|
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.
|
