Steganography is the science and art of secret communication between two sides that attempt to hide the content of the message. It is the science of embedding information into the cover image without causing a loss in the cover image after embedding. Steganography is the art and technology of writing hidden messages in such a manner that no person, apart from the sender and supposed recipient, suspects the lifestyles of the message. It is gaining huge attention these days as it does now not attract attention to its information's existence. In this project the secret message is encrypted first then DCT technique is applied. Moreover, Discrete Cosine Transform (DCT) is used to transform the image into the frequency domain. DCT algorithm is implemented in frequency domain in which the stego-image is transformed from spatial domain to the frequency domain and the payload bits are inserted into the frequency components of the cover image.
The processing of images by performing some operations in order to get enhanced images is called as image processing. It is widely used to diagnose the eye diseases in an easy and efficient manner. Several techniques has been developed for the early detection of DR on the basis of features such as blood. It includes the image enhancement processes like histogram equalization and adaptive histogram equalization for the detection of DR. The persistent damage caused to the retina is termed as the retinopathy. The condition of diabetic retinopathy (DR) happens with those who have diabetes that results in progressive damage to the retina. Due to high blood glucose levels it leads to the damage of small blood vessels in the retina and this may result into swelling of the retina. ie., DR is a diabetes related eye disease which occurs when the blood vessels in the retina become swelled and leaks fluid which ultimately leads to vision loss. The DR is regarded as a serious sight threatening condition. The main objective of this method is to detect DR (Diabetic Retinopathy) eye disease using Image Processing techniques. The tool used in this method is MATLAB and it is widely used in image processing. This project proposes a method for Extraction of Blood Vessels from the medical image of human eye-retinal fundus image that can be used in ophthalmology for detecting DR. This method utilizes an approach of Adaptive Histogram Equalization using CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm with Convolutional Neural Networks algorithm implementation. The result shows that affected DR is detected in fundus image and the DR is not detected in the healthy fundus image and upto 98% of Accuracy can be achieved in the detection of DR Project.
Skin diseases are more common than other diseases. Skin diseases may be caused by fungal infection, bacteria, allergy, or viruses, etc. A skin disease may change texture or color of the skin. In general, skin diseases are chronic, infectious and sometimes may develop into skin cancer. The advancement of lasers and Photonics based medical technology has made it possible to diagnose the skin diseases much more quickly and accurately. But the cost of such diagnosis is still limited and very expensive. So, image processing techniques help to build automated screening system for dermatology at an initial stage. The extraction of features plays a key role in helping to classify skin diseases. Computer vision has a role in the detection of skin diseases in a variety of techniques. Due to deserts and hot weather, skin diseases are common in various country. We proposed an image processing-based method to detect skin diseases. This method takes the digital image of disease effect skin area, then use image analysis to identify the type of disease. This project is developed in matlab using image processing techniques with up to 98% Accuracy.
Leaf Recognition is now emerging for research purposes. Leaf recognition technology plays an important role in plant classification and its key issue lies in whether selected features are stable and have good ability to discriminate different kinds of leaves. It is well known that the correct way to extract plant features involves plant recognition based on leaf images. In Agriculture, vegetables plants have become an important source of energy and source of living for farmers. Correctly identifying a vegetable leaf allows farmers to differentiate between vegetables as well as a vegetable seedling and weed in the garden. With so many varieties of leafy greens coming from our local farmers each week, it can be difficult to figure out vegetable it belongs to. Though these leaves may appear similar at a glance, they are actually quite unique in terms of Shape, Texture and Color. And with the increasing use of innovative computer technology, digitalized ways have become a possibility for plant identification. The proposed system will solve the problem of determining the vegetables just through the photograph of their leaves. In particular, identification process is carried out by gathering leaves detached from the plants, treated and stained prior to the imaging. Recognition of Vegetable Leaf using Matlab project, is to create an Informative Vegetable’s Leaf Recognition using Matlab to help the farmers, botanist and Agricultural Researchers in identifying a vegetable and its common details in a convenient and reliable way. The output parameters are used to compute well documented metrics for the statistical and shape. Base on the study, the following conclusion are drawn: The system can extract various parameters from the leaf’s image that will be used in identifying Vegetable`s from the extracted leaf parameters, the system provides the statistical analysis and general information of the identified leaf.
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