A Tumor Detection Algorithm for Whole Breast Ultrasound Images Incorporating Breast Anatomy Information

2014 ◽  
Author(s):  
Chia-Yun Hsu ◽  
Yi-Hong Chou ◽  
Chung-Ming Chen
Keyword(s):  
Tumor Detection ◽  
Detection Algorithm ◽  
Breast Ultrasound ◽  
Ultrasound Images

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

Tumor detection in automated breast ultrasound images using quantitative tissue clustering

2014 ◽  
Vol 41 (4) ◽  
pp. 042901 ◽  
Author(s):  
Woo Kyung Moon ◽  
Chung-Ming Lo ◽  
Rong-Tai Chen ◽  
Yi-Wei Shen ◽  
Jung Min Chang ◽  
...  
Keyword(s):  
Tumor Detection ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Automated Breast Ultrasound

Tumor detection from breast ultrasound images using mammary gland attentive U-Net

2021 ◽  
Author(s):  
Kaiwen Yang ◽  
Aiga Suzuki ◽  
Jiaxing Ye ◽  
Hirokazu Nosato ◽  
Ayumi Izumori ◽  
...  
Keyword(s):  
Mammary Gland ◽  
Tumor Detection ◽  
Breast Ultrasound ◽  
Ultrasound Images

Nipple Localization in Automated Whole Breast Ultrasound Coronal Scans Using Ensemble Learning

2020 ◽  
Vol 43 (1) ◽  
pp. 29-45
Author(s):  
Alex Noel Joseph Raj ◽  
Ruban Nersisson ◽  
Vijayalakshmi G. V. Mahesh ◽  
Zhemin Zhuang
Keyword(s):  
Texture Features ◽  
Breast Ultrasound ◽  
Support Vector ◽  
Ultrasound Images ◽  
Shadow Area ◽  
Hu Moments ◽  
Artificial Neural Network Ann ◽  
Cade System ◽  
Occurrence Matrix ◽  
Shape Complexity

Nipple is a vital landmark in the breast lesion diagnosis. Although there are advanced computer-aided detection (CADe) systems for nipple detection in breast mediolateral oblique (MLO) views of mammogram images, few academic works address the coronal views of breast ultrasound (BUS) images. This paper addresses a novel CADe system to locate the Nipple Shadow Area (NSA) in ultrasound images. Here the Hu Moments and Gray-level Co-occurrence Matrix (GLCM) were calculated through an iterative sliding window for the extraction of shape and texture features. These features are then concatenated and fed into an Artificial Neural Network (ANN) to obtain probable NSA’s. Later, contour features, such as shape complexity through fractal dimension, edge distance from the periphery and contour area, were computed and passed into a Support Vector Machine (SVM) to identify the accurate NSA in each case. The coronal plane BUS dataset is built upon our own, which consists of 64 images from 13 patients. The test results show that the proposed CADe system achieves 91.99% accuracy, 97.55% specificity, 82.46% sensitivity and 88% F-score on our dataset.

Computer-Aided Multiview Tumor Detection for Automated Whole Breast Ultrasound

2013 ◽  
Vol 36 (1) ◽  
pp. 3-17 ◽  
Author(s):  
Chiao Lo ◽  
Yi-Wei Shen ◽  
Chiun-Sheng Huang ◽  
Ruey-Feng Chang
Keyword(s):  
Tumor Detection ◽  
Breast Ultrasound ◽  
Computer Aided

Automatic superpixel-based segmentation method for breast ultrasound images

2019 ◽  
Vol 121 ◽  
pp. 78-96 ◽  
Author(s):  
Mohammad I. Daoud ◽  
Ayman A. Atallah ◽  
Falah Awwad ◽  
Mahasen Al-Najjar ◽  
Rami Alazrai
Keyword(s):  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Segmentation Method
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