How to Plot MATLAB Graph with Colors, Markers, Line Specification Complete Details

This is a typical single color graph as shown below.


This graph still looks good as you are drawing a single graph on the MATLAB display.

Each color describes one graph and that makes the graph self-descriptive.

How can you decorate your MATLAB graph?

In this tutorial, you will learn to plot the colorful graphs in MATLAB.

I am also explaining by plotting a graph for the mathematical equation on MATLAB by using a single color, simple marker and line specification.

We will also see what are the most important and useful color coding functions, marker style and line-specification designing functions available in MATLAB.
By using these functions, you can draw the graph or waveform as per your color and plotting style choice. And you can easily understand the particular equation’s graph.

Let’s begin by considering the top three essential components to decorate your graph more meaningful.


  • Colour
  • Marker Style
  • Line Specification

Explanation of these three component functions are one-by-one,

MATLAB Plot Colors to draw the Graph
If you are drawing any picture on paper, you have different color pencils to use.

Likewise, for plotting the graph on MATLAB, we have different colors code or functions.

Widely, eight colors are used for MATLAB graph. And each color has the corresponding color code.

The below table shows color specification with the color code.

You can use these eight colors code to draw the colorful waveforms in MATLAB.

MATLAB Plot Marker | Different Style to Draw the Graph
Rather than just a simple line, do you want to make your waveform look different?

There are different marker style functions. For example, star format function, point format function, square format function, plus format function and so on.
In the below table, I am sharing the 12 marker style functions and its useful code for MATLAB graph.

How does the graph look different after using these marker styles? This we will see later in this tutorial example.

MATLAB Plot Line Specification | Code for MATLAB Graph

The four different spaceline codes are used for the plotting waveform or graph.

Check this below table, for line specification code.

The syntax for plotting graph to add color, marker, and line specification:

plot (x, y, 'colour marker linespec')

These codes are placed inside single inverted comma.

How to Plot MATLAB Graph with different colors, markers, and line specifier

How to change Colour, Marker, and Line-Specification in MATLAB plot?

Let’s take these two mathematical equations to plot the MATLAB graph.
Now its time to implement all three essentials components (color, marker, and line specifier) for decorating the MATLAB graph.

1)  y(x)=sin(2x) 

2) derivative of the same function d/dx(sin(2x)) on the same graph.

Solution:

The first mathematical equation is trigonometric.

y1`= sin (2x)

And it’s derivative of a mathematical equation of y(x) is

y2= d/dx (y1)= 2 cos (2x)

MATLAB Code:

Here is MATLAB code you can write to plot the graph for the function of f(x) and its d/dx (f(x)).

x=[0:0.01:10];
y1 = sin(2*x);
y2=2.*cos(2.x);
plot(x,y1,'r * -');
hold on
plot(x,y2,'k . :');
legend('sin', 'cos');

In this program, I have used the ‘legend’ function to label data series plotted on a graph. You can see in the below MATLAB output.

We are using different colors, markers and line specifications for plotting two different graphs.

Output:

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Audio Steganography using Matlab Project Source Code (Hiding Secret Text in Audio)

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. 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.  

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Matlab Code for Fruit Disease Detection and Classification using Image Processing Final Year Project

ABSTRACT
            Diseases in fruit cause devastating problem in economic losses and production in agricultural industry worldwide. In this project, a solution for the detection and classification of fruit diseases is proposed and experimentally validated. The image processing based proposed approach is composed of the following steps; in the first step K-Means clustering technique is used for the image segmentation, in the second step some features are extracted from the segmented image, and finally images are classified into one of the classes by using a Support Vector Machine. Our experimental results express that the proposed solution can significantly support accurate detection and automatic classification of fruit diseases. An early detection of fruit diseases can aid in decreasing such losses and can stop further spread of diseases. A lot of work has been done to automate the visual inspection of the fruits by machine vision with respect to size and color. However, detection of defects in the fruits using images is still problematic due to the natural variability of skin color in different types of fruits, high variance of defect types, and presence of stem/calyx. To know what control factors to consider next year to overcome similar losses, it is of great significance to analyze what is being observed.

