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6 July 2012 Hyperspectral imaging and quantitative analysis for prostate cancer detection
Hamed Akbari, Luma Halig, David M. Schuster, Baowei Fei, Adeboye Osunkoya, Viraj Master, Peter Nieh, Georgia Chen
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Journal of Biomedical Optics, Vol. 17, Issue 7, 076005 (July 2012). https://doi.org/10.1117/1.JBO.17.7.076005
Abstract
Hyperspectral imaging (HSI) is an emerging modality for various medical applications. Its spectroscopic data might be able to be used to noninvasively detect cancer. Quantitative analysis is often necessary in order to differentiate healthy from diseased tissue. We propose the use of an advanced image processing and classification method in order to analyze hyperspectral image data for prostate cancer detection. The spectral signatures were extracted and evaluated in both cancerous and normal tissue. Least squares support vector machines were developed and evaluated for classifying hyperspectral data in order to enhance the detection of cancer tissue. This method was used to detect prostate cancer in tumor-bearing mice and on pathology slides. Spatially resolved images were created to highlight the differences of the reflectance properties of cancer versus those of normal tissue. Preliminary results with 11 mice showed that the sensitivity and specificity of the hyperspectral image classification method are 92.8% to 2.0% and 96.9% to 1.3%, respectively. Therefore, this imaging method may be able to help physicians to dissect malignant regions with a safe margin and to evaluate the tumor bed after resection. This pilot study may lead to advances in the optical diagnosis of prostate cancer using HSI technology.
© 2012 Society of Photo-Optical Instrumentation Engineers (SPIE) 0091-3286/2012/$25.00 © 2012 SPIE
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Hamed Akbari, Luma Halig, David M. Schuster, Baowei Fei, Adeboye Osunkoya, Viraj Master, Peter Nieh, and Georgia Chen "Hyperspectral imaging and quantitative analysis for prostate cancer detection," Journal of Biomedical Optics 17(7), 076005 (6 July 2012). https://doi.org/10.1117/1.JBO.17.7.076005
Published: 6 July 2012
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Cited by 214 scholarly publications and 6 patents.
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KEYWORDS
Tissues
Hyperspectral imaging
Cancer
Tumors
Prostate cancer
Pathology
Image classification
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