Multi-panel medical image segmentation framework for image retrieval system

The automatic segmentation of multi-panel medical images into sub-images improves the retrieval accuracy of medical image retrieval systems. However, the accuracy and efficiency of the available multi-panel medical image segmentation techniques are not satisfactory for multi-panel images containing homogenous color inter-panel borders and image boundary, heterogeneous color inter-panel borders, small size sub-images, or numerous number of sub-images. In order to improve the accuracy and efficiency, a Multi-panel Medical Image Segmentation Framework (MIS-Framework) is proposed and implemented based on locating the longest inter-panel border inside the boundary of the input image. We evaluated the proposed framework on a subset of imageCLEF 2013 dataset containing 2407 images. The proposed framework showed promising experimental results in terms of accuracy and efficiency on single panel as well as multi-panel image class identification and on sub-image separation as compared to the available techniques.