Comput Math Methods Med - Assessment of feasibility to use computer aided texture analysis based tool for parametric images of suspicious lesions in DCE-MR mammography.

Tópicos

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Resumo

Our aim was to analyze the feasibility of computer aided malignant tumor detection using the traditional texture analysis applied on two-compartment-based parameter pseudoimages of dynamic contrast-enhanced magnetic resonance (DCE-MR) breast image data. A major contribution of this research will be the through-plane assessment capability. Texture analysis was performed on two-compartment-based pseudo images of DCE-MRI datasets of breast data of eight subjects. The resulting texture parameter pseudo images were inputted to a feedforward neural network classification system which uses the manual segmentations of a primary radiologist as a gold standard, and each voxel was assigned as malignant or nonmalignant. The classification results were compared with the lesions manually segmented by a second radiologist. Results show that the mean true positive fraction (TPF) and false positive fraction (FPF) performance of the classifier vs. primary radiologist is statistically as good as the mean TPF and FPF performance of the second radiologist vs. primary radiologist with a confidence interval of 95% using a one-sample t-test with a = 0.05. In the experiment implemented on all of the eight subjects, all malignant tumors marked by the primary radiologist were classified to be malignant by the computer classifier. Our results have shown that neural network classification using the textural parameters for automated screening of two-compartment-based parameter pseudo images of DCE-MRI as input data can be a supportive tool for the radiologists in the preassessment stage to show the possible cancerous regions and in the postassessment stage to review the segmentations especially in analyzing complex DCE-MRI cases.

Resumo Limpo

aim analyz feasibl comput aid malign tumor detect use tradit textur analysi appli twocompartmentbas paramet pseudoimag dynam contrastenhanc magnet reson dcemr breast imag data major contribut research will throughplan assess capabl textur analysi perform twocompartmentbas pseudo imag dcemri dataset breast data eight subject result textur paramet pseudo imag input feedforward neural network classif system use manual segment primari radiologist gold standard voxel assign malign nonmalign classif result compar lesion manual segment second radiologist result show mean true posit fraction tpf fals posit fraction fpf perform classifi vs primari radiologist statist good mean tpf fpf perform second radiologist vs primari radiologist confid interv use onesampl ttest experi implement eight subject malign tumor mark primari radiologist classifi malign comput classifi result shown neural network classif use textur paramet autom screen twocompartmentbas paramet pseudo imag dcemri input data can support tool radiologist preassess stage show possibl cancer region postassess stage review segment especi analyz complex dcemri case

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