By Gautam S. Muralidhar, PhD student, with Professors Alan C. Bovik and Mia K. Markey
UT researchers are developing novel computer-aided detection and diagnosis strategies (CAD) for breast cancer. Specifically, the research is focused on the automatic detection and diagnosis of spiculated masses, a type of breast cancer finding, commonly observed on mammography (routinely used screening modality for the detection of breast cancer). Collaborators include UT ECE professor Dr. Alan C. Bovik, director of LIVE, UT BME professor Dr. Mia K. Markey, director of BMIL, and researchers at the Department of Diagnostic Radiology, UT M D Anderson Cancer Center.
Spiculated masses are highly malignant and failing to detect these findings early can prove fatal. Unfortunately, detecting these findings is not easy as these masses are invariably submerged in the dense tissue background. Studies have shown spiculated masses account for a fairly large proportion of missed cancers by both radiologists and CAD algorithms.
UT researchers have developed a novel model-based computer-aided detection framework for the detection of spiculated masses on mammography.1 As a part of this framework, they have invented a new class of filters called Spiculated Lesion Filters (SLFs). SLFs are a bank of multi-scale pattern matching filters, which are a new class of complex quadrature filters. What makes the design of these filters novel is that the filters are parameterized from a statistical model of shape measurements of real spiculated masses obtained from mammograms. Their innovative methods for improving the detection performance of SLFs use two ideas: (1) applying a &ldquo
