Soft Computing in Digital Image Processing using Artificial Neural Network and Genetic Algorithm

Abstract

The image data are effective for acquisition, processing and analysis which make it highly suitable for sensitive applications. The quality of the medical image is crucial and it determines the efficiency of the image analytical system. On the other hand, obtaining the needed image is a significant problem when the application deals with an enormous amount of image data. Understanding these research issues, this work considers issues such as medical image quality enhancement and required image retrieval from a massive image database by incorporating Soft Computing (SC) approach. This research work considers issues in the area of medical image processing, such as image quality enhancement and CBIR. A Medical Image Integrated Possessions Assisted Soft Computing Techniques (MIPSCT) for Optimized Image Fusion with Less Noise and High Contour Detection?. A heuristic learning approach for the computation of data processing and then introduces a soft computing approach in digital image processing using Artificial Neural Networks (ANN) and Genetic Algorithm Framework (GAF). The performances of all the proposed approaches are analyzed by considering standard performance measures such as Peak-Signal-to-Noise Ratio (PSNR), Absolute Mean Error (AME), accuracy, sensitivity, contrast ratio, matching rate, error rate, selectivity and so on. The attained results are compared with the existing approaches and the proposed works perform well.