In this article, we propose a pyramid multilayer machine learning method to combine classification and feature selection into the same model for polyp classification. This model provides a solution to pick the best attributes from three different texture features to form a new descriptor set with much better classification results. Generally, this method has several good properties including generalization, extendibility, and monotonicity. From its performance, the original metric image descriptor (MD) and the post-histogram-equalized metric image descriptor (PMD) form a descriptor pair as the preliminary unit of this pyramid framework. This model is driven by a feature merging performance unit run iteratively until the final results are obtained. After every feature merging step, a new attribute group is selected to construct a shorter but much stronger new descriptor. To reach this purpose, a forward selection method is adopted only to select attributes from every descriptor with positive gains for classification. Therefore, this feature merging performance provides a guarantee of the classification’s monotonicity in the practice. In our experiments, a simple scheme is designed to illustrate its construction and performance. Three image metrics are selected including intensity, gradient and curvature which are put into the gray-level co-occurrence matrix (CM) model to construct polyp descriptors. Random forest is chosen as the classifier and Gini coefficient is used to be the importance score. The AUC (area under the curve of receiver operating characteristics) scores are our evaluation measure. Experimental results showed that the pyramid learning model outperforms other methods over 4%-6% by AUC scores.
Inspired by the co-occurrence matrix (CM) model for texture description, we introduce another important local metric, gradient direction, into polyp descriptor construction. Gradient direction and its two independent components, azimuth angle and polar angle, are used instead of the gray-level intensity to calculate the CMs of the Haralick model. Thus we obtain three new models: azimuth CM model (ACM), polar CM model (PCM) and gradient direction CM model (GDCM). These three new models share similar parameters with the traditional gray-level CM (GLCM) model which has 13 directions for volumetric data and 4 directions for image slices. To train and test the data, random forest method is employed. These three models are affected by angle quantization and, therefore, more than 10 experimental schemes are designed to get reasonable parameters for angle discretization. We compared our three models (ACM, PCM, GDCM) with the traditional GLCM model, a gradient magnitude CM (GMCM) model, and local anisotropic gradient orientations CM model (CoLIAge). Experimental results showed that our three models exceed the other three methods (GLCM, GMCM, CoLIAge) by their receiver operating characteristic (ROC) curves, AUC (area under the ROC curve) scores and accuracy values. Based on their AUC and accuracy, ACM should be the first choice for polyp classification.
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.
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. Virtual colonoscopy has been shown to be a practical screening method and provide a high sensitivity and specificity for diagnosis between hyperplastic polyps and precancerous adenomas or adenocarcinomas through the use of texture feature analysis. We hypothesize that effects from nonhyperplastic polyps, such as angiogenesis from adenocarcinomas, may result in changes to the texture of the colon wall that could help with computer aided diagnosis of the colorectal polyps. Here we present the preliminary results of incorporating the texture features of neighboring colon wall tissue into the diagnostic classification. We use gray level co-occurrence matrices to calculate the established Haralick features and a set of supplemental features for colorectal polyp regions of interest, as well as for the neighboring colon wall environment of the polyp. A random forest package was then used to perform the classification tests on different sets of features, with and without the inclusion of the environment to obtain an area under the curve (AUC) value of the receiver operating characteristic (ROC). Experiments show approximately a 1% increase in overall classification performance with the inclusion of the environment features.
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.