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Proceedings Paper

A new case-based CAD scheme using a hierarchical SSIM feature extraction method to classify between malignant and benign cases
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Paper Abstract

The purpose of this study is to assess feasibility of developing a new case-based computer-aided diagnosis (CAD) scheme of mammograms based on a tree-based analysis of SSIM characteristics of the matched bilateral local areas of left and right breasts to predict likelihood of cases being malignant. We assembled a dataset involving screening mammograms acquired from 1000 patients. Among them, 500 cases were positive with cancer detected and verified, while other 500 cases had benign masses. Both CC and MLO view of the mammograms were used for feature extraction in this study. A CAD scheme was applied to preprocess the bilateral mammograms of the left and right breasts, generate image maps in the special domain, compute SSIM-based image features between the matched bilateral mammograms, and apply a support vector machine model to classify between malignant and benign cases. For performance evaluation, CAD scheme was trained and tested using a 10-fold cross-validation method. The area under a receiving operating characteristic curve (AUC) was computed as an index of performance evaluation. Using the poll of 12 extracted SSIM features, the CAD scheme yielded a performance level of AUC = 0.84±0.016, which is significantly higher than using each individual SSIM feature for the classification purpose (p < 0.05), and an odds ratio of 19.0 with 95% confidence interval of [15.3, 29.8]. Thus, this study supports the feasibility of applying an innovative method to develop a new case-based CAD scheme without lesion segmentation and demonstrates higher performance of new CAD scheme to classify between malignant and benign mammographic cases.

Paper Details

Date Published: 2 March 2020
PDF: 7 pages
Proc. SPIE 11318, Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications, 1131816 (2 March 2020); doi: 10.1117/12.2549130
Show Author Affiliations
Morteza Heidari, The Univ. of Oklahoma (United States)
Seyedehnafiseh Mirniaharikandehei, The Univ. of Oklahoma (United States)
Gopichandh Danala, The Univ. of Oklahoma (United States)
Yuchen Qiu, The Univ. of Oklahoma (United States)
Bin Zheng, The Univ. of Oklahoma (United States)


Published in SPIE Proceedings Vol. 11318:
Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications
Po-Hao Chen; Thomas M. Deserno, Editor(s)

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