Quantitative analysis of breast echotexture patterns in automated breast ultrasound images

• Published in: Medical physics (Lancaster) 2015-08, Vol.42 (8), p.4566-4578
• Main Authors: Chang, Ruey‐Feng, Hou, Yu‐Ling, Lo, Chung‐Ming, Huang, Chiun‐Sheng, Chen, Jeon‐Hor, Kim, Won Hwa, Chang, Jung Min, Bae, Min Sun, Moon, Woo Kyung
• Format: Article
• Language: English
• Subjects: 60 APPLIED LIFE SCIENCES; ACCURACY; Adult; Aged; Analysis of texture; ANIMAL TISSUES; Area Under Curve; automated breast ultrasound; Biological material, e.g. blood, urine; Haemocytometers; biomedical ultrasonics; breast cancer; Breast Neoplasms - classification; Breast Neoplasms - diagnostic imaging; cancer; Cluster Analysis; computer‐aided classification; Databases, Factual; Diagnosis using ultrasonic, sonic or infrasonic waves; Digital computing or data processing equipment or methods, specially adapted for specific applications; echotexture; Entropy; feature extraction; Female; fuzzy set theory; HEALTH HAZARDS; Humans; image classification; Image data processing or generation, in general; Image Interpretation, Computer-Assisted - methods; image texture; Image transforms; In which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines; Inference methods or devices; Logic and set theory; MAMMARY GLANDS; mammographic density; Mammography; medical image processing; Medical image segmentation; Middle Aged; Muscles; NEOPLASMS; RADIATION PROTECTION AND DOSIMETRY; Radiologists; Retrospective Studies; ROC Curve; Sensitivity and Specificity; Support Vector Machine; support vector machines; tumours; Ultrasonographic imaging; ULTRASONOGRAPHY; Ultrasonography, Mammary - methods; Young Adult
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1118/1.4923754

Purpose: Breast tissue composition is considered to be associated with breast cancer risk. This study aimed to develop a computer‐aided classification (CAC) system to automatically classify echotexture patterns as heterogeneous or homogeneous using automated breast ultrasound (ABUS) images. Methods: A CAC system was proposed that can recognize breast echotexture patterns in ABUS images. For each case, the echotexture pattern was assessed by two expert radiologists and classified as heterogeneous or homogeneous. After neutrosophic image transformation and fuzzy c‐mean clusterings, the lower and upper boundaries of the fibroglandular tissues were defined. Then, the number of hypoechoic regions and histogram features were extracted from the fibroglandular tissues, and the support vector machine model with the leave‐one‐out cross‐validation method was utilized as the classifier. The authors’ database included a total of 208 ABUS images of the breasts of 104 females. Results: The accuracies of the proposed system for the classification of heterogeneous and homogeneous echotexture patterns were 93.48% (43/46) and 92.59% (150/162), respectively, with an overall Az (area under the receiver operating characteristic curve) of 0.9786. The agreement between the radiologists and the proposed system was almost perfect, with a kappa value of 0.814. Conclusions: The use of ABUS and the proposed method can provide quantitative information on the echotexture patterns of the breast and can be used to evaluate whether breast echotexture patterns are associated with breast cancer risk in the future.