Mr. Jiang Meng Profile

Jiang Meng

at PLA Air Force Military Medical Univ.

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Proceedings Article | 13 March 2019 Paper

The detection of non-polypoid colorectal lesions using the texture feature extracted from intact colon wall: a pilot study

Hainan Sang, Jiang Meng, Yang Liu, Zhengrong Liang, Hongbing Lu

Proceedings Volume 10950, 109502X (2019) https://doi.org/10.1117/12.2511823

KEYWORDS: Colon, Feature extraction, Computer aided diagnosis and therapy, Image segmentation, Computed tomography, Tissues, Matrices, Colorectal cancer, Target detection, Curium

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The detection of non-polypoid colorectal lesions (e.g., the flat and small sessile polyps) is still a challenging task for the computer-aided detection (CADe) method. Different from previous CADe method, we proposed a new scheme to detect the lesion using the texture feature extracted from intact colon wall, since texture feature is sensitivity to detect subtle lesion and all the available information of the lesion imbeds in the colon wall. In this scheme, the inner and outer wall surface were segmented. Then, for each voxel of inner surface, a fixed size neighborhood was projected onto the colon model and the intersection volume of the projection through the colon model was selected as the volume of interest (VOI). From each VOI, three images were obtained: the original CT intensity and its gradient and curvature maps, Gray-scale co-occurrence matrices (CMs) were calculated from these 3 volumetric images, respectively. A total of 196 texture features (60 Haralick features and 6 CT histogram features extracted from each CM) were used to detect initial polyp candidates by a piecewise anomaly detection method of isolation forest, followed by a supervised classification (random Forests) for false positive (FP) reduction. The detection performance was evaluated by a 10-fold cross-validation and free-response receiver operating characteristics analysis. We evaluated our method via 10 patients with 36 confirmed flat and small sessile polyps were collected, including 16 flat, 18 sessile, and 2 pedunculated polyps. The presented detection method achieved 80% sensitivity with 9.98 FPs per dataset. The experiment results demonstrate that our method is a potential way to detect nonpolypoid polyps, particularly flat and depressed ones.

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