Computer Aided Diagnosis - Medical Image Analysis Techniques

Breast cancer is the second leading cause of death among women worldwide. Mammography is the basic tool available for screening to find the abnormality at the earliest. It is shown to be effective in reducing mortality rates caused by breast cancer. Mammograms produced by low radiation X-ray are difficult to interpret, especially in screening context. The sensitivity of screening depends on image quality and unclear evidence available in the image. The radiologists find it difficult to interpret the digital mammography; hence, computer-aided diagnosis (CAD) technology helps to improve the performance of radiologists by increasing sensitivity rate in a cost-effective way. Current research is focused toward the designing and development of medical imaging and analysis system by using digital image processing tools and the techniques of artificial intelligence, which can detect the abnormality features, classify them, and provide visual proofs to the radiologists. The computer-based techniques are more suitable for detection of mass in mammography, feature extraction, and classification. The proposed CAD system addresses the several steps such as preprocessing, segmentation, feature extraction, and classification. Though commercial CAD systems are available, identification of subtle signs for breast cancer detection and classification remains difficult. The proposed system presents some advanced techniques in medical imaging to overcome these difficulties

Texture Based Malware Pattern Identification and Classification

Malware texture pattern plays an essential role in defense against malicious instructions which were analyzed by malware analyst. It is identified as a security threat. Classifying malware samples based on static analysis which is a challenging task. This paper introduces an approach to classify malware variants as a gray scale image based on texture features such as different patterns of malware samples. Malicious samples are classified through the machine learning techniques. The proposed method experimented on malware dataset which is consisting of large number of malware samples. The similarities are calculated by texture analysis methods with Euclidian distance for various variants of malware families. The available samples are named by the Antivirus companies which can analyze through supervised learning techniques. The experimental results show that the effective identification of malware texture pattern through the image processing which gives better accuracy results compared to existing work

Mammography Image Enhancement using Linear, Nonlinear and Wavelet Filters with Histogram Equalization

In the worldwide, breast cancer is one of the major diseases among the women. In the modern medical science, there are plenty of newly devised methodologies and techniques for the timely detection of breast cancer. However, there are difficulties still exist for detecting breast cancer at an early stage for its diagnoses because of poor visualization and artifacts present in the mammography. Thus the Digital mammographic image preprocessing often requires, enhancement of the image to improve the quality while preserving important details. The proposed method works in three stages. First it removes all the artifacts present in the image. Second it denoise the image by using Linear, nonlinear and wavelet filters. Third, contrast of the image increased by histogram equalization. This method definitely helps to computer aided diagnosis system to increase the accuracy. The experimental results are tested on two standard datasets MIAS and DDSM.

VIP methods for Sports Video - an Analysis

Video Annotation has become challenging process in the field of Sports Video. An every event with respect to the game requires the precise gloss. This has been done with different Video Image Processing(VIP) Techniques using MATLAB tool. This paper elaborates the all efficient video processing methods applied on the different sports videos and these analyzed results will be tested for Kabaddi Game for image mosaicking in the current case stud

Performance analysis of medical image compression using DCT and FFT Transforms

There is a high demand for image compression since it reduces the computational time, which in turn reduces the storage and transmission costs. Image compression involves reducing excessive and irrelevant data while maintaining reasonable image quality. Image compression techniques such as the Discrete Cosine Transform (DCT) and Fast Fourier Transform (FFT) are the focus of this study. These tools were selected because of their wide application in image processing; one example is JPEG (Joint Photographic Experts Group), which uses DCT for compression. A comparison is made between DCT and FFT, two compression methods implemented in MATLAB. CT and MRI images are used for an experiment, the quality of an image is determined by various parameters. To perform DCT the filter mask is used and a threshold is used for FFT to keep the top coefficient values. The experimental findings are compared and evaluated in terms of Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR)

Impact of Edge Detection Algorithms on Different Types of Images using PSNR and MSE

Edge detection is the process of detecting sharp changes in image brightness in a digital image. It aids in the recognition of an object and its shape in an image. As a result, edge detection plays a vital role in image processing, especially in domains like segmentation, image registration, and object identification. This paper is an attempt to study the impact of several edge detection algorithms such as Sobel, Prewitt, Robert, Kirsch, Robinson, Laplacian of Gaussian (LOG) and Canny. The three different types of images such as medical , natural and satellite images are considered for experiment. Performance measures used for comparison are Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR)