ABSTRACT

Image segmentation plays an important role in medical imaging by automating detection of false structures and other regions of interest. An image segmentation method partitions an image into multiple segments, representing an image into more meaningful, simpler and easier to analyze. Several general-purpose algorithm and techniques have been developed for image segmentation. This paper explains different segmentation techniques used in medical image analysis addressing the segmentation of abdominal and liver images as case study. Experiments are performed on abdominal and liver CT scan images and the outcomes of these segmentation techniques are discussed. Performance of the methods is presented on the basis of parameters namely, pixel values, mean and standard deviation.

Abstract-It is well known that the classic image compression techniques such as JPEG and MPEG have serious limitations at high compression rate; the decompressed image gets really fuzzy or indistinguishable. To overcome problems associated with conventional methods, artificial neural networks based method can be used. Genetic algorithm is a very powerful method for solving real life problems and this has been proven by applying to number of different applications. There is lots of interest to involve the GA with ANN for various reasons at various levels. Trapping in the local minima is one of the well-known problems of gradient decent based learning in ANN. The problem can be addressed using GA algorithm. But no work has been done to evaluate the performance of both learning methods from the image compression point of view. In this paper, we investigate the performance of ANN with GA in the application of image compression for obtaining optimal set of weights. Direct method of compression has been applied with neural network to get the additive advantage for security of compressed data. The experiments reveal that the standard BP with proper parameters provide good generalize capability for compression and is much faster compared to earlier work in the literature, based on cumulative distribution function. Further, the results obtained shows that general concept about GA, it performs better over gradient decent based learning, is not applicable for image compression.