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Matlab Code for Medical Image Fusion using Wavelet Transform Image Processing Project

ABSTRACT
            Different medical imaging techniques such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI) provide different perspectives for the human body that are important in the physical disorders or diagnosis of diseases .To derive useful information from multimodality medical image data medical image fusion has been used. In the medical field different radiometric scanning techniques can be used to evaluate and examine the inner parts of the body. The idea is to improve the image content by fusing images like computer tomography (CT) and magnetic resonance imaging (MRI) images, so as to provide as much details as possible for the sake of diagnosis. The objective of image fusion is to merge information from multiple images of the same image. The resultant image after image fusion is more suitable for human and machine perception and further helpful for image-processing tasks such as segmentation, feature extraction and object recognition. This project mainly presents image fusion using wavelet method for multispectral data and high-resolution data conveniently, quickly and accurately in MATLAB. Wavelet toolbox with abundant functions, provide a quick and convenient platform to improve image visibility. The work covers the selection of wavelet function, the use of wavelet based fusion algorithms on CT and MRI medical images, implementation of fusion rules and the fusion image quality evaluation. Matlab Results show that effectiveness of Image Fusion with Wavelet Transform on preserving the feature information for the test images.

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Matlab Code for Content Based Image Retrieval System Using Image Processing Final Year Project

ABSTRACT
                  Content Based Image Retrieval is an important technique which uses visual contents to retrieve images from large database. Many traditional methods have been employed to retrieve images. Relevance feedback is often a critical component when designing image databases. Relevance feedback interactively determines the user’s query by asking the user whether image is relevant or not. The use of support vector machine active learning algorithm makes this task more easy and effective. This algorithm selects the most informative images that satisfies the user’s requirement. Experiment results show that this algorithm achieves the effective results. Content based image retrieval(CBIR), also known as query by image content(QBIC)and content-based visual information retrieval (CBVIR) is the application of computer vision to the image retrieval problem. “Content-based” means that the search will analyze the actual contents of the image. The term ‘content’ in this context might refers color, shapes, textures or any other information like similarity matrix which compare pixel by pixel value that can be derived from the image itself. One key design task, when constructing image databases, is the creation of an effective relevance feedback components. Creating the database by relevance feedback or by hand labeling each image is time consuming, costly and subjective.

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Matlab Project Code for Lung Cancer Detection using Neural Network

ABSTRACT
             Lung cancer prevalence is one of the highest of cancers. One of the first steps in lung cancer diagnosis is sampling of lung tissues or biopsy. These tissue samples are then microscopically analyzed. This procedure is taken once imaging tests indicate the presence of cancer cells in the chest. Lung cancer diagnosis using lung images. One of them is that doctor still relies on subjective visual observation. A medical specialist must do thorough observation and accurate analysis in detecting lung cancer in patients. Hence, there is need for a system that is capable for detecting lung cancer automatically from microscopic images of biopsy. This method will improve the accuracy and efficiency for lung cancer detection. The aim of this research is to design a lung cancer detection system based on analysis of microscopic image of biopsy using digital image processing. Microscopic images of biopsy are feature extracted and classified. Neural Network method is implemented here to detection of lung cancer of lung samples.

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Matlab Code for Video Steganography Hiding Image in Video Source Code Final Year Project

ABSTRACT
          Steganography has become great area of interest for researchers as need for secure transaction of information is increasing day by day. Information may be text, image, audio or video. Steganography is a technique in which required information is hided in any other information such that the second information does not change significantly and it appears the same as original. This paper presents a novel approach of hiding image in a video. The proposed algorithm is replacing one LSB of each pixel in video frames. It becomes very difficult for intruder to guess that an image is hidden in the video as individual frames are very difficult to analyze in a video running at 30 frames per second. The process of analysis has been made more difficult by hiding each row of image pixels in multiple frames of the video, so intruder cannot even try to unhide image until he get full video.

