTRACT

            Image processing is a process where input image is processed to get output also as an image or attributes of the image. Main aim of all image processing techniques is to recognize the image or object under consideration easier visually. Segmentation of images holds a crucial position in

the field of image processing. In medical imaging, segmentation is important for feature extraction, image measurements and image display. A tumor can be defined as a mass which grows without any control of normal forces. Real time diagnosis of tumors by using more reliable algorithms has been an active of the latest developments in medical imaging and detection of brain tumor in MR and

CT scan images. Hence image segmentation is the fundamental problem used in tumor detection. Image segmentation can be defined as the partition or segmentation of a digital image into similar regions with a main aim to simplify the image under consideration into something that is more meaningful and easier to analyze visually.

         Brain tumor is an abnormal growth caused by cells reproducing themselves in an uncontrolled manner. Magnetic Resonance Imager (MRI) is the commonly used device for diagnosis. In MR images, the amount of data is too much for manual interpretation and analysis. During the past few years, brain tumor segmentation in Magnetic Resonance Imaging(MRI) has become an emergent research area in the field of medical imaging system. Accurate detection of size and location of brain tumor plays a vital role in the diagnosis of tumor. Image processing is an active research area in which medical image processing is a highly challenging field. Image segmentation plays a significant role in image processing as it helps in the extraction of suspicious regions from the medical images. In this project an efficient algorithm is proposed for tumor detection based on segmentation of brain MRI images using KNN clustering.

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Rough Set Theory Based Brain Tumor Detection on Dicom Images Matlab Project with Source Code

ABSTRACT

               Brain tumor is a life threatening disease and its early detection is very important to save life. The tumor region can be detected by segmentation of brain Magnetic Resonance Image (MRI). Once a brain tumor is clinically suspected, radiologic evaluation is required to determine the location, the extent of the tumor, and its relationship to the surrounding structures. This information is very important and critical in deciding between the different forms of therapy such as surgery, radiation, and chemotherapy. The segmentation must be fast and accurate for the diagnosis purpose. Manual segmentation of brain tumors from magnetic resonance images is a tedious and time-consuming task.

Also the accuracy depends upon the experience of expert. Hence, the computer aided automatic segmentation has become important. MRI scanned images offer valuable information regarding brain tissues. MRI scans provide very detailed diagnostic pictures of most of the important organs and tissues in our body. It is generally painless and noninvasive. It does not produce ionizing radiation. So MRI is one of the best clinical imaging modalities. Several automated segmentation algorithms have been proposed. But still segmentation of MRI brain image remains as a challenging problem due to its complexity and there is no standard algorithm that can produce satisfactory results. The  aim of this research work is to propose and implement an efficient system for tumor detection and classification. The different steps involved in this work are image pre-processing for noise removal, feature extraction, segmentation and classification.

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Color Based Image Retrieval System Using Image Processing Matlab Project with Source Code

ABSTRACT

                  Advances in the data storage and image acquisition technologies have enabled the creation of large datasets. It is necessary to develop appropriate information systems to efficiently manage these collections. The most common approaches use Color-Based Image Retrieval (CBIR) system. The goal of CBIR system is to support image retrieval based on color. In a color based image retrieval system querying can be done by a query image. The goal is to find the images most resembling the query. In this Project we mainly focused on color histogram-based method. Color is most intuitive feature of an image and to describe colors generally histograms are adopted. Histogram methods have the advantages of speediness, low demand of memory space. Color features are the most important elements enabling human to recognize images. For categorizing images, color features can provide powerful information and they are used for image retrieval, so color based image retrieval is mostly used method. Color features of the images are generally represented by color histograms. Before using color histograms, however, we need to select and quantify a color space model and choose a distance metric. 

