Design and implementation of a colour vision model for computer vision applications
Computer Vision, Graphics, and Image Processing
Advances in statistical pattern recognition
Proc. of the NATO Advanced Study Institute on Pattern recognition theory and applications
Optimisation algorithms in probabilistic relaxation labelling
Proc. of the NATO Advanced Study Institute on Pattern recognition theory and applications
Color spaces for computer graphics
SIGGRAPH '78 Proceedings of the 5th annual conference on Computer graphics and interactive techniques
Studies in Global and Local Histogram-Guided Relaxation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Foundations of Relaxation Labeling Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color Segmentation Applied to Study of the Angiogenesis. Part I
Journal of Intelligent and Robotic Systems
A Strategy for Atherosclerosis Image Segmentation by Using Robust Markers
Journal of Intelligent and Robotic Systems
A comparison between two robust techniques for segmentation of blood vessels
Computers in Biology and Medicine
Computers in Biology and Medicine
A Segmentation Algorithm Based on an Iterative Computation of the Mean Shift Filtering
Journal of Intelligent and Robotic Systems
An image segmentation algorithm using iteratively the mean shift
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
A strategy for atherosclerotic lesions segmentation
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
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In this paper, a new color image segmentation method is proposed to extract the region of bladder tumor from a color bladder image. In the past, the diagnosis of bladder tumors mainly relies upon cystoscopic examination with an in vivo staining technique. This manner heavily depends on the interpreter's experience and often results in errors in diagnosis. Instead of previous method, we use methylene blue in vivo staining combined with color segmentation techniques to improve the accuracy of the diagnosis of bladder tumors. The segmentation task can be decomposed into two stages. First, cluster detection combined with probabilistic relaxation is used to extract the clusters of specified colors from the HLS color space. Then, in order to refine the chromatic properties, the Bayesian algorithm is employed to reject the false region from the clusters obtained in the first stage. Experimental results show that the proposed method can segment the bladder tumor successfully. The technique could serve as an auxiliary tool for doctors or researchers in the diagnosis of bladder tumors.