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Matlab Project Barcode Recognition using Image Processing Full Source Code Final Year Project

ABSTRACT
            Barcodes are the symbols which represents the products information which is present at the backside of the product of a company. Barcodes are the thick and thin lines which are parallel to each other and it is in the form of rectangle shape. Barcodes are easy way to enter the information of the product than the manual methods. Its speed and reliability improves many operations like forwarding,
packing, reception, and manufacturing. Barcodes can be found in the libraries (in the backside of book), factories, blood banks, supermarkets etc. Barcodes are the efficient way to encoding the machine readable information on most books and products. The barcode reading system is based on image processing, providing more information than laser barcode readers at a time. So, it’s started gaining more importance than laser barcode readers. In this project, we are adopting some type of barcode algorithms which segmenting the barcode patterns from images. In this project, we are going to adopt the effective barcode algorithm for various types of images and also the suggestions to improve the efficiency of the information.

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Matlab Project on Shadow Detection and Removal using Image Processing

ABSTRACT
           Shadow Detection and removal from images is a challenging task in visual surveillance and computer vision applications. The appearance of shadows creates severe problems. Shadows cause problems in PC vision and image processing, such as detection of edge, video surveillance, stereo registration, object recognition, and image segmentation .To identify and remove the shadows from the image gives practical significance in image processing. Shadow forms when direct light cannot reach properly. This project proposes a simple method to detect and remove shadows from a single RGB image. A shadow detection method is selected on the basis of the mean value of RGB image in A and B planes of LAB equivalent of the image and shadow removal method is based on the identification of the amount of light impinging on a surface. The lightness of shadowed regions in an image is increased and then the color of that part of the surface is corrected so that it matches the lit part of the surface.

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Matlab Code for Audio Steganography Hiding Secret Text in Audio

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 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 sequence before 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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Leukemia Detection using Image Processing Matlab Project Source Code Final_Year_Project

ABSTRACT
             Blood cancer is the most prevalent and it is very much dangerous among all type of cancers. Early detection of blood cancer has the potential to reduce mortality and morbidity. There are many diagnostic technologies and tests to diagnose blood cancer. However many of these tests are extremely complex and subjective and depend heavily on the experience of the technician. To obviate these problems, image processing techniques is use in this study as promising modalities for detection of Leukemia blood cancer. The accuracy rate of the diagnosis of blood cancer by using image processing will be yield a slightly higher rate of accuracy then other traditional methods and will reduce the effort and time. We first discuss the preliminary of cell biology required to proceed to implement our proposed method. This project presents a new automated approach for blood Cancer detection and analysis from a given photograph of patient’s cancer affected blood sample. The proposed method is using image improvement, image segmentation for segmenting the different cells of blood, edge detection for detecting the boundary, size, and shape of the cells and finally Clustering for final decision of blood cancer based on the number of different cells.

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Matlab Code for Audio Watermarking Full Project Source Code Final Year Project

ABSTRACT
           Currently over the millions of digital audio files such as digital songs are copied illegally during file-sharing over the networks. It has resulted as the loss of revenue for music and broadcasting industries. The traditional protection schemes are no longer useful to protect copyright and ownership of multimedia objects. These challenges have prompted significant research in digital audio watermarking for protection and authentication. Watermarking is a technique, which is used in protecting digital information like text, images, videos and audio as it provides copyrights and ownership. The identity of the owner of the audio file can be hidden in the audio file which is called Watermark. Therefore, digital audio watermarking is the process of hiding some information into the audio file in such a way that the quality and the audibility of the audio is not affected. It helps to prevent forgery and impersonation of audio signal. Audio watermarking is more challenging than image watermarking due to the dynamic supremacy of hearing capacity over the visual field. This paper presents a novel audio watermarking using wavelet transformation. The proposed method involves Embedding and extraction of audio signal using wavelet transformation. The audio signal which is in .Wave or Mp3 format undergoes segmentation, transformation and embedding the watermarked data and at the last inverse transformation will be carried out. We attempt to develop an efficient method for hiding the information in the audio file such that the copyright information will be protected from illegal copying of the information

