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

Zhou Z, Wu S, Chang KJ, Chen WR, Chen YS, Kuo WH, Lin CC, Tsui PH - J Med Biol Eng (2015)

Bottom Line: Based on the detected full contour, the upper half contour is extracted.Fifty clinical cases (40 with PAS and 10 without PAS) were used.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).

View Article: PubMed Central - PubMed

Affiliation: Biomedical Engineering Center, College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124 China.

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), 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.

No MeSH data available.


Related in: MedlinePlus

a Breast ultrasound image. b Image after adaptive thresholding. c Tumor contour extracted using DE
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Fig1: a Breast ultrasound image. b Image after adaptive thresholding. c Tumor contour extracted using DE

Mentions: The DE segmentation method [24] is employed to detect the tumor contour in the binary image B. Figure 1b shows the result of using the proposed adaptive thresholding for an ultrasound image. Figure 1c shows the tumor contour extracted from the ultrasound image using DE.Fig. 1


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

Zhou Z, Wu S, Chang KJ, Chen WR, Chen YS, Kuo WH, Lin CC, Tsui PH - J Med Biol Eng (2015)

a Breast ultrasound image. b Image after adaptive thresholding. c Tumor contour extracted using DE
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4414937&req=5

Fig1: a Breast ultrasound image. b Image after adaptive thresholding. c Tumor contour extracted using DE
Mentions: The DE segmentation method [24] is employed to detect the tumor contour in the binary image B. Figure 1b shows the result of using the proposed adaptive thresholding for an ultrasound image. Figure 1c shows the tumor contour extracted from the ultrasound image using DE.Fig. 1

Bottom Line: Based on the detected full contour, the upper half contour is extracted.Fifty clinical cases (40 with PAS and 10 without PAS) were used.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).

View Article: PubMed Central - PubMed

Affiliation: Biomedical Engineering Center, College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124 China.

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), 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.

No MeSH data available.


Related in: MedlinePlus