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Closed Contour Specular Reflection Segmentation in Laparoscopic Images
Author(s) -
Jan Marek Marcinczak,
RolfRainer Grigat
Publication year - 2013
Publication title -
international journal of biomedical imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.626
H-Index - 41
eISSN - 1687-4196
pISSN - 1687-4188
DOI - 10.1155/2013/593183
Subject(s) - thresholding , specular highlight , specular reflection , artificial intelligence , computer science , segmentation , computer vision , image segmentation , reflection (computer programming) , region growing , pattern recognition (psychology) , image (mathematics) , scale space segmentation , optics , physics , programming language
Segmentation of specular reflections is an essential step in endoscopic image analysis; it affects all further processing steps including segmentation, classification, and registration tasks. The dichromatic reflectance model, which is often used for specular reflection modeling, is made for dielectric materials and not for human tissue. Hence, most recent segmentation approaches rely on thresholding techniques. In this work, we first demonstrate the limited accuracy that can be achieved by thresholding techniques and propose a hybrid method which is based on closed contours and thresholding. The method has been evaluated on 269 specular reflections in 49 images which were taken from 27 real laparoscopic interventions. Our method improves the average sensitivity by 16% compared to the state-of-the-art thresholding methods.

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