IIAE CONFERENCE SYSTEM, The 1st IEEE/IIAE International Conference on Intelligent Systems and Image Processing 2013 (ICISIP2013)

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Breast Lesions Analysis Using Kinetic and Pyramid Spatial Features in Dynamic Contrast Enhanced MRI
Ju Ou-Yang, Pai-Jung Huang, Tung-Kuo Huang, Hsi-Jian Lee

Last modified: 2013-10-01


Local gradient based descriptors are very popular in the computer vision world recently; however, there are few applications of these descriptors in medical image analysis. The goal of this study is to classify breast lesions using local oriented gradients and temporal information. The proposed method combines the kinetics of dynamic contrast enhanced MRIs, which were examined at 0, 90 and 275 seconds separately, and extracts the histograms of oriented gradients descriptors from the interest points of kinetics extreme. The performance of the classifier was assessed with leave-one-out cross-validation among different patients. A total of 39 breast lesions of mass from 25 patients were selected; and the features extracted from these MRIs were further trained using J48 decision tree. The experimental results demonstrated that the average sensitivity and specificity are 0.83 and 0.95, respectively. By the combination of kinetics and pyramid spatial feature, it provides a new application of modern computer vision descriptors in breast MRI analysis.


Pyramid of histograms of oriented gradients; Breast lesion analysis; Classification; Dynamic contrast enhanced MRI


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