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1 July 2008 Automated breast cancer classification using near-infrared optical tomographic images
James Z. Wang, Xiaoping Liang, Qizhi Zhang, Laurie L. Fajardo, Huabei Jiang
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Journal of Biomedical Optics, Vol. 13, Issue 4, 044001 (July 2008). https://doi.org/10.1117/1.2956662
Abstract
An automated procedure for detecting breast cancer using near-infrared (NIR) tomographic images is presented. This classification procedure automatically extracts attributes from three imaging parameters obtained by an NIR imaging system. These parameters include tissue absorption and reduced scattering coefficients, as well as a tissue refractive index obtained by a phase-contrast-based reconstruction approach. A support vector machine (SVM) classifier is utilized to distinguish the malignant from the benign lesions using the automatically extracted attributes. The classification results of in vivo tomographic images from 35 breast masses using absorption, scattering, and refractive index attributes demonstrate high sensitivity, specificity, and overall accuracy of 81.8%, 91.7%, and 88.6% respectively, while the classification sensitivity, specificity, and overall accuracy are 63.6%, 83.3%, and 77.1%, respectively, when only the absorption and scattering attributes are used. Furthermore, the automated classification procedure provides significantly improved specificity and overall accuracy for breast cancer detection compared to those by an experienced technician through visual examination.
©(2008) Society of Photo-Optical Instrumentation Engineers (SPIE)
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James Z. Wang, Xiaoping Liang, Qizhi Zhang, Laurie L. Fajardo, and Huabei Jiang "Automated breast cancer classification using near-infrared optical tomographic images," Journal of Biomedical Optics 13(4), 044001 (1 July 2008). https://doi.org/10.1117/1.2956662
Published: 1 July 2008
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Cited by 14 scholarly publications.
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KEYWORDS
Scattering
Absorption
Refractive index
Image classification
Image segmentation
Visualization
Breast cancer
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