A medical image retrieval framework in correlation enhanced visual concept feature space

This paper presents a medical image retrieval framework that uses visual concepts in a feature space employing statistical models built using a probabilistic multi-class support vector machine (SVM). The images are represented using concepts that comprise color and texture patches from local image regions in a multi-dimensional feature space. A major limitation of concept feature representation is that the structural relationship or spatial ordering between concepts are ignored. We present a feature representation scheme as visual concept structure descriptor (VCSD) that overcomes this challenge and captures both the concept frequency similar to a color histogram and the local spatial relationships of the concepts. A probabilistic framework makes the descriptor robust against classification and quantization errors. Evaluation of the proposed image retrieval framework on a biomedical image dataset with different imaging modalities validates its benefits.

• Type: Conference
• Year: 2009
• Where: CBMS
• Authors: Md. Mahmudur Rahman, Sameer Antani, George R. Thoma
• Keywords: CBMS 2009, Feature Space, Image Retrieval Framework, Medical Imaging, Visual Concept