Computer-Aided tumor diagnosis in 3-D breast elastography. (January 2018)

• Record Type: Journal Article
• Authors: Huang, Yao-Sian; Takada, Etsuo; Konno, Sachiyo; Huang, Chiun-Shen; Kuo, Ming-Hao; Chang, Ruey-Feng
• Is Part Of: Computer methods and programs in biomedicine. Volume 153(2018)
• Page Start: 201
• Page End: 209
• Publication Date: 2018-01
• Subjects: Elastography -- Diagnosis -- Breast -- Shape -- Ellipsoid; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.10.021
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Using 3-D Gradient vector flow for automatic tumor segmentation. The proposed feature sets, morphology and elastography, can increase the classification accuracy. Our CAD quantifies 3-D US image features to provide a promising diagnostic suggestion. Abstract: Background and objective: Breast cancer is the major cause of cancer-related mortality in women. However, the death rate can be effectively decreased if the breast cancer can be detected early and treated appropriately. In recent years, many studies have indicated that the elastography has the better diagnosis performance than conventional ultrasound (US). Method: In this study, the 3-D tumor contour is obtained by using the proposed segmentation methods and then the features containing texture information, shape information, ellipsoid fitting information are extracted respectively by using the segmented 3-D tumor contour and B-mode images, and the features containing elasticity information are calculated using the same contour and elastographic images. Results: In this experiment, totally 40 biopsy-proved lesions containing 20 benign tumors and 20 malignant tumors are used to evaluate the proposed computer-aided diagnosis (CAD) system. From the experimental results, the combination of shape, ellipsoid fitting and elastographic features has the best performance with accuracy 90.50% (36/40), sensitivity 85.00% (17/20), specificity 95.00% (19/20), and the area under the ROC curve Az 0.987. Conclusion: The result shows that tumors can be diagnosed more precisely by using the elastography images.