December 2002
Volume 43, Issue 13
ARVO Annual Meeting Abstract  |   December 2002
Discrimination of Noisy Tissue Structures in Surgery
Author Affiliations & Notes
  • D Ebrahimi
    Dept of Surgery University of Toronto Toronto ON Canada
  • SJ Hamstra
    Dept of Surgery University of Toronto Toronto ON Canada
  • Footnotes
    Commercial Relationships   D. Ebrahimi, None; S.J. Hamstra, None. Grant Identification: NSERC
Investigative Ophthalmology & Visual Science December 2002, Vol.43, 4794. doi:
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      D Ebrahimi, SJ Hamstra; Discrimination of Noisy Tissue Structures in Surgery . Invest. Ophthalmol. Vis. Sci. 2002;43(13):4794.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract: : Little is known about the statistics of images in surgery, or the feasibility of applying methods of signal enhancement to assist surgeons in the identification of tissue in visually noisy fields. This may be especially important for endoscopic/laparoscopic surgery, as the visual field is constrained. Purpose: To characterize the natural statistics of surgical images and to employ computational image analysis techniques from vision science to solve problems of tissue identification in surgery. Methods: As a first step in investigating the feasibility of using multiresolution (wavelet transform) texture analysis for visual discrimination of surgical tissues, we measured the statistical characteristics of several surgical images and compared them with the known characteristics of natural scenes. Natural images have a simple characteristic spatial structure, with amplitude spectra that decrease with frequency roughly as 1/f, and considerable variability in amplitude spectra between individual images and in image ensembles. Therefore, amplitude is generally thought to be proportional to 1/fα where α has been found to be within a fairly narrow range (0.7 -1.5) for natural scenes. For the purpose of this study, a digital set of 24 parathyroid images was acquired and its spatial frequency content analysed. We used a set of parathyroid images since these represent a difficult visual problem and provided a good set for the development of image enhancement techniques. Results: The value of α for this set of images was found to be 1.59. The same measure for a set of 60 natural images from the Brodatz book of texture gave a value of 1.12. Consequently, we developed examples of image enhancement using a histogram equalization technique. Conclusion: The large value of α for surgical images is barely in the range of natural images and indicates a steep spectrum. This may be expected as the amplitude spectrum of scenes is a representation of correlational structure. Surgical images lack sharp edges and high frequency content, but it is feasible to enhance target structures.

Keywords: 429 image processing • 579 shape, form, contour, object perception • 586 spatial vision 

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