A Novel Approach for Information Content Retrieval and Analysis of Bio-Images using Datamining techniques.

In Bio-Medical image processing domain, contentbased analysis and Information retrieval of bioimages is very critical for disease diagnosis. Content-Based Image Analysis and Information Retrieval (CBIAIR) has become a significant part of information retrieval technology. One challenge in this area is that the ever-increasing number of bio-images acquired through the digital world makes the brute force searching almost impossible. Medical Image structural objects content and object identification plays significant role for image content analysis and information retrieval. There are basically three fundamental concepts for content-based bio-image retrieval, i.e. visualfeature extraction, multi-dimensional indexing, and retrieval system process. Each image has three contents such as: colour, texture and shape features. Colour and Texture both plays important image visual features used in Content-Based Image Retrieval to improve results. In this paper, we have presented an effective image retrieval system using features like texture, shape and color, called CBIAIR (Content-Based Image Analysis and Information Retrieval). Here, we have taken three different features such as texture, color and shape. Firstly, we have developed a new texture pattern feature for pixel based feature in CBIAIR system. Subsequently, we have used semantic color feature for color based feature and the shape based feature selection is done using the existing technique. For retrieving, these features are extracted from the query image and matched with the feature library using the feature weighted distance. After that, all feature vectors will be stored in the database using indexing procedure. Finally, the relevant images that have less matched distance than the predefined threshold value are retrieved from the image database after adapting the K-NN classifier.