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

Overview

Journal J Med Biol Eng

Specialty Biomedical Engineering

Date 2015 May 12

PMID 25960706

Citations 7

Authors

Zhuhuang Zhou

Shuicai Wu

King-Jen Chang

Wei-Ren Chen

Yung-Sheng Chen

Wen-Hung Kuo

Chung-Chih Lin

Po-Hsiang Tsui

Affiliations

Soon will be listed here.

Abstract

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), > 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, > 0.05). Half-contour SDD (72 % accuracy, 76 % sensitivity, 68 % specificity, 0.81 AUC, < 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, > 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.

Citing Articles

Stiffness in breast masses with posterior acoustic shadowing: significance of ultrasound real time shear wave elastography.

Luo H, Li J, Shi Y, Xiao X, Wang Y, Wei Z BMC Med Imaging. 2022; 22(1):71.

PMID: 35430798 PMC: 9013446. DOI: 10.1186/s12880-022-00797-3.

Incorporating the Breast Imaging Reporting and Data System Lexicon with a Fully Convolutional Network for Malignancy Detection on Breast Ultrasound.

Hsieh Y, Hsu F, Dai S, Huang H, Chen D, Shia W Diagnostics (Basel). 2022; 12(1).

PMID: 35054233 PMC: 8774546. DOI: 10.3390/diagnostics12010066.

Classification of malignant tumours in breast ultrasound using unsupervised machine learning approaches.

Shia W, Lin L, Chen D Sci Rep. 2021; 11(1):1418.

PMID: 33446841 PMC: 7809485. DOI: 10.1038/s41598-021-81008-x.

Utility of CT imaging in a novel form of high-dose-rate intraoperative breast radiation therapy.

Hassinger T, Showalter T, Schroen A, Brenin D, Berger A, Libby B J Med Imaging Radiat Oncol. 2018; 62(6):835-840.

PMID: 30102019 PMC: 6283680. DOI: 10.1111/1754-9485.12790.

Blood Smear Image Based Malaria Parasite and Infected-Erythrocyte Detection and Segmentation.

Tsai M, Yu S, Chan Y, Jen C J Med Syst. 2015; 39(10):118.

PMID: 26289625 DOI: 10.1007/s10916-015-0280-9.

References

Xiao G, Brady M, Noble J, Zhang Y . Segmentation of ultrasound B-mode images with intensity inhomogeneity correction. IEEE Trans Med Imaging. 2002; 21(1):48-57. DOI: 10.1109/42.981233. View

Zhou Z, Wu W, Wu S, Tsui P, Lin C, Zhang L . Semi-automatic breast ultrasound image segmentation based on mean shift and graph cuts. Ultrason Imaging. 2014; 36(4):256-76. DOI: 10.1177/0161734614524735. View

Moon W, Lo C, Cho N, Chang J, Huang C, Chen J . Computer-aided diagnosis of breast masses using quantified BI-RADS findings. Comput Methods Programs Biomed. 2013; 111(1):84-92. DOI: 10.1016/j.cmpb.2013.03.017. View

Chen D, Chang R, Wu W, Moon W, Wu W . 3-D breast ultrasound segmentation using active contour model. Ultrasound Med Biol. 2003; 29(7):1017-26. DOI: 10.1016/s0301-5629(03)00059-0. View

McCavert M, ODonnell M, Aroori S, Badger S, Sharif M, Crothers J . Ultrasound is a useful adjunct to mammography in the assessment of breast tumours in all patients. Int J Clin Pract. 2009; 63(11):1589-94. DOI: 10.1111/j.1742-1241.2009.02102.x. View

Madabhushi A, Yang P, Rosen M, Weinstein S . Distinguishing lesions from posterior acoustic shadowing in breast ultrasound via non-linear dimensionality reduction. Conf Proc IEEE Eng Med Biol Soc. 2007; 2006:3070-3. DOI: 10.1109/IEMBS.2006.260189. View

Huang Y, Jiang Y, Chen D, Moon W . Level set contouring for breast tumor in sonography. J Digit Imaging. 2007; 20(3):238-47. PMC: 3043893. DOI: 10.1007/s10278-006-1041-6. View

Drukker K, Giger M, Mendelson E . Computerized analysis of shadowing on breast ultrasound for improved lesion detection. Med Phys. 2003; 30(7):1833-42. DOI: 10.1118/1.1584042. View

Drukker K, Giger M, Horsch K, Kupinski M, Vyborny C, Mendelson E . Computerized lesion detection on breast ultrasound. Med Phys. 2002; 29(7):1438-46. DOI: 10.1118/1.1485995. View

10.

Siegel R, Naishadham D, Jemal A . Cancer statistics, 2013. CA Cancer J Clin. 2013; 63(1):11-30. DOI: 10.3322/caac.21166. View

11.

Shan J, Cheng H, Wang Y . Completely automated segmentation approach for breast ultrasound images using multiple-domain features. Ultrasound Med Biol. 2012; 38(2):262-75. DOI: 10.1016/j.ultrasmedbio.2011.10.022. View

12.

Gomez W, Leija L, Alvarenga A, Infantosi A, Pereira W . Computerized lesion segmentation of breast ultrasound based on marker-controlled watershed transformation. Med Phys. 2010; 37(1):82-95. DOI: 10.1118/1.3265959. View

13.

Madabhushi A, Metaxas D . Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions. IEEE Trans Med Imaging. 2003; 22(2):155-69. DOI: 10.1109/TMI.2002.808364. View

14.

Kilday J, Palmieri F, Fox M . Classifying mammographic lesions using computerized image analysis. IEEE Trans Med Imaging. 1993; 12(4):664-9. DOI: 10.1109/42.251116. View

15.

Huang Q, Lee S, Liu L, Lu M, Jin L, Li A . A robust graph-based segmentation method for breast tumors in ultrasound images. Ultrasonics. 2011; 52(2):266-75. DOI: 10.1016/j.ultras.2011.08.011. View

16.

Zhou Z, Wu S, Chang K, Chen W, Chen Y, Kuo W . Classification of Benign and Malignant Breast Tumors in Ultrasound Images with Posterior Acoustic Shadowing Using Half-Contour Features. J Med Biol Eng. 2015; 35(2):178-187. PMC: 4414937. DOI: 10.1007/s40846-015-0031-x. View

17.

Huang Y, Chen D . Watershed segmentation for breast tumor in 2-D sonography. Ultrasound Med Biol. 2004; 30(5):625-32. DOI: 10.1016/j.ultrasmedbio.2003.12.001. View

18.

Diao X, Zhang X, Wang T, Chen S, Yang Y, Zhong L . Highly sensitive computer aided diagnosis system for breast tumor based on color Doppler flow images. J Med Syst. 2010; 35(5):801-9. DOI: 10.1007/s10916-010-9461-8. View

19.

Giger M, Karssemeijer N, Schnabel J . Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer. Annu Rev Biomed Eng. 2013; 15:327-57. DOI: 10.1146/annurev-bioeng-071812-152416. View

20.

Chou Y, Tiu C, Hung G, Wu S, Chang T, Chiang H . Stepwise logistic regression analysis of tumor contour features for breast ultrasound diagnosis. Ultrasound Med Biol. 2001; 27(11):1493-8. DOI: 10.1016/s0301-5629(01)00466-5. View