Computer-aided classification of breast masses using speckle features of automated breast ultrasound images

2012 ◽  
Vol 39 (10) ◽  
pp. 6465-6473 ◽  
Author(s):  
Woo Kyung Moon ◽  
Chung-Ming Lo ◽  
Jung Min Chang ◽  
Chiun-Sheng Huang ◽  
Jeon-Hor Chen ◽  
...  
Keyword(s):  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Breast Masses ◽  
Computer Aided ◽  
Automated Breast Ultrasound

Computer-Aided Diagnosis for the Classification of Breast Masses in Automated Whole Breast Ultrasound Images

2011 ◽  
Vol 37 (4) ◽  
pp. 539-548 ◽  
Author(s):  
Woo Kyung Moon ◽  
Yi-Wei Shen ◽  
Chiun-Sheng Huang ◽  
Li-Ren Chiang ◽  
Ruey-Feng Chang
Keyword(s):  
Computer Aided Diagnosis ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Breast Masses ◽  
Computer Aided ◽  
Aided Diagnosis

Computer-Aided Tumor Detection Based on Multi-Scale Blob Detection Algorithm in Automated Breast Ultrasound Images

2013 ◽  
Vol 32 (7) ◽  
pp. 1191-1200 ◽  
Author(s):  
Woo Kyung Moon ◽  
Yi-Wei Shen ◽  
Min Sun Bae ◽  
Chiun-Sheng Huang ◽  
Jeon-Hor Chen ◽  
...  
Keyword(s):  
Tumor Detection ◽  
Detection Algorithm ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Multi Scale ◽  
Blob Detection ◽  
Computer Aided ◽  
Automated Breast Ultrasound

scholarly journals Classification of Breast Masses Using a Computer-Aided Diagnosis Scheme of Contrast Enhanced Digital Mammograms

2018 ◽  
Vol 46 (9) ◽  
pp. 1419-1431 ◽  
Author(s):  
Gopichandh Danala ◽  
Bhavika Patel ◽  
Faranak Aghaei ◽  
Morteza Heidari ◽  
Jing Li ◽  
...  
Keyword(s):  
Computer Aided Diagnosis ◽  
Breast Masses ◽  
Contrast Enhanced ◽  
Computer Aided ◽  
Aided Diagnosis

Computer-aided diagnosis system for breast ultrasound images using deep learning

2019 ◽  
Vol 64 (23) ◽  
pp. 235013 ◽  
Author(s):  
Hiroki Tanaka ◽  
Shih-Wei Chiu ◽  
Takanori Watanabe ◽  
Setsuko Kaoku ◽  
Takuhiro Yamaguchi
Keyword(s):  
Deep Learning ◽  
Computer Aided Diagnosis ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Said Boumaraf ◽  
Xiabi Liu ◽  
Chokri Ferkous ◽  
Xiaohong Ma
Keyword(s):  
Feature Selection ◽  
Predictive Value ◽  
Breast Imaging ◽  
Feature Selection Method ◽  
Computer Aided Diagnosis ◽  
Screening Mammography ◽  
Breast Masses ◽  
Computer Aided ◽  
Aided Diagnosis

Mammography remains the most prevalent imaging tool for early breast cancer screening. The language used to describe abnormalities in mammographic reports is based on the Breast Imaging Reporting and Data System (BI-RADS). Assigning a correct BI-RADS category to each examined mammogram is a strenuous and challenging task for even experts. This paper proposes a new and effective computer-aided diagnosis (CAD) system to classify mammographic masses into four assessment categories in BI-RADS. The mass regions are first enhanced by means of histogram equalization and then semiautomatically segmented based on the region growing technique. A total of 130 handcrafted BI-RADS features are then extracted from the shape, margin, and density of each mass, together with the mass size and the patient’s age, as mentioned in BI-RADS mammography. Then, a modified feature selection method based on the genetic algorithm (GA) is proposed to select the most clinically significant BI-RADS features. Finally, a back-propagation neural network (BPN) is employed for classification, and its accuracy is used as the fitness in GA. A set of 500 mammogram images from the digital database for screening mammography (DDSM) is used for evaluation. Our system achieves classification accuracy, positive predictive value, negative predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%, and 79.3%, respectively. To our best knowledge, this is the best current result for BI-RADS classification of breast masses in mammography, which makes the proposed system promising to support radiologists for deciding proper patient management based on the automatically assigned BI-RADS categories.

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