Improving the performance of computer-aided classification of breast lesions using a new feature fusion method

Warid Islam; Gopichandh Danala; Huong Pham; Bin Zheng

doi:10.1117/12.2611841

4 April 2022 Improving the performance of computer-aided classification of breast lesions using a new feature fusion method

Warid Islam, Gopichandh Danala, Huong Pham, Bin Zheng

Author Affiliations +

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 120330E (2022) https://doi.org/10.1117/12.2611841
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Computer-Aided Diagnosis (CAD) schemes used to classify suspicious breast lesions typically include machine learning classifiers that are trained using features computed from either the segmented lesions or fixed regions of interest (ROIs) covering the lesions. Both methods have advantages and disadvantages. In this study, we investigate a new approach to train a machine learning classifier that fuses image features computed from both the segmented lesions and the fixed ROIs. We assembled a dataset with 2,000 mammograms. Based on lesion center, a ROI is extracted from each image. Among them, 1,000 ROIs depict verified malignant lesions and rest include benign lesions. An adaptive multilayer region growing algorithm is applied to segment suspicious lesions. Several sets of statistical features, texture features based on GLRLM, GLDM and GLCM, Wavelet transformed features and shape-based features are computed from the original ROI and segmented lesion, respectively. Three support vector machines (SVM) are trained using features computed from original ROIs, segmented lesions, and fusion of both, respectively, using a 10-fold cross-validation method embedded with a feature reduction method, namely a random projection algorithm. By applying the area under the ROC curve (AUC) as an evaluation index, our study results reveal no significant difference between AUC values computed using classification scores generated by two SVMs trained with features computed from original ROIs or segmented lesions. However, utilizing the fused features, AUC of SVM increases more than 10% (p<0.05). This study demonstrates that image features computed using the segmented lesions and the fixed ROIs contain complementary discriminatory information. Thus, fusing these features can significantly improve CAD performance.

Conference Presentation

Citation Download Citation

Warid Islam, Gopichandh Danala, Huong Pham, and Bin Zheng "Improving the performance of computer-aided classification of breast lesions using a new feature fusion method", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120330E (4 April 2022); https://doi.org/10.1117/12.2611841

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