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Proceedings Paper

Content-based image retrieval utilizing explicit shape descriptors: applications to breast MRI and prostate histopathology
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Paper Abstract

Content-based image retrieval (CBIR) systems, in the context of medical image analysis, allow for a user to compare a query image to previously archived database images in terms of diagnostic and/or prognostic similarity. CBIR systems can therefore serve as a powerful computerized decision support tool for clinical diagnostics and also serve as a useful learning tool for medical students, residents, and fellows. An accurate CBIR system relies on two components, (1) image descriptors which are related to a previously defined notion of image similarity and (2) quantification of image descriptors in order to accurately characterize and capture the a priori defined image similarity measure. In many medical applications, the morphology of an object of interest (e.g. breast lesions on DCE-MRI or glands on prostate histopathology) may provide important diagnostic and prognostic information regarding the disease being investigated. Morphological attributes can be broadly categorized as being (a) model-based (MBD) or (b) non-model based (NMBD). Most computerized decision support tools leverage morphological descriptors (e.g. area, contour variation, and compactness) which belong to the latter category in that they do not explicitly model morphology for the object of interest. Conversely, descriptors such as Fourier descriptors (FDs) explicitly model the object of interest. In this paper, we present a CBIR system that leverages a novel set of MBD called Explicit Shape Descriptors (ESDs) which accurately describe the similarity between the morphology of objects of interest. ESDs are computed by: (a) fitting shape models to objects of interest, (b) pairwise comparison between shape models, and (c) a nonlinear dimensionality reduction scheme to extract a concise set of morphological descriptors in a reduced dimensional embedding space. We utilized our ESDs in the context of CBIR in three datasets: (1) the synthetic MPEG-7 Set B containing 1400 silhouette images, (2) DCE-MRI of 91 breast lesions, (3) and digitized prostate histopathology dataset comprised of 888 glands. For each dataset, each image was sequentially selected as a query image and the remaining images in the database were ranked according to how similar they were to the query image based on the ESDs. From this ranking, area under the precision-recall curve (AUPRC) was calculated and averaged over all possible query images, for each of the three datasets. For the MPEG-7 dataset bull's eye accuracy for our CBIR system is 78.65%, comparable to several state of the art shape modeling approaches. For the breast DCE-MRI dataset, ESDs outperforms a set of NMBDs with an AUPRC of 0.55 ± 0.02. For the prostate histopathology dataset, ESDs and FDs perform equivalently with an AUPRC of 0.40 ± .01, but outperform NMBDs.

Paper Details

Date Published: 11 March 2011
PDF: 13 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79621I (11 March 2011); doi: 10.1117/12.878428
Show Author Affiliations
Rachel Sparks, Rutgers Univ. (United States)
Anant Madabhushi, Rutgers Univ. (United States)


Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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