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IDentifying cancer regions in vital-stained magnification endoscopy images using adapted color histograms

IDentifying cancer regions in vital-stained magnification endoscopy images using adapted color histograms,10.1109/ICIP.2009.5414082,André Sousa,Mário

IDentifying cancer regions in vital-stained magnification endoscopy images using adapted color histograms   (Citations: 2)
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In-body imaging technologies such as vital-stained magnification endoscopy pose novel image processing challenges to computer-assisted decision systems given their unique visual characteristics such as reduced color spaces and natural textures. In this paper we will show the potential of using adapted color features combined with local binary patterns, a texture descriptor that has exhibited good adaptation to natural images, for classifying gastric regions into three groups: normal, pre-cancer and cancer lesions. Results exhibit 91% accuracy, confirming that specific research for in-body imaging could be the key for future computer assisted decision systems for medicine.
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    • ...Experiments show that for CH images, LBP perform better as compared with other feature extraction methods however the difference between LBP and FG is not much (Fig. 3). Previous literature on classification of CH images [10] shows a reasonable increase in classification rates of LBP when complemented with color features, thus hinting at their importance for classification of CH images...

    F. Riazet al. Gabor textons for classification of gastroenterology images

    • ...Previous literature on gastrointestinal image analysis confirms both the importance and the difficulty of the segmentation stage of these algorithms, since authors either assume perfect segmentations [1], avoid segmentation by using full images [2], or resort to grid-based segmentation for obtaining image sections [3]...
    • ...We have previously studied cancer classification potential for CAD systems using traditional color and texture descriptors [1], and have inspected segmentation performance of classic algorithms (mean shift, normalized cuts) as a good approximation to the area and shape of the manual annotation provided by clinicians [4]...
    • ...Given that this pattern recognition system layer (Fig.1) is not being considered as a variable for this study, we will use the same methodology of our previous work where perfect segmentation is assumed for a chromoendoscopy scenario [1]...
    • ...Although our proposed research question does not concern absolute classification accuracy, it is still worth observing them in Table I. Results show interesting classification rates for CH, although inferior to the ones using adapted color histograms [1]...

    M. Coimbraet al. Segmentation for classification of gastroenterology images

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