28 February 2013 Quantitative evaluation of automatic methods for lesions detection in breast ultrasound images
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Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 867027 (2013) https://doi.org/10.1117/12.2008056
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
Ultrasound (US) is a useful diagnostic tool to distinguish benign from malignant breast masses, providing more detailed evaluation in dense breasts. Due to the subjectivity in the images interpretation, computer-aid diagnosis (CAD) schemes have been developed, increasing the mammography analysis process to include ultrasound images as complementary exams. As one of most important task in the evaluation of this kind of images is the mass detection and its contours interpretation, automated segmentation techniques have been investigated in order to determine a quite suitable procedure to perform such an analysis. Thus, the main goal in this work is investigating the effect of some processing techniques used to provide information on the determination of suspicious breast lesions as well as their accurate boundaries in ultrasound images. In tests, 80 phantom and 50 clinical ultrasound images were preprocessed, and 5 segmentation techniques were tested. By using quantitative evaluation metrics the results were compared to a reference image delineated by an experienced radiologist. A self-organizing map artificial neural network has provided the most relevant results, demonstrating high accuracy and low error rate in the lesions representation, corresponding hence to the segmentation process for US images in our CAD scheme under tests.
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Karem D. Marcomini, Karem D. Marcomini, Homero Schiabel, Homero Schiabel, Antonio Adilton O. Carneiro, Antonio Adilton O. Carneiro, "Quantitative evaluation of automatic methods for lesions detection in breast ultrasound images", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 867027 (28 February 2013); doi: 10.1117/12.2008056; https://doi.org/10.1117/12.2008056
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