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Classification of Benign and Malignant Breast Tumors in Ultrasound Images with Posterior Acoustic Shadowing Using Half-Contour Features
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Classification of Benign and Malignant Breast Tumors in Ultrasound Images with Posterior Acoustic Shadowing Using Half-Contour Features

Zhuhuang Zhou, Shuicai Wu, King-Jen Chang, Wei-Ren Chen, Chen Y.-S, Wen-Hung Kuo, Chung-Chih LinPo-Hsiang Tsui
卷.35(2)
11/04/2015
Web of Science ID: WOS:000353828200004

摘要

Breast tumor Half-contour feature Posterior acoustic shadowing (PAS) Standard deviation of degree (SDD) Ultrasound
Posterior acoustic shadowing (PAS) can bias breast tumor segmentation and classification in ultrasound images. In this paper, half-contour features are proposed to classify benign and malignant breast tumors with PAS, considering the fact that the upper half of the tumor contour is less affected by PAS. Adaptive thresholding and disk expansion are employed to detect tumor contours. Based on the detected full contour, the upper half contour is extracted. For breast tumor classification, six quantitative feature parameters are analyzed for both full contours and half contours, including standard deviation of degree (SDD), which is proposed to describe tumor irregularity. Fifty clinical cases (40 with PAS and 10 without PAS) were used. Tumor circularity (TC) and SDD were both effective full- and half-contour parameters in classifying images without PAS. Half-contour TC [74 % accuracy, 72 % sensitivity, 76 % specificity, 0.78 area under the receiver operating characteristic curve (AUC), p > 0.05] significantly improved the classification of breast tumors with PAS compared to that with full-contour TC (54 % accuracy, 56 % sensitivity, 52 % specificity, 0.52 AUC, p > 0.05). Half-contour SDD (72 % accuracy, 76 % sensitivity, 68 % specificity, 0.81 AUC, p < 0.05) improved the classification of breast tumors with PAS compared to that with full-contour SDD (62 % accuracy, 80 % sensitivity, 44 % specificity, 0.61 AUC, p > 0.05). The proposed half-contour TC and SDD may be useful in classifying benign and malignant breast tumors in ultrasound images affected by PAS.

相關連結

InCites亮點

本研究成果之相關指標(擷取自 InCites Benchmarking & Analytics)

合作類型
機構合作
國際合作
引用書目主題
1 Clinical & Life Sciences
1.119 Breast Cancer Scanning
1.119.583 Breast Cancer Imaging
Web Of Science研究領域
Engineering, Biomedical
ESI研究領域
Molecular Biology & Genetics

聯合國永續發展目標(SDGs)

此研究成果有助於達成以下目標:

#3 Good Health and Well-Being
#5 Gender Equality

來源:來自InCites的SDGs

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