Wavelet-based compression and segmentation of hyperspectral images in surgery

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. © 2008 Springer-Verlag Berlin Heidelberg.