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Author Notes:

Address all correspondence to: Baowei Fei, Emory University, Center for Systems Imaging, Department of Radiology and Imaging Sciences, 1841 Clifton Road NE, Atlanta, GA 30329. Tel: (404) 712-5649; Fax: (404) 712-5689; E-mail: bfei@emory.edu, http://feilab.org.

Subjects:

Research Funding:

This research is supported in part by NIH grant R01CA156775 (PI: Fei), Georgia Cancer Coalition Distinguished Clinicians and Scientists Award (PI: Fei), Emory Molecular and Translational Imaging Center (NIH P50CA128301), SPORE in Head and Neck Cancer (NIH P50CA128613), and the Atlanta Clinical and Translational Science Institute (ACTSI) that is supported by PHS Grant UL1 RR025008 from the Clinical and Translational Science Award program.

Keywords:

  • Science & Technology
  • Life Sciences & Biomedicine
  • Physical Sciences
  • Biochemical Research Methods
  • Optics
  • Radiology, Nuclear Medicine & Medical Imaging
  • Biochemistry & Molecular Biology
  • BIOCHEMICAL RESEARCH METHODS
  • OPTICS
  • RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
  • hyperspectral imaging
  • prostate cancer
  • least squares support vector machine
  • image classification
  • optical diagnosis
  • SUPPORT VECTOR MACHINES
  • MEANS CLASSIFICATION METHOD
  • PHOTODYNAMIC THERAPY
  • RAMAN-SPECTROSCOPY
  • IMAGES
  • SEGMENTATION
  • MULTISCALE
  • DIAGNOSIS
  • SURGERY
  • CELLS

Hyperspectral imaging and quantitative analysis for prostate cancer detection

Tools:

Journal Title:

Journal of Biomedical Optics

Volume:

Volume 17, Number 7

Publisher:

, Pages 076005-076005

Type of Work:

Article | Final Publisher PDF

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.

Copyright information:

© 2012 Society of Photo-Optical Instrumentation Engineers (SPIE).

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