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Diagnostic Efficacy of All Series of Dynamic Contrast Enhanced Breast MR Images Using Gradient Vector Flow (GVF) Segmentation and Novel Border Feature Extraction for Differentiation Between Malignant

Keywords: BS DCE-MRI , GVF Snake Segmentation , Enhancement Sign , Fourier Factor , ROC Analysis

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Abstract:

Background/Objective: To discriminate between malignant and benign breast lesions;"nconventionally, the first series of Breast Subtraction Dynamic Contrast-Enhanced Magnetic"nResonance Imaging (BS DCE-MRI) images are used for quantitative analysis. In this study, we"ninvestigated whether using all series of these images could provide us with more diagnostic"ninformation."nPatients and Methods: This study included 60 histopathologically proven lesions. The steps of"nthis study were as follows: selecting the regions of interest (ROI), segmentation using Gradient"nVector Flow (GVF) snake for the first time, defining new feature sets, using artificial neural network"n(ANN) for optimal feature set selection, evaluation using receiver operating characteristic (ROC)"nanalysis."nResults: The results showed GVF snake method correctly segmented 95.3% of breast lesion"nborders at the overlap threshold of 0.4. The first classifier which used the optimal feature set"nextracted only from the first series of BS DCE-MRI images achieved an area under the curve"n(AUC) of 0.82, specificity of 60% at sensitivity of 81%. The second classifier which used the same"noptimal feature set but was extracted from all five series of these images achieved an AUC of"n0.90, specificity of 79% at sensitivity of 81%."nConclusion: The result of GVF snake segmentation showed that it could make an accurate"nsegmentation in the borders of breast lesions. According to this study, using all five series of BS"nDCE-MRI images could provide us with more diagnostic information about the breast lesion and"ncould improve the performance of breast lesion classifiers in comparison with using the first"nseries alone.

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