2008
@article{RDB2008,
vgclass = {refpap},
author = {Md. Mahmudur Rahman and Bipin C. Desai and Prabir Bhattacharya},
title = {Medical image retrieval with probabilistic multi-class
support vector machine classifiers and adaptive similarity fusion},
journal = {Computerized Medical Imaging and Graphics},
volume = {32},
number = {2},
pages = {95--108},
month = {March},
year = {2008},
url = {http://dx.doi.org/10.1016/j.compmedimag.2007.10.001},
abstract = {We present a content-based image retrieval framework for
diverse collections of medical images of different modalities,
anatomical regions, acquisition views, and biological systems. For the
image representation, the probabilistic output from multi-class support
vector machines (SVMs) with low-level features as inputs are
represented as a vector of confidence or membership scores of
pre-defined image categories. The outputs are combined for
feature-level fusion and retrieval based on the combination rules that
are derived by following Bayes' theorem. We also propose an adaptive
similarity fusion approach based on a linear combination of individual
feature level similarities. The feature weights are calculated by
considering both the precision and the rank order information of top
retrieved relevant images as predicted by SVMs. The weights are
dynamically updated by the system for each individual search to produce
effective results. The experiments and analysis of the results are
based on a diverse medical image collection of 11,000 images of 116
categories. The performances of the classification and retrieval
algorithms are evaluated both in terms of error rate and
precision-recall. Our results demonstrate the effectiveness of the
proposed framework as compared to the commonly used approaches based on
low-level feature descriptors.},
}