Hue-texture-embedded region-based model for magnifying endoscopy with narrow-band imaging image segmentation based on visual features

Magnification endoscopy with narrow-band imaging (ME-NBI) has become a feasible tool for detecting diseases within the human gastrointestinal tract, and is more applied by physicians to search for pathological abnormalities with gastric cancer such as precancerous lesions, early gastric cancer and advanced cancer. In order to improve the reliability of diseases detection, there is a need for applying or proposing computer-assisted methodologies to efficiently analyze and process ME-NBI images. However, traditional computer vision methodologies, mainly segmentation, do not express well to the specific visual characteristics of NBI scenario.

In this paper, two energy functional items based on specific visual characteristics of ME-NBI images have been integrated in the framework of Chan-Vese model to construct the Hue-texture-embedded model. On the one hand, a global hue energy functional was proposed representing a global color information extracted in H channel (HSI color space). On the other hand, a texture energy was put forward presenting local microvascular textures extracted by the PIF of adaptive threshold in S channel.

The results of our model have been compared with Chan-Vese model and manual annotations marked by physicians using F-measure and false positive rate. The value of average F-measure and FPR was 0.61 and 0.16 achieved through the Hue-texture-embedded region-based model. And the C-V model achieved the average F-measure and FPR value of 0.52 and 0.32, respectively. Experiments showed that the Hue-texture-embedded region-based outperforms Chan-Vese model in terms of efficiency, universality and lesion detection.

Better segmentation results are acquired by the Hue-texture-embedded region-based model compared with the traditional region-based active contour in these five cases: chronic gastritis, intestinal metaplasia and atrophy, low grade neoplasia, high grade neoplasia and early gastric cancer. In the future, we are planning to expand the universality of our proposed methodology to segment other lesions such as intramucosal cancer etc. As long as these issues are solved, we can proceed with the classification of clinically relevant diseases in ME-NBI images to implement a fully automatic computer-assisted diagnosis system.