Image retrieval of breast masses on ultrasound images

2019 ◽  
Author(s):  
Chisako Muramatsu ◽  
Shunichi Higuchi ◽  
Takako Morita ◽  
Mikinao Oiwa ◽  
Tomonori Kawasaki ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Ultrasound Images ◽  
Breast Masses

scholarly journals Similarity evaluation in a content-based image retrieval (CBIR) CADx system for characterization of breast masses on ultrasound images

2011 ◽  
Vol 38 (4) ◽  
pp. 1820-1831 ◽  
Author(s):  
Hyun-chong Cho ◽  
Lubomir Hadjiiski ◽  
Berkman Sahiner ◽  
Heang-Ping Chan ◽  
Mark Helvie ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Ultrasound Images ◽  
Breast Masses

scholarly journals Color Doppler Ultrasound Improves Machine Learning Diagnosis of Breast Cancer

Diagnostics ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 631
Author(s):  
Afaf F. Moustafa ◽  
Theodore W. Cary ◽  
Laith R. Sultan ◽  
Susan M. Schultz ◽  
Emily F. Conant ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Breast Imaging ◽  
Color Doppler ◽  
Color Doppler Ultrasound ◽  
Ensemble Classifier ◽  
Diagnostic Model ◽  
Ultrasound Images ◽  
Breast Masses ◽  
Roc Area

Color Doppler is used in the clinic for visually assessing the vascularity of breast masses on ultrasound, to aid in determining the likelihood of malignancy. In this study, quantitative color Doppler radiomics features were algorithmically extracted from breast sonograms for machine learning, producing a diagnostic model for breast cancer with higher performance than models based on grayscale and clinical category from the Breast Imaging Reporting and Data System for ultrasound (BI-RADSUS). Ultrasound images of 159 solid masses were analyzed. Algorithms extracted nine grayscale features and two color Doppler features. These features, along with patient age and BI-RADSUS category, were used to train an AdaBoost ensemble classifier. Though training on computer-extracted grayscale features and color Doppler features each significantly increased performance over that of models trained on clinical features, as measured by the area under the receiver operating characteristic (ROC) curve, training on both color Doppler and grayscale further increased the ROC area, from 0.925 ± 0.022 to 0.958 ± 0.013. Pruning low-confidence cases at 20% improved this to 0.986 ± 0.007 with 100% sensitivity, whereas 64% of the cases had to be pruned to reach this performance without color Doppler. Fewer borderline diagnoses and higher ROC performance were both achieved for diagnostic models of breast cancer on ultrasound by machine learning on color Doppler features.

scholarly journals Automated Detection Algorithm of Breast Masses in Three-Dimensional Ultrasound Images

2016 ◽  
Vol 22 (4) ◽  
pp. 293 ◽  
Author(s):  
Ji-Wook Jeong ◽  
Donghoon Yu ◽  
Sooyeul Lee ◽  
Jung Min Chang
Keyword(s):  
Three Dimensional ◽  
Detection Algorithm ◽  
Automated Detection ◽  
Ultrasound Images ◽  
Breast Masses

scholarly journals The diagnostic performance of leak-plugging automated segmentation versus manual tracing of breast lesions on ultrasound images

Ultrasound ◽  
2017 ◽  
Vol 25 (2) ◽  
pp. 98-106 ◽  
Author(s):  
Hui Xiong ◽  
Laith R Sultan ◽  
Theodore W Cary ◽  
Susan M Schultz ◽  
Ghizlane Bouzghar ◽  
...  
Keyword(s):  
Diagnostic Performance ◽  
Roc Curves ◽  
Lesion Size ◽  
Ultrasound Images ◽  
Segmentation Method ◽  
Breast Masses ◽  
Segmentation Methods ◽  
Malignant Lesions ◽  
Overlap Area ◽  
Better Than

Purpose To assess the diagnostic performance of a leak-plugging segmentation method that we have developed for delineating breast masses on ultrasound images. Materials and methods Fifty-two biopsy-proven breast lesion images were analyzed by three observers using the leak-plugging and manual segmentation methods. From each segmentation method, grayscale and morphological features were extracted and classified as malignant or benign by logistic regression analysis. The performance of leak-plugging and manual segmentations was compared by: size of the lesion, overlap area ( Oa) between the margins, and area under the ROC curves ( Az). Results The lesion size from leak-plugging segmentation correlated closely with that from manual tracing ( R2 of 0.91). Oa was higher for leak plugging, 0.92 ± 0.01 and 0.86 ± 0.06 for benign and malignant masses, respectively, compared to 0.80 ± 0.04 and 0.73 ± 0.02 for manual tracings. Overall Oa between leak-plugging and manual segmentations was 0.79 ± 0.14 for benign and 0.73 ± 0.14 for malignant lesions. Az for leak plugging was consistently higher (0.910 ± 0.003) compared to 0.888 ± 0.012 for manual tracings. The coefficient of variation of Az between three observers was 0.29% for leak plugging compared to 1.3% for manual tracings. Conclusion The diagnostic performance, size measurements, and observer variability for automated leak-plugging segmentations were either comparable to or better than those of manual tracings.

Computerized characterization of breast masses using three-dimensional ultrasound images

1998 ◽  
Author(s):  
Berkman Sahiner ◽  
Gerald L. LeCarpentier ◽  
Heang-Ping Chan ◽  
Marilyn A. Roubidoux ◽  
Nicholas Petrick ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Ultrasound Images ◽  
Breast Masses
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