Image modality classification: a late fusion method based on confidence indicator and closeness matrix

Abstract

Automatic recognition or classification of medical image modality can provide valuable information for medical image retrieval and analysis. In this paper, we discuss an application of SVM ensemble classifiers to the problem, and explore a confidence indicator based late fusion method to resolve ambiguity across competing classes. Using a matrix of closeness and a set of additional fusion rules, the proposed method improves the classification performance by only subjecting likely misclassified samples to a text-based classifier followed by additional fusion of both image-based classification and text-based classification results. An empirical evaluation using standard ImageClef2010 Medical Retrieval data show very promising performance for the proposed approach.