Selection of Spatiotemporal Features in Breast MRI to Differentiate between Malignant and Benign Small Lesions Using Computer-Aided Diagnosis. (12th July 2012)

• Record Type: Journal Article
• Authors: Steinbruecker, F.; Meyer-Baese, A.; Plant, C.; Schlossbauer, T.; Meyer-Baese, U.
• Other Names: Saez Juan Manuel Gorriz Academic Editor.
• Is Part Of: Advances in artificial neural systems. (2012)
• Publication Date: 2012-07-12
• Subjects: Neural networks (Computer science) -- Periodicals; Neural networks (Computer science); Periodicals; Electronic journals; 006.32
• DOI: 10.1155/2012/919281
• Languages: English
• ISSNs: 1687-7594

Abstract

Abstract : Automated detection and diagnosis of small lesions in breast MRI represents a challenge for the traditional computer-aided diagnosis (CAD) systems. The goal of the present research was to compare and determine the optimal feature sets describing the morphology and the enhancement kinetic features for a set of small lesions and to determine their diagnostic performance. For each of the small lesions, we extracted morphological and dynamical features describing both global and local shape, and kinetics behavior. In this paper, we compare the performance of each extracted feature set for the differential diagnosis of enhancing lesions in breast MRI. Based on several simulation results, we determined the optimal feature number and tested different classification techniques. The results suggest that the computerized analysis system based on spatiotemporal features has the potential to increase the diagnostic accuracy of MRI mammography for small lesions and can be used as a basis for computer-aided diagnosis of breast cancer with MR mammography.