American Journal of Applied Sciences 11 (2): 258-265, 2014 ISSN: 1546-9239 ©2014 Science Publication doi:10.3844/ajassp.2014.258.265 Published Online 11 (2) 2014 (http://www.thescipub.com/ajas.toc) Corresponding Author: M. Mary Helta Daisy, Department of E.C.E, St. Xavier’s Catholic College of Engineering, Chunkankadai, India 258 Science Publications AJAS SOFT COMPUTING BASED MEDICAL IMAGE RETRIEVAL USING SHAPE AND TEXTURE FEATURES 1 M. Mary Helta Daisy and 2 S. Tamil Selvi 1 Department of E.C.E, St. Xavier’s Catholic College of Engineering, Chunkankadai, India 2 Department of E.C.E, National Engineering College, Kovilpatti, India Received 2013-10-06; Revised 2013-11-03; Accepted 2013-12-24 ABSTRACT Image retrieval is a challenging and important research applications like digital libraries and medical image databases. Content-based image retrieval is useful in retrieving images from database based on the feature vector generated with the help of the image features. In this study, we present image retrieval based on the genetic algorithm. The shape feature and morphological based texture features are extracted images in the database and query image. Then generating chromosome based on the distance value obtained by the difference feature vector of images in the data base and the query image. In the selected chromosome the genetic operators like cross over and mutation are applied. After that the best chromosome selected and displays the most similar images to the query image. The retrieval performance of the method shows better retrieval result. Keywords: Medical Image, Genetic Algorithm, Image Retrieval 1. INTRODUCTION Content based image retrieval is applied to visual contents for search images from the feature databases. In the medical field enormous images are produced and used for diagnostic purposes (Daisy and Selvi, 2012). The visual and multimedia data are steadily increasing. So, there is a need for fast retrieval methods apart from text- based retrieval. To search large multimedia databases for retrieval purposes, the visual and audio content use commonly (Muller et al., 2004). The visual content of images like color, texture and shape are use by the CBIR system. The unified approach usually have extraction of visual features, feature extraction, classification and similarity measure (Jaganathan and Vennila, 2013). The genetic algorithm is introduced to do the optimization, genetic algorithm to evolve chromosome population using the various genetic operations like selection, crossover and mutation. The objective of the selection and reproduction operator is to keep copies of best element in the chromosome to propagate for next generation. The fitness function is used to evaluate the chromosome. The crossover operator creates a new structure by exchanging element in the chromosome (Cuevasa et al., 2002). Optimization of CBIR is a time consuming task because the entire image in the database indexed again when each time the indexing algorithm is use for that purpose (Saadatmand-Tarzjan and Moghaddam, 2007). The crossover operator exchanges the subset of genes with pair of individuals and generates two others. Mutation operator replaces randomly selected genes from an individual (Santos et al., 2008). The genetic algorithm application begins with initial population and the individuals which are randomly generated. The fitness value of the each chromosome is evaluated and determines the appropriateness of the problem. The individual selected from the population before the recombination are called parent. After the recombination the chromosome are called as the children (Cho and Lee, 2002). The genetic cross over is applied to the feature vectors. The new chromosome after the crossover and feature vector in the database are compared based on similarity measure. The most similar chromosome are use for the next generation (Yoo and Cho, 2007).