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Matlab code for Object Detection and Tracking Final Year Project

ABSTRACT
             Digital image processing is one of the most researched fields nowadays. The ever increasing need of surveillance systems has further on made this field the point of emphasis. Surveillance systems are used for security reasons, intelligence gathering and many individual needs. Object tracking and detection is one of the main steps in these systems. Different techniques are used for this task and research is vastly done to make this system automated and to make it reliable. In this research subjective quality assessment of object detection and object tracking is discussed in detail. In the proposed system the background subtraction is done from the clean original image by using distortion of color and brightness.  The detection of a moving object and tracking of different objects in a video or video sequence is a very important task in the surveillance videos, analysis and monitoring of traffic, tracking and detection of humans and different gesture recognition in human-machine interface. The technique of Object tracking can be explained to be the method of tracking the different number of objects in the video and also the certain directions those objects are traversing in and also to track the entrances to the surveillance site as per the unit time. The sophistication and the complexity of the system determine the resolution of the measurement. This system is often deployed in public places such as shopping malls, metro stations, airports and independent surveillance requests. Different approaches can be used for the surveillance and different technologies used as computer vision, infrared beams and thermal imaging. The reasons for object tracking are many For example People counting in retail stores for intelligence gathering can be regarded as one. 

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Matlab Project on Image Steganography Full Source Code

ABSTRACT
          Steganography is the one type of powerful technique which is science & art in which we have to write hidden messages, or we hide some important images, audio files, videos in this way that no-one, can find a hidden message which exists in cover images. Steganography is most strong techniques to mask the existence of unseen secret data within a cover object. Actually Stego means "Cover" graphy means "writing" that means It is nothing but we are hiding secret objects in cover image in which medium is different types of images. In practical feasible implementation practical approach would be to make the algorithm as strong as possible. In steganographed images are the most powerful objects that means cover objects, and therefore importance of image steganographed which can Embedding secret information inside images requires systematic computations. Various metrics were used to judge imperceptibility of steganography. The metrics in Matlab indicates how similar or dissimilar the stego-image compares with Cover.

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Matlab Project Lung Cancer Detection using Neural Network full Source Code

ABSTRACT
             Lung cancer prevalence is one of the highest of cancers, at 18 %. One of the first steps in lung cancer diagnosis is sampling of lung tissues or biopsy. These tissue samples are then microscopically analyzed. This procedure is taken once imaging tests indicate the presence of cancer cells in the chest. Lung cancer diagnosis using lung images. One of them is that doctor still relies on subjective visual observation. A medical specialist must do thorough observation and accurate analysis in detecting lung cancer in patients. Hence, there is need for a system that is capable for detecting lung cancer automatically from microscopic images of biopsy. This method will improve the accuracy and efficiency for lung cancer detection. The aim of this research is to design a lung cancer detection system based on analysis of microscopic image of biopsy using digital image processing. Microscopic images of biopsy are feature extracted and classified. Neural Network method is implemented here to detection of lung cancer of lung samples.

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Matlab Code for Diabetic Retinopathy Detection using Convolutional Neural Network CNN

ABSTRACT
            Diabetic Retinopathy (DR) is one of the major causes of blindness in the western world. Increasing life expectancy, indulgent lifestyles and other contributing factors mean the number of people with diabetes is projected to continue rising. Regular screening of diabetic patients for DR has been shown to be a cost-effective and important aspect of their care. The accuracy and timing of this care is of significant importance to both the cost and effectiveness of treatment. If detected early enough, effective treatment of DR is available; making this a vital process. The diagnosis of diabetic retinopathy (DR) through colour fundus images requires experienced clinicians to identify the presence and significance of many small features which, along with a complex grading system, makes this a difficult and time consuming task. In this project , we propose a CNN approach to diagnosing DR from digital fundus images and accurately classifying its severity. We develop a network with CNN architecture and data augmentation which can identify Diabetic Retinopathy.

