TOWARDS APPLYING HYPERSPECTRAL IMAGERY AS AN INTRAOPERATIVE VISUAL AID TOOL

During a surgery, the inevitable presence of blood covering the surgical field demands efforts to keep the area as clean as possible. A new hyperspectral data processing method is being developed to deliver clearer images to the surgeon. The analysis of optical absorption properties of the blood and water indicates that, between the visible and near infrared spectral regions, some valuable information under the blood layer may be obtained using a spectral imaging system. We propose a neural network approach to provide a nonlinear combination of spectral band reflectance in order to reveal images that could not be seeing with unprocessed images. This paper describes the implementation of single-layer and multi-layer perceptron architectures to perform the hyperspectral data processing. We present experimental results attesting the viability of the proposed method. We demonstrate that hyperspectral imagery can be exploited as visual aid for surgical guidance.

[1]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[2]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[3]  Thomas G. Dietterich What is machine learning? , 2020, Archives of Disease in Childhood.

[4]  K Danzer,et al.  Non-Invasive Blood Glucose Monitoring by Means of near Infrared Spectroscopy: Methods for Improving the Reliability of the Calibration Models , 1997, The International journal of artificial organs.

[5]  Erzsébet Merényi,et al.  The challenges in spectral image analysis: an introduction, and review of ANN approaches , 1999, ESANN.

[6]  John A. Richards,et al.  Remote Sensing Digital Image Analysis: An Introduction , 1999 .

[7]  Masahiro YAMAGUCHI Medical Application of a Color Reproduction System with a Multispectral Camera , 2001 .

[8]  J. Barton,et al.  Cooperative Phenomena in Two-pulse, Two-color Laser Photocoagulation of Cutaneous Blood Vessels¶ , 2001, Photochemistry and photobiology.

[9]  Anita Mahadevan-Jansen,et al.  Intraoperative application of optical spectroscopy in the presence of blood , 2001 .

[10]  Michael Egmont-Petersen,et al.  Image processing with neural networks - a review , 2002, Pattern Recognit..

[11]  G. Shaw,et al.  Signal processing for hyperspectral image exploitation , 2002, IEEE Signal Process. Mag..

[12]  Pramod K. Varshney,et al.  Target detection in hyperspectral images based on independent component analysis , 2002, SPIE Defense + Commercial Sensing.

[13]  R. Pepperkok,et al.  Spectral imaging and its applications in live cell microscopy , 2003, FEBS letters.

[14]  A. Vogel,et al.  Mechanisms of pulsed laser ablation of biological tissues. , 2003, Chemical reviews.

[15]  R. Solberg,et al.  A constrained spectral unmixing approach to snow-cover mapping in forests using MODIS data , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[16]  H. H. Bennett,et al.  Classification of Hyperspectral Data: A Comparative Study , 2004, Precision Agriculture.

[17]  Richard M. Levenson,et al.  Spectral Imaging and Pathology: Seeing More , 2004 .