Model-based Framework for the Detection of Spiculated Masses on Mammography
Background The detection of lesions on mammography is a repetitive and fatiguing task. Only three or four out of a thousand examined cases are malignant, and thus an abnormality may be overlooked. As a result, radiologists fail to detect 10% to 30% of cancers.
Computer-Aided Detection (CADe) systems have been developed to aid radiologists. These systems act as a second reader, thus eliminating the need for a second radiologist. However, the detection accuracy of current systems is much higher for detection of micro calcifications than for spiculated masses. We have designed a new model-based framework for the detection of spiculated masses on mammograms.
Invention Description This technology is a detection algorithm that:
a) enhances spicules through Spiculation Filtering and detects the spatial locations where the spicules converge b) detects the central mass region of the spiculated masses, and c) reduces the false positives due to normal linear structures.
The foundation of this algorithm is strong, as all the parameters are based on actual physical properties of spiculated masses measured by experienced radiologists.The algorithm, when tested on a set of 100 challenging images from the publicly available DDSM database, showed a sensitivity of 88% at 2.7 FPI (sensitivity is the fraction of regions marked as suspicious that are actually lesions and FPI (false positives per image) is the number of regions marked per image that are not lesions). This technique aims to find the highest risk abnormalities and will be a useful aid to radiologists in detecting breast cancer.
More data on the images used to test the technology can be made available on request.
The algorithm is modular and could be easily integrated with the existing CAD Systems. Employs a model-based, evidence-based approach New knowledge about the physical properties of spiculated masses or normal tissue structures can be easily incorporated into the framework of this algorithm.
Enhancement of linear structures via filtering in the Radon Domain A new class of filters, called spiculation filters, that were specifically designed to detect locations at which linear structures converge All filter parameters set based on measurements of spiculated masses made by radiologists Explicit models of normal structures used to reduce the number of false positive detections
Market Potential/Applications Computer-Aided Detection (CADe) mammography systems
Development Stage Lab/bench prototype
IP Status One U.S. patent application filed
UT Researcher Mehul P. Sampat, BE, Biomedical Engineering, The University of Texas at Austin Alan C. Bovik, Ph.D., Electrical and Computer Engineering, The University of Texas at Austin Mia K. Markey, Ph.D., Biomedical Engineering, The University of Texas at Austin
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