By a News Reporter-Staff News Editor at Health & Medicine Week -- Investigators discuss new findings in Support Vector Machines. According to news reporting from Salt Lake City, Utah, by NewsRx journalists, research stated, "The goal of the study is to develop a technique to achieve accurate volumetric breast tissue segmentation using magnetic resonance imaging (MRI) data. This segmentation can be useful to aid in the diagnosis of breast cancers and to assess breast cancer risk based on breast density."
The news correspondents obtained a quote from the research from the University of Utah, "Tissue segmentation is also essential for development of acoustic and thermal models used in magnetic resonance guided high-intensity focused ultrasound treatment of breast lesions. In addition to commonly used T1-, T2-, and proton density-weighted images, three-point Dixon water-and fat-only images were also included as part of the multiparametric inputs to a tissue segmentation algorithm using a hierarchical support vector machine (SVM). The effectiveness of a variety of preprocessing schemes was evaluated through two in vivo datasets. The performance of the hierarchical SVM was investigated and compared to the conventional classification algorithms-conventional SVM and fuzzy C-mean (FCM). The need for co-registration, zero-filled interpolation, coil sensitivity correction, and optimal SNR reconstruction before the final stage classification was demonstrated. The overlap ratios of the hierarchical SVM, conventional SVM and FCM were 93.25%-94.08%, 81.68-92.28%, and 75.96%-91.02%, respectively. Classification outputs from in vivo experiments showed that the presented methodology is consistent and outperforms other algorithms. The presented hierarchical SVM-based technique showed promising results in automatically segmenting breast tissues into fat, fibroglandular tissue, skin, and lesions."
According to the news reporters, the research concluded: "The results provide evidence that both the multiparametric breast MRI inputs and the preprocessing procedures contribute to the high accuracy of tissue classification."
For more information on this research see: 3D multi-parametric breast MRI segmentation using hierarchical support vector machine with coil sensitivity correction. Academic Radiology, 2013;20(2):137-47. (Elsevier - www.elsevier.com; Academic Radiology - www.elsevier.com/wps/product/cws_home/672918)
Our news journalists report that additional information may be obtained by contacting Y. Wang, Dept. of Bioengineering, Utah Center for Advanced Imaging Research, University of Utah, 729 Arapeen Drive, Salt Lake City, UT 84108, United States. Additional authors for this research include G. Morrell, M.E. Heibrun, A. Payne and D.L Parker (see also Support Vector Machines).
Keywords for this news article include: Utah, Algorithms, United States, Salt Lake City, Machine Learning, Emerging Technologies, Support Vector Machines, North and Central America.
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