Hyperspectral imaging and quantitative analysis for prostate cancer detection

Hamed Akbari, Luma V. Halig, David M. Schuster, Adeboye Osunkoya, Viraj Master, Peter T. Nieh, Georgia Z. Chen, Baowei Fei

Research output: Contribution to journalArticlepeer-review

188 Scopus citations


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.

Original languageEnglish (US)
Article number076005
JournalJournal of biomedical optics
Issue number7
StatePublished - Jul 2012
Externally publishedYes


  • Hyperspectral imaging
  • Image classification
  • Least squares support vector machine
  • Optical diagnosis
  • Prostate cancer

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Biomedical Engineering


Dive into the research topics of 'Hyperspectral imaging and quantitative analysis for prostate cancer detection'. Together they form a unique fingerprint.

Cite this