Paper
27 March 2009 A comparative study in ultrasound breast imaging classification
Author Affiliations +
Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 72591S (2009) https://doi.org/10.1117/12.811208
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
American College of Radiology introduces a standard in classification, the breast imaging reporting and data system (BIRADS), standardize the reporting of ultrasound findings, clarify its interpretation, and facilitate communication between clinicians. The effective use of new technologies to support healthcare initiatives is important and current research is moving towards implementing computer tools in the diagnostics process. Initially a detailed study was carried out to evaluate the performance of two commonly used appearance based classification algorithms, based on the use of Principal Component Analysis (PCA), and two dimensional linear discriminant analysis (2D-LDA). The study showed that these two appearance based classification approaches are not capable of handling the classification of ultrasound breast image lesions. Therefore further investigations in the use of a popular feature based classifier - Support Vector Machine (SVM) was conducted. A pre-processing step before feature based classification is feature extraction, which involve shape, texture and edge descriptors for the Region of Interest (ROI). The input dataset to SVM classification is from a fully automated ROI detection. We achieve the success rate of 0.550 in PCA, 0.500 in LDA, and 0.931 in SVM. The best combination of features in SVM classification is to combine the shape, texture and edge descriptors, with sensitivity 0.840 and specificity 0.968. This paper briefly reviews the background to the project and then details the ongoing research. In conclusion, we discuss the contributions, limitations, and future plans of our work.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Moi Hoon Yap, Eran A. Edirisinghe, and Helmut E. Bez "A comparative study in ultrasound breast imaging classification", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72591S (27 March 2009); https://doi.org/10.1117/12.811208
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Cited by 11 scholarly publications.
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KEYWORDS
Tumors

Image classification

Principal component analysis

Ultrasonography

Breast imaging

Feature extraction

Shape analysis

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