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Matlab Code for Rice Leaf Disease Detection using Image Processing

ABSTRACT
            Agriculture is the main backbone for most of the developing/developed countries; agriculture production itself is the main feed for ever growing populations and it is the major source of income for the rural people/farmers especially in India. In India farmers are called “the backbone of India”. The main aim of the proposed system is to detect and classify the diseases in Rice leafs. Rice Diseases Classification comprises of two steps: first one is Detection, Extraction and Segmentation of diseases. Secondly, Feature extraction, Classification level of disease by using Support Vector Machine (SVM) classifiers respectively. The proposed system has been experimentally tested for our own dataset and results achieved are encouraging.

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Matlab code for Blood Group Detection using Neural Network

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 Pre-processing, Segmentation, Thresholding, Morphological operations and Neural Network  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. 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 non occurrence of the agglutination determines the blood type of the patient. 

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Hand Bone Fracture Detection using Image Processing Matlab Project with source Code

ABSTRACT
            Analysis of medical images plays a very important role in clinical decision making. For a long time it has required extensive involvement of a human expert. However, recent progress in data mining techniques, especially in machine learning, allows for creating decision models and support systems that help to automatise this task and provide clinicians with patient-specific therapeutic and diagnostic suggestions. In this project, we describe a study aimed at building a decision model (a classifier) that would predict the type of treatment (surgical vs. non-surgical) for patients with bone fractures based on their X-ray images. We consider two types of features extracted from images (structural and textural) and used them to construct multiple classifiers that are later evaluated in a computational experiment. Structural features are computed by applying the Hough transform, while textural information is obtained from Gray-level occurrence matrix. In research reported by other authors structural and textural features were typically considered separately. Our findings show that while structural features have better predictive capabilities, they can benefit from combining them with textural ones.

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Breast Cancer Detection using Neural Network Matlab Project 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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Audio Steganography (Hiding Secrete Information in Audio) Matlab Project Code

ABSTRACT
           Today’s large demand of internet applications requires data to be transmitted in a secure manner. Data transmission in public communication system is not secure because of interception and improper manipulation by eavesdropper. So the attractive solution for this problem is Steganography, which is the art and science of writing hidden messages in such a way that no one, apart from the sender and intend recipient, suspects the existence of the message, a form of security through obscurity. Audio steganography is the scheme of hiding the existence of secret information by concealing it into another medium such as audio file. The steganography application hides different types of data within a cover file. The resulting stego also contains hidden information, although it is virtually identical to the cover file.

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Matlab code for Brain Tumor Detection using Convolutional Neural Network (CNN)

ABSTRACT
           Brain tumor identification is really challenging task in early stages of life. But now it became advanced with various machine learning algorithms. Now a day’s issue of brain tumor automatic identification is of great interest. In Order to detect the brain tumor of a patient we consider the data of patients like MRI images of a patient’s brain. Here our problem is to identify whether tumor is present in patients brain or not. It is very important to detect the tumors at starting level for a healthy life of a patient. There are many literatures on detecting these kinds of brain tumors and improving the detection accuracies. In this project, we Estimate the brain tumor severity using Convolutional Neural Network algorithm which gives us accurate results.

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Image Fusion using Wavelet Transform Matlab Source code

ABSTRACT
            Different medical imaging techniques such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) provide different perspectives for the human body that are important in the physical disorders or diagnosis of diseases .To derive useful information from multimodality medical image data medical image fusion has been used. In the medical field different radiometric scanning techniques can be used to evaluate and examine the inner parts of the body. The idea is to improve the image content by fusing images like computer tomography (CT) and magnetic resonance imaging (MRI) images, so as to provide as much details as possible for the sake of diagnosis. The objective of image fusion is to merge information from multiple images of the same image. The resultant image after image fusion is more suitable for human and machine perception and further helpful for image-processing tasks such as segmentation, feature extraction and object recognition. This paper mainly presents image fusion using wavelet method for multispectral data and high-resolution data conveniently, quickly and accurately in MATLAB. Wavelet toolbox with abundant functions, provide a quick and convenient platform to improve image visibility. The work covers the selection of wavelet function, the use of wavelet based fusion algorithms on CT and MRI medical images, implementation of fusion rules and the fusion image quality evaluation. Matlab Results show that effectiveness of Image Fusion with Wavelet Transform on preserving the feature information for the test images.

