Python Source Code for Image Compression | Image Compression Using Python Project With Source Code

 ABSTRACT

            The lossless compression is that allows the original data to be perfectly reconstructed from the compressed data. Lossless compression programs do two things in sequence: the first step generates a statistical model for the input data, and the second step uses this model to map input data to bit sequences in such a way that probable. The main objective of image compression is to decrease the redundancy of the image data which helps in increasing the capacity of storage and efficient transmission. Image compression aids in decreasing the size in bytes of a digital image without degrading the quality of the image to an undesirable level. Image compression plays an important role in computer storage and transmission. The purpose of data compression is that we can reduce the size of data to save storage and reduce time for transmission. Image compression is a result of applying data compression to the digital image.

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Prof. Roshan P. Helonde

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Matlab Code for Liver Cancer Detection Using Image Processing Full Project Source Code

 ABSTRACT

        Liver cancer is one of the most severe types ofcancerous diseases which is responsible for the death of many patients. Liver cancer images have more noises which is difficult to diagnose the level of the tumor. It is a challenging task to automatically identify the tumor from images because of several anatomical changes in different patients. The cancer is difficult to find because of the presence of objects with same intensity level. In this proposed system, fully automated machine learning is used to detect the liver tumor from input image. Region growing technique is used to segment the region of interest. The textural feature are extracted from Gray level co-occurrence matrix (GLCM) of the segmented image. Extracted textural features are given as input to the designed SVM classifier system. Performance analysis of SVM classification of liver cancer image is studied. This will be useful for physician in better automatic diagnosis of liver caner from input images.

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Mr. Roshan P. Helonde

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Matlab Code for Blood Cancer Detection using Image Processing

ABSTRACT

             Leukemia 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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Mr. Roshan P. Helonde

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Matlab Code for Steganography using AES Algorithm Information Hiding Using AES Algorithm

ABSTRACT

            In today’s world, confidential information is growing due to various areas of works. Internet is the main area of transmission of digital data, so security must be more considered. Two common ways of providing security is cryptography and steganography. Employing a hybrid of cryptography and steganography enhances the security of data. This project employs LSB (Least significant Bit) as the steganography algorithm and AES algorithms as cryptographic algorithms to encrypt a message that should be hidden in a cover image. The results are represented in the form of execution time, PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error). The experimental results reveal that the algorithms achieve appropriate quality of stego image. They can be used as cryptographic algorithms to encrypt a message before applying steganography algorithms.

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Mr. Roshan P. Helonde

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Matlab Code for Image Fusion using PCA Principal Component Analysis full Source Code FINAL YEAR 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 PCA Principal Component Analysis on preserving the feature information for the test images.

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Mr. Roshan P. Helonde

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Image Enhancement using Histogram Equalization and Bi-Histogram Equalization

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

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Matlab Code for Plant Disease Detection using Neural Network

ABSTRACT

            The detection and classification of plant diseases are the crucial factors in plant production and the reduction of losses in crop yield. This paper proposes an approach for leaf disease detection and classification on plants using image processing. The algorithm presented has three basic steps: Image Pre-processing and analysis, Feature Extraction and Recognition of plant disease. The plant disease diagnosis is restricted by person’s visual capabilities as it is microscopic in nature. Due to optical nature of plant monitoring task, computer visualization methods are adopted in plant disease recognition. The aim is to detect the symptoms of the disease occurred in leaves in an accurate way. Once the captured image is pre-processed, the various properties of the plant leaf such as intensity, color and size are extracted and sent to with Neural Network for classification. 

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Image Steganography Data Hiding in Cover Image Full Source Code

ABSTRACT

            Today, digital communication has become an integral part of everyday life. Many applications are based on the Internet and communications must be made in secret. This is especially important when confidential information needs to be exchanged. As a result, the security of information transmitted over public channels is a fundamental problem, and as a result, the confidentiality and integrity of the data is essential to protect against unauthorized access and use. This caused the information concealment area to become unstable. Encryption and steganography are two of the most common methods you can use to ensure safety. With encryption, the data is converted to another form of gibberish and the encrypted data is transmitted. In Steganography, the image and text is transferred to the image, which is embedded in the image without affecting the quality of the image.This project suggests a new way to embed data into images with secret key.

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Mr. Roshan P. Helonde

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