Colorectal cancer (CRC) remains one of the leading causes of cancer deaths today. Since precancerous colorectal polyps slowly progress into cancer, screening methods are highly effective in reducing the overall mortality rate of CRC by removing them before developing into later stages. The two current screening modalities, optical colonoscopy (OC) and virtual tomographic colonography (CTC), are both effective at detecting polyps, but the diagnostic performance from each has lagged behind detection. In this paper, we propose a texture analysis-based approach for integrating the complementary information from these two screening modalities. We use a set of well-established texture features including gray-level co-occurrence matrix features, gray-level run-length matrix features, local binary pattern features, first order histogram features, and more. To maximize the amount of textures extracted to examine the tissue heterogeneities between polyp pathologies, these textures are also computed on the higher order derivative images of the CTC polyp images and on the Hue/Saturation/Value color-space of the optical polyp images. The dataset used consisted of 165 polyps taken from 113 patients who underwent standard clinical prep prior to the procedures. Patients first had the CTC scan followed by the OC procedure, where the polyps where registered between imaging modalities and were pathologically confirmed for ground truth. Using a random forest classifier with a greedy feature selection algorithm, we find that the combination of using both CTC and OC texture features can improve the diagnostic performance by area under the receiver operating characteristic (AUC) score by upwards of 3%.
Weber’s law for image feature descriptor (WLD) is based on the theory that the ratio of the increment threshold to the background intensity is a constant. It has been used in facial recognition, structure detection, and tissue classification in X-ray images. In this paper, WLD is explored in the polyp classification in color colonoscopy images for the first time. An open, on-line colonoscopy image database is used to evaluate the new descriptor. The database contains 74 polyps, including 19 benign polyps and 55 malignant ones. Each polyp has a white light image (WLI) and a narrow band image (NBI), both were obtained by the same fibro-colonoscopy from the same patient. WLD image texture features are extracted from three color channels of (1) color WLI, (2) color NBI and (3) WLI+NBI. The extracted features are analyzed, ranked and classified using a Random Forest package based on the merit of the area under the curve (AUC) of the Receiver Operating Characteristics (ROC). The performance of WLD is quantitatively documented by the AUC, the ROC curve, the P-R (precision-recall) plot and the accuracy measure with comparison to commonly used features, such as Haralick and local binary pattern feature descriptors. The results demonstrate the advantage of WLD in the polyp classification in terms of the quantitative measures.
Clinical colonoscopy is currently the gold standard for polyp detection and resection. Both white light images (WLI) and narrow band images (NBI) could be obtained by the fibro-colonoscope from the same patient and currently used as a diagnosing reference for differentiation of hyperplastic polyps from adenomas. In this paper, we investigate the performance of WLI and NBI in different color spaces for polyp classification. A Haralick model with 30 co-occurrence matrix features is used in our experiments on 74 polyps, including 19 hyperplastic polyps and 55 adenomatous ones. The features are extracted from different color channels in each of three color spaces (RGB, HSV, chromaticity) and different derivative (intensity, gradient and curvature) images. The features from each derivative image in each color space are classified. The classification results from all the color spaces and all the derive images are input to a greedy machine learning program to verify the necessity of the integration of derivative image data and different color spaces. The feature classification and machine learning are implemented by the use of the Random Forest package. The wellknown area under the receiver operating characteristics curve is calculated to quantify the performances. The experiments validated the advantage of using the integration of the three derivatives of WLI and NBI and the three different color spaces for polyp classification.
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