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Image Compression Using SPIHT Techniques Matlab Project with Source Code

ABSTRACT

             In recent years there has been an astronomical increase in the usage of computers for a variety of tasks. With the advent of digital cameras, one of the most common uses has been the storage, manipulation, and transfer of digital images. The files that comprise these images, however, can be quite large and can quickly take up precious memory space on the computer’s hard drive. In multimedia application, most of the images are in color and color images contain lot of data redundancy and require a large amount of storage space. Set partitioning in hierarchical trees (SPIHT) is wavelet based computationally very fast and among the best image compression based transmission algorithm that offers good compression ratios, fast execution time and good image quality. We will obtain a bit stream with increasing accuracy from EZW algorithm because of basing on progressive encoding to compress an image. All the numerical results were done by using matlab coding and the numerical analysis of this algorithm is carried out by sizing Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR) for standard Image.

                    Digital image compression is now essential. Internet teleconferencing, High Definition Television (HDTV), satellite communications and digital storage of images will not be feasible without a high degree of compression. Wavelets became popular in past few years in mathematics and digital signal processing area because of their ability to effectively represent and analyze data. Typical application of wavelets in digital signal processing is image compression. Image compression algorithms based on Discrete Wavelet Transform (DWT),such as Embedded Zero Wavelet (EZW) which produces excellent compression performance, both in terms of statistical peak signal to noise ratio (PSNR) and subjective human perception of the reconstructed image. Said and Pearlman further enhanced the performance of EZW by presenting a more efficient and faster implementation called set partitioning in hierarchical trees. SPIHT is one of the best algorithms in terms of the peak signal-to-noise ratio (PSNR) and execution time. Set partitioning in hierarchical trees provide excellent rate distortion performance with low encoding complexity.

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Image Restoration Using Multiple Thresholds Matlab Project with Source Code

ABSTRACT

               Image restoration is an art to improve the quality of image via estimating the amount of noises and blur involved in the image. With the passage of time, image gets degraded due to different atmospheric and environmental conditions, so it is required to restore the original image using different image processing algorithms. Application area varies from restoration of old images in museum and radar based image acquisition and restoration. Image restoration is based on the attempt to improve the quality of an image through knowledge of the physical process which led to its formation. The purpose of image restoration is to "compensate for" or "undo" defects which degrade an image. Degradation comes in many forms such as motion blur, noise, and camera mis-focus. In cases like motion blur, it is possible to come up with a very good estimate of the actual blurring function and "undo" the blur to restore the original image. In cases where the image is corrupted by noise, the best we may hope to do is to compensate for the degradation it caused. Image restoration differs from image enhancement in that the latter is concerned more with accentuation or extraction of image features rather than restoration of degradation's. 

               Restoration tries to reconstruct by using a priori knowledge of the degradation phenomenon. It deals with getting an optimal estimate of the desired result. Some restoration techniques are best achieved in the spatial domain, while there are some cases where frequency domain techniques are better suited The Purpose of smoothing is to reduce noise and improve the visual quality of the image. A variety of algorithms i.e. linear and nonlinear algorithms are used for filtering the images. Image filtering makes possible several useful tasks in image processing. A filtering technique can be applied to reduce the amount of unwanted noise in a particular image Another type of filter can be used to reverse the effects of blurring on a particular picture. Nonlinear filters have quite different behaviour as compared to linear filters. For nonlinear filters, the output or response of the filter does not follow the principles outlined earlier, particularly scaling and shift invariance. Moreover, a nonlinear filter can generate output that varies in a non-intuitive manner.

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Micro Calcification Detection Using Wavelet Transform Full Matlab Project with Source Code

ABSTRACT

            The World Health Organization's International agency for Research on Cancer in Lyon, France, estimates that more than 150 000 women worldwide die of breast cancer each year. The breast cancer is one among the top three cancers in American women. In United States, the American Cancer Society estimates that, 215 990 new cases of breast carcinoma has been diagnosed, in 2004. It is the leading cause of death due to cancer in women under the age of 65 . In India, breast cancer accounts for 23% of all the female cancers followed by cervical cancers (17.5%) in metropolitan cities such as Mumbai, Calcutta, and Bangalore. However, cervical cancer is still number one in rural India. Although the incidence is lower in India than in the developed countries, the burden of breast cancer in India is alarming. Organ chlorines are considered a possible cause for hormone-dependent cancers . Detection of early and subtle signs of breast cancer requires high-quality images and skilled mammographic interpretation. In order to detect early onset of cancers in breast screening, it is essential to have high-quality images. Radiologists reading mammograms should be trained in the recognition of the signs of early onset of, which may be subtle and may not show typical malignant features. Mammography screening programs have shown to be effective in decreasing breast cancer mortality through the detection and treatment of early onset of breast cancers.

