%0 Journal Article
%T Closed Contour Specular Reflection Segmentation in Laparoscopic Images
%A Jan Marek Marcinczak
%A Rolf-Rainer Grigat
%J International Journal of Biomedical Imaging
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/593183
%X 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. 1. Introduction One major concern in laparoscopic image processing is specular reflections which are present in the majority of laparoscopic interventions and affect all following processing. Specular reflections are most pronounced if the surface normal bisects the angle between the incident light and the camera. They are caused by moist tissue and appear as white glare or light-colored glare in the images. Many different approaches to segment specular reflections have been proposed in the previous decades. Most of them are based on the dichromatic reflection model [1, 2]. Let be the incident angle, the exitance angle, the phase angle, and the wavelength. The reflectance and model the surface reflection and the body reflection. The radiance reflected by a surface can be defined as The dichromatic reflection model holds for dielectric surfaces and separates the spectral reflection from the geometric reflection [3¨C5]: where and are geometric scaling factors and and are spectral power distributions. The body reflection (diffuse reflection) and the specular reflection form linear clusters in a color histogram [6]; fitting linear subspaces to these clusters can be used to detect specular reflections in images, and the diffuse color can be reconstructed by projection. However, in practice, surface roughness and the imaging geometry make the fitting of subspaces inaccurate [7]. Additionally, the assumption of dielectric surfaces is not fulfilled by human tissue. Nevertheless, several algorithms have been proposed that use the dichromatic model in an endoscopic environment [8, 9]. However, Vogt et al. show that simple S channel thresholding in the HSV color space achieves similar accuracy on endoscopic images [10]. Several adaptive
%U http://www.hindawi.com/journals/ijbi/2013/593183/