Open AccessClassification of Breast Ultrasound Tomography by Using Textural Analysis
Author(s) -
Chih-Yu Liang,
TaiBeen Chen,
NanHan Lu,
Yichen Shen,
KuoYing Liu,
Shih-Yen Hsu,
ChiaJung Tsai,
Yiming Wang,
Chih-I Chen,
Weichang Du,
Yung-Hui Huang
Publication year - 2020
Publication title -
iranian journal of radiology./iranian journal of radiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.12
H-Index - 14
eISSN - 2008-2711
pISSN - 1735-1065
DOI - 10.5812/iranjradiol.91749
Subject(s) - medicine , ultrasound , receiver operating characteristic , breast ultrasound , kurtosis , region of interest , radiology , cohen's kappa , standard deviation , breast cancer , kappa , nuclear medicine , artificial intelligence , mammography , cancer , mathematics , computer science , statistics , geometry
Background: Ultrasound imaging has become one of the most widely utilized adjunct tools in breast cancer screening due to its advantages. The computer-aided detection of breast ultrasound is rapid development via significant features extracted from images. Objectives: The main aim was to identify features of breast ultrasound image that can facilitate reasonable classification of ultrasound images between malignant and benign lesions. Patients and Methods: This research was a retrospective study in which 85 cases (35 malignant [positive group] and 50 benign [negative group] with diagnostic reports) with ultrasound images were collected. The B-mode ultrasound images have manually selected regions of interest (ROI) for estimated features of an image. Then, a fractal dimensional (FD) image was generated from the original ROI by using the box-counting method. Both FD and ROI images were extracted features, including mean, standard deviation, skewness, and kurtosis. These extracted features were tested as significant by t-test, receiver operating characteristic (ROC) analysis and Kappa coefficient. Results: The statistical analysis revealed that the mean texture of images performed the best in differentiating benign versus malignant tumors. As determined by the ROC analysis, the appropriate qualitative values for the mean and the LR model were 0.85 and 0.5, respectively. The sensitivity, specificity, accuracy, positive predicted value (PPV), negative predicted value (NPV), and Kappa for the mean was 0.77, 0.84, 0.81, 0.77, 0.84, and 0.61, respectively. Conclusion: The presented method was efficient in classifying malignant and benign tumors using image textures. Future studies on breast ultrasound texture analysis could focus on investigations of edge detection, texture estimation, classification models, and image features.