          Emotional disturbances are known to occur in patient's suffering from malignant diseases even after treatment. This is mainly because of a fear of death, which modifies Quality Of Life (QOL). Desai et al.,reported an immuno histo chemical analysis of steroid receptor status in 798 cases of breast tumors encountered in Indian patients, suggests that breast cancer seen in the Indian population may be biologically different from that encountered in western practice. Most imaging studies and biopsies of the breast are conducted using mammography or ultrasound, in some cases, magnetic resonance (MR) imaging . Although by now some progress has been achieved, there are still remaining challenges and directions for future research such as developing better enhancement and segmentation algorithms. 

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Detection of Diabetic Retinopathy In Fundus Images Using Neural Network Full Matlab Project with Source Code

ABSTRACT

                  Diabetes is a group of metabolic disease in which a person has high blood sugar.  Diabetic Retinopathy (DR) is caused by the abnormalities in the retina due to insufficient insulin in the body. It can lead to sudden vision loss due to delayed detection of retinopathy. So that Diabetic patients require regular medical checkup for effective timing of sight saving treatment.  This is continuous and stimulating research area for automated analysis of Diabetic Retinopathy in Diabetic patients. A completely automated screening system for the detection of Diabetic Retinopathy can effectively reduces the burden of the specialist and saves cost as well as time. Due to noise and other disturbances that occur during image acquisition Diabetic Retinopathy may lead to false detection and this is overcome by various image processing techniques. Further the different features are extracted which serves as the guideline to identify and grade the severity of the disease. Based on the extracted features classification of the retinal image as normal or abnormal is carried out.  In this paper, we have presented detail study of various screening methods for Diabetic Retinopathy. Many researchers have made number of attempts to improve accuracy, productivity, sensitivity and specificity.

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License Plate Recognition Using Image Processing Matlab Project with Source Code

ABSTRACT

                The road becomes more pervasive, our country's road transport development, because of rapid labor management has not filled with actual needs, microelectronics, communications and computer technology in the transport sector of the application has greatly improved the traffic management efficiency. car license plates for automatic identification technology has been widely applied. car license plates automatically identify the entire process is divided into pre-processing, edge extraction, License Plate Positioning, character segmentation and character recognition 5 module, which character recognition process mainly consists of the following three components: 1) correctly to split text image area; 2) correct separation of a single text; 3) correctly identify a single character. The MATLAB software programming to achieve each and every part, and finally identify the license plate of a car. In the study of the same in which the issue of a concrete analysis, and processing. vehicle license plate recognition system as a whole is the main vehicle positioning and character recognition made up of two parts, one license plate positioning and can be divided into image pre-processing and edge extraction module and the licensing of the positioning and segmentation module; character recognition can be divided into character segmentation and feature extraction and a single character recognition two modules.

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Image Watermarking Based On DWT and DCT Matlab Project with Source Code

ABSTRACT 

             The authenticity & copyright protection are two major problems in handling digital multimedia. The Image watermarking is most popular method for copyright protection by discrete Wavelet Transform (DWT) which performs 2 Level Decomposition of original (cover) image and watermark image is embedded in Lowest Level (LL) sub band of cover image. Inverse Discrete Wavelet Transform (IDWT) is used to recover original image from watermarked image. And Discrete Cosine Transform (DCT) which convert image into Blocks of M bits and then reconstruct using IDCT. In this paper we have compared watermarking using DWT & DWT-DCT methods performance analysis on basis of PSNR, Similarity factor of watermark and recovered watermark.

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