Bimodal Multiparameter-Based Approach for Benign-Malignant Classification of Breast Tumors.
Proposed here is a breast tumor classification technique using conventional ultrasound B-mode imaging and a new elasticity imaging-based bimodal multiparameter index. A set of conventional ultrasound (US) and ultrasound elastography (UE) parameters are studied, and among those, the effective ones whose independent as well as combined performance is found satisfactory are selected. To improve the combined US performance, two new US parameters are proposed: edge diffusivity, which assesses edge blurriness to differentiate malignant from benign lesions, and the shape asymmetry factor, which quantifies tumor shape irregularity by comparing the tumor boundary with an ellipse fitted to the lesion. Then a new bimodal multiparameter characterization index is defined to discriminate 201 pathologically confirmed breast tumors of which 56 are malignant lesions, 79 are fibroadenomas, 42 are cysts and 24 are inflammatory lesions. The weights of the multiparameter bimodal index are optimally computed using a genetic algorithm (GA). To evaluate the performance variation of the index on different data sets, the tumors are categorized into three classes: malignant lesion versus fibroadenoma, malignant lesion versus fibroadenoma and cyst and malignant lesion versus fibroadenoma, cyst and inflammation. The test results reveal that the proposed bimodal index achieves satisfactory quality metrics (e.g., 94.64%-98.21% sensitivity, 97.24%-100.00% specificity and 96.52%-99.44% accuracy) for classification of the aforementioned three classes of breast tumors. Its performance is also observed to be better in totality of the quality metrics sensitivity, specificity, accuracy, positive predictive value and negative predictive value as compared with that of a conventional bimodal index as well as unimodal multiparameter indices based on US or UE. It is suggested that the proposed simple bimodal linear classifier may assist radiologists in better diagnosis of breast tumors and help reduce the number of unnecessary biopsies.
Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.
Sharmin R
SR
Ara
Department of Radiology and Imaging, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh.
Farzana
F
Alam
Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.
Md Hadiur
MH
Rahman
Department of Pathology, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh.
Shabnam
S
Akhter
Department of Plastic Surgery, Dhaka Medical College and Hospital, Dhaka, Bangladesh.
Rayhana
R
Awwal
Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh. Electronic address: khasan@eee.buet.ac.bd.
Kamrul
K
Hasan
D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D001943:Breast Neoplasms / Q000145:classification / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016217:Ultrasonography, Mammary / Q000379:methods*; D055815:Young Adult
B-mode indices; Bimodal indices; Breast tumor characterization; Computer-aided diagnosis; Elastographic indices; Elastography; Ultrasound