For cancer polyp detection based on CT colonography we investigate the sample variance of two methods for estimating the sensitivity and specificity. The goal is the reduction of sample variance for both error estimates, as a first step towards comparison with other detection schemes. Our detection scheme is based on a committee of support vector machines. The two estimates of sensitivity and specificity studied here are a smoothed bootstrap (the 632+ estimator), and ten-fold cross-validation. It is shown that the 632+ estimator generally has lower sample variance than the usual cross-validation estimator. When the number of nonpolyps in the training set is relatively small we obtain approximately 80% sensitivity and 50% specificity (for either method). On the other hand, when the number of nonpolyps in the training set is relatively large, estimated sensitivity (for either method) drops considerably. Finally, we consider the intertwined roles of relative sample sizes (polyp/nonpolyp), misclassification costs, and bias-variance reduction.
To improve computer aided diagnosis (CAD) for CT colonography we designed a hybrid classification scheme that uses a committee of support vector machines (SVMs) combined with a genetic algorithm (GA) for variable selection. The genetic algorithm selects subsets of four features, which are later combined to form a committee, with majority vote for classification across the base classifiers. Cross validation was used to predict the accuracy (sensitivity, specificity, and combined accuracy) of each base classifier SVM. As a comparison for GA, we analyzed a popular approach to feature selection called forward stepwise search (FSS). We conclude that genetic algorithms are effective in comparison to the forward search procedure when used in conjunction with a committee of support vector machine classifiers for the purpose of colonic polyp identification.
A multi-network decision classification scheme for colonic polyp detection is presented. The approach is based on the results of voting over several neural networks using different variable sets of size N which are selected randomly or by an expert from a general variable set of size M. Detection of colonic polyps is complicated by a large variety of polypoid looking shapes (haustral folds, leftover stool) on the colon surface. Using various shape and curvature characteristics, intensity, size measurements and texture features to distinguish real polyps from false positives leads to an intricate classification problem. We used 17 features including region density, Gaussian and average curvature and sphericity, lesion size, colon wall thickness, and their means and standard deviations in the vicinity of the prospective polyp. Selection of the most important parameters to reduce a feature set to acceptable size is a generally unsolved problem. The method suggested in this paper uses a collection of subsets of variables. These sets of variables are weighted by their effectiveness. The effectiveness cost function is calculated on the basis of the training and test sample mis-classification rates obtained by the training neural net with the given variable set. The final decision is based on the majority vote across the networks generated using the variable subsets, and takes into account the weighted votes of all nets. This method reduces the flst positive rate by a factor of 1.7 compared to single net decisions. The overall sensitivity and specificity rates reached are 100% and 95% correspondingly. Best specificity and sensitivity rates were reached using back propagation neural nets with one hidden layer trained with the Levenberg-Marquardt algorithm. Ten-fold cross-validation is used to better estimate the true error rates.
Virtual endoscopy reconstructions of the body noninvasively provide morphologic information of gross structural abnormalities such as stenoses in airways or blood vessels and polyps in the colonic wall. Surface irregularity or roughness is another indication of abnormality potentially detectible on virtual endoscopy. In this paper, we show how fractal dimension can be used to quantify surface roughness and how these methods may be applied to virtual angioscopy to distinguish the thoracic aorta in a normal volunteer from that of a patient predisposed to atherosclerosis. Finally, we discuss some problems we encountered applying fractal analysis to small, noisy datasets.
3D reconstruction of medical images is increasingly being used to diagnose disease and to direct therapy. Virtual bronchoscopy is a recently developed type of 3D reconstruction of the airways that may be useful for diagnosis of lesions of the airway. In this study, we compare two methods for computer-aided diagnosis of polypoid airway tumors: a parametric (`patch') and non-parametric ('grey-scale') algorithm. We found that both methods have comparable specificities. Although the non-parametric method is twelve times faster than the parametric method, we found that is sensitivity lags behind that of the parametric method by 3 to 16% when lesions of all sizes are considered. For lesions at least 5 mm in size, the sensitivities are comparable if a small convolution kernel is used.
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