Application of Color Change Feature in Gastroscopic Image Retrieval

Abstract

Content-based medical image retrieval is getting more and more importance in aspect of clinical assistant diagnose. In this paper a new method based on the characters of color change is proposed. First a color clustering technique is used for image segmentation in CIE L*a*b* color space. And then color change feature is extracted from the binary edge image. Kullback-Leibler distance is used to calculate the dissimilarity. Meanwhile a method combining both color change feature and dominant color information is proposed to carry out integrate retrieval. Finally, a system for gastroscopic image retrieval is developed which is available to support clinical decision making. Some contrast experiments are designed in the retrieval accuracies the rank and the execution time. The comparison of the experimental results shows that the approach proposed in this paper is effective.

Content-based processing and analysis of endoscopic images and videos: A survey

“…An early approach [272] uses simple color histograms in HSV color space to determine the similarity between colonoscopy images. A more recent approach [270] for gastroscopic images uses image segmentation in the CIE L*a*b* color space to extract a "color change feature" and dominant color information and compares images using the Kullback-Leibler distance. Tai et al [233] also incorporate texture information by using a color-texture correlogram and the Generalized Tersky Index as similarity measure.…”