Decision support system for breast lesions via dynamic contrast enhanced magnetic resonance imaging.

Physical and Engineering Sciences in Medicine
Gökçen ÇetinelSevda Gül

Abstract

The presented study aims to design a computer-aided detection and diagnosis system for breast dynamic contrast enhanced magnetic resonance imaging. In the proposed system, the segmentation task is performed in two stages. The first stage is called breast region segmentation in which adaptive noise filtering, local adaptive thresholding, connected component analysis, integral of horizontal projection, and breast region of interest detection algorithms are applied to the breast images consecutively. The second stage of segmentation is breast lesion detection that consists of 32-class Otsu thresholding and Markov random field techniques. Histogram, gray level co-occurrence matrix and neighboring gray tone difference matrix based feature extraction, Fisher score based feature selection and, tenfold and leave-one-out cross-validation steps are carried out after segmentation to increase the reliability of the designed system while decreasing the computational time. Finally, support vector machines, k- nearest neighbor, and artificial neural network classifiers are performed to separate the breast lesions as benign and malignant. The average accuracy, sensitivity, specificity, and positive predictive values of each classifier are calc...Continue Reading

References

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Methods Mentioned

BETA
feature extraction
biopsy

Software Mentioned

Decision Support System for Breast Lesions ( DSSBL )
DSSBL
Omega
GLCM
Breast Imaging and Reporting Data System ( BI - RADS )
ROCKIT

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