A multi-atlas approach to region of interest detection for medical image classification

Abstract

A common approach for image classification is based on image feature extraction and supervised discriminative learning. For medical image classification problems where discriminative image features are spatially distributed around certain anatomical structures, localizing the region of interest (ROI) essential for the classification task is a key to success. To address this problem, we develop a multi-atlas label fusion technique for automatic ROI detection. Given a set of training images with class labels, our method infers voxel-wise scores for each image showing how discriminative each voxel is for categorizing the image. We applied our method in a 2D cardiac CT body part classification application and show the effectiveness of the detected ROIs.