Content Based Medical Image Retrieval with Texture Content Using Gray Level Co-occurrence Matrix and K-Means Clustering Algorithms

Problem statement: Recently, there has been a huge progress in collec tion of varied image databases in the form of digital. Most of the users found it difficult to search and retrieve required images in large collections. In order to provide an effective and efficient search engine tool, the sy stem has been implemented. In image retrieval system, th ere is no methodologies have been considered directly to retrieve the images from databases. Ins tead of that, various visual features that have bee n considered indirect to retrieve the images from dat abases. In this system, one of the visual features such as texture that has been considered indirectly into images to extract the feature of the image. T hat featured images only have been considered for the r etrieval process in order to retrieve exact desired images from the databases. Approach: The aim of this study is to construct an efficient image retrieval tool namely, "Content Based Medical Image Retrieval with Texture Content using Gray Level Co-occurrence Matrix (GLCM) and k-Means Clustering algorithms". This image retrieval tool is capable of retrieving images based on the texture f eature of the image and it takes into account the P re- processing, feature extraction, Classification and retrieval steps in order to construct an efficient retrieval tool. The main feature of this tool is us ed of GLCM of the extracting texture pattern of the image and k-means clustering algorithm for image cl assification in order to improve retrieval efficiency. The proposed image retrieval system con sists of three stages i.e., segmentation, texture feature extraction and clustering process. In the s egmentation process, preprocessing step to segment the image into blocks is carried out. A reduction i n an image region to be processed is carried out in the texture feature extraction process and finally, the extracted image is clustered using the k-means algorithm. The proposed system is employed for domain specific based search engine for medical Images such as CT-Scan, MRI-Scan and X-Ray. Results: For retrieval efficiency calculation, conventional measures namely precision and recall were calculated using 1000 real time medical images (100 in each category) from the MATLAB Workspace database. For selected query images from the MATLAB-Image Processing tool Box-Workspace Database, the proposed tool was tested and the precision and recall results were presented. Th e result indicates that the tool gives better performance in terms of percentage for all the 1000 real time medical images from which the scalable performance of the system has been proved. Conclusion: This study proposed a model for the Content Based Medical Image Retrieval System by using texture feature in calculating the Gray Level Co Occurrence matrix (GLCM) from which various statistical measures were computed in order to increasing similarities between query image and dat abase images for improving the retrieval performance along with the large scalability of the databases.