Wavelet-Based Compression and Segmentation of Hyperspectral Images in Surgery

  • Authors:

  • Hamed Akbari;Yukio Kosugi;Kazuyuki Kojima;Naofumi Tanaka

  • Affiliations:

  • Tokyo Institute of Technology, Yokohama, Japan 226-8502;Tokyo Institute of Technology, Yokohama, Japan 226-8502;Tokyo Medical and Dental University, Tokyo, Japan;Tokyo Medical and Dental University, Tokyo, Japan

  • Venue:
  • MIAR '08 Proceedings of the 4th international workshop on Medical Imaging and Augmented Reality
  • Year:
  • 2008

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Abstract

Considering the anatomical variations and unpredictable nature of surgeries, visibility during surgery is very important especially to correctly diagnose problems. Hyperspectral imaging has developed as a compact imaging and spectroscopic tool that can be used for different applications including medical diagnostics. This paper presents the application of hyperspectral imaging as a visual supporting tool to detect different organs and tissues during surgeries. It will be useful for finding ectopic tissues and diagnosis of tissue abnormalities. The high-dimensional data were compressed using wavelet transform and classified using artificial neural networks. The performance of this method is evaluated for the detection of the spleen, colon, small intestine, urinary bladder, and peritoneum in a surgery on a pig.