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Handwritten Digit Recognition using Neural Network Matlab Source code

ABSTRACT
           Handwriting recognition is one of the compelling research works going on because every individual in this world has their own style of writing. It is the capability of the computer to identify and understand handwritten digits automatically. Because of the progress in the field of science and technology, everything is being digitalised to reduce human effort. Hence, there comes a need for handwritten digit recognition in many real-time applications. Many Machine Learning and Deep Learning Algorithms are developed which can be used for this digit classification. This project performs Digit Recognition and the analysis of accuracy of algorithms Neural Network.

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Mr. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +91-7276355704
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Image Fusion using Wavelet Transform and PCA Matlab Source code

ABSTRACT
            Different medical imaging techniques such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI) provide different perspectives for the human body that are important in the physical disorders or diagnosis of diseases .To derive useful information from multimodality medical image data medical image fusion has been used. In the medical field different radiometric scanning techniques can be used to evaluate and examine the inner parts of the body. The idea is to improve the image content by fusing images like computer tomography (CT) and magnetic resonance imaging (MRI) images, so as to provide as much details as possible for the sake of diagnosis. The objective of image fusion is to merge information from multiple images of the same image. The resultant image after image fusion is more suitable for human and machine perception and further helpful for image-processing tasks such as segmentation, feature extraction and object recognition. This project mainly presents image fusion using wavelet method for multispectral data and high-resolution data conveniently, quickly and accurately in MATLAB. Wavelet toolbox with abundant functions, provide a quick and convenient platform to improve image visibility. The work covers the selection of wavelet function, the use of wavelet based fusion algorithms on CT and MRI medical images, implementation of fusion rules and the fusion image quality evaluation. Matlab Results show that effectiveness of Image Fusion with Wavelet Transform on preserving the feature information for the test images.

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Mr. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +91-7276355704
Email: roshanphelonde@rediffmail.com
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Image Enhancement using Histogram Equalization Matlab Source code

ABSTRACT
             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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Mr. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +91-7276355704
Email: roshanphelonde@rediffmail.com
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Prostate Cancer Detection using Image Processing Matlab Source Code

ABSTRACT
          This project gives an overview of the method of detecting prostate cancer by associating Region of interest segmentation method with Support Vector Machine. Prostate cancer is commonly prevalent carcinoma detected in most of the male population. A diagnosis of prostate cancer was complicated due to unclear symptoms and involves many procedures. One of these procedures involves the study of prostate tissue biopsy to find cancer affected region. However, no boundary specified region was considered for further studies. Recent developmental techniques in the medical imaging field, especially in SVM, have paved the way for prostate carcinoma detection. The MRI image of the prostate gland is pre-processed to reduce noise effects and Region of interest is obtained with the svm and segmentation is done. The core idea of this project is to assume that every region of prostate tissue could be related to malignant or unnatural tissues.

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Mr. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +91-7276355704
Email: roshanphelonde@rediffmail.com
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Grape Leaf Disease Detection using Image Processing Matlab Source code

ABSTRACT
             Identification of the grape leaf disease is the main goal to prevent the losses and quality of agricultural product. In India grape fruit crop is widely grown. So disease detection and classification of grape leaf is very critical for sustainable agriculture. It’s not possible to farmer, to monitor continuously the grape disease manually. It requires the excessive processing time, tremendous amount of work, and some expertise in the grape leaf diseases. To detect and classify the grape disease we need fast automatic process so we use SVM classifier technique. This project presents mainly five stages, viz image acquisition, pre-processing, segmentation GLCM feature extraction and SVM classification. This project is proposed to benefit in the detection and classification of grape leaf disease using support vector machine (SVM) classifier.

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Mr. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +91-7276355704
Email: roshanphelonde@rediffmail.com
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Prof. Roshan P. Helonde
Mobile: +917276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com

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