Colonic Polyp Detection in CT Colonography with Fuzzy Rule Based 3D Template Matching

Eigenspace template matching for detection of lacunar infarcts on MR images

Detection of lacunar infarcts is important because their presence indicates an increased risk of severe cerebral infarction. However, accurate identification is often hindered by the difficulty in distinguishing between lacunar infarcts and enlarged Virchow-Robin spaces. Therefore, we developed a computer-aided detection (CAD) scheme for the detection of lacunar infarcts. Although our previous CAD method indicated a sensitivity of 96.8% with 0.71 false positives (FPs) per slice, further reduction of FPs remained an issue for the clinical application. Thus, the purpose of this study is to improve our CAD scheme by using template matching in the eigenspace. Conventional template matching is useful for the reduction of FPs, but it has the following two pitfalls: (1) It needs to maintain a large number of templates to improve the detection performance, and (2) calculation of the cross-correlation coefficient with these templates is time consuming. To solve these problems, we used template matching in the lower dimension space made by a principal component analysis. Our database comprised 1,143 T1- and T2-weighted images obtained from 132 patients. The proposed method was evaluated by using twofold cross-validation. By using this method, 34.1% of FPs was eliminated compared with our previous method. The final performance indicated that the sensitivity of the detection of lacunar infarcts was 96.8% with 0.47 FPs per slice. Therefore, the modified CAD scheme could improve FP rate without a significant reduction in the true positive rate.

Breast Cancer Detection with Reduced Feature Set

This paper explores feature reduction properties of independent component analysis (ICA) on breast cancer decision support system. Wisconsin diagnostic breast cancer (WDBC) dataset is reduced to one-dimensional feature vector computing an independent component (IC). The original data with 30 features and reduced one feature (IC) are used to evaluate diagnostic accuracy of the classifiers such as k-nearest neighbor (k-NN), artificial neural network (ANN), radial basis function neural network (RBFNN), and support vector machine (SVM). The comparison of the proposed classification using the IC with original feature set is also tested on different validation (5/10-fold cross-validations) and partitioning (20%–40%) methods. These classifiers are evaluated how to effectively categorize tumors as benign and malignant in terms of specificity, sensitivity, accuracy, F-score, Youden's index, discriminant power, and the receiver operating characteristic (ROC) curve with its criterion values including area under curve (AUC) and 95% confidential interval (CI). This represents an improvement in diagnostic decision support system, while reducing computational complexity.

Kernel eigenspace template matching for detection of lacunar infarcts on MR images

Detection of lacunar infarcts is important because their presence indicates an increased risk of severe cerebral infarction and dementia. However, accurate identification of lacunar infarcts is often difficult for radiologists. Our previous computer-aided detection (CAD) scheme achieved a sensitivity of 96.8% with 0.76 false positives (FPs) per slice. However, further reduction of FPs remained an issue for the clinical application. The purpose of this study is to improve our CAD scheme by using kernel eigenspace template matching. First, we selected the regions of interest (ROIs) around the candidate regions detected in our previous method. A kernel eigenspace was then made by using kernel principal component analysis of the training data set. A test ROI was projected onto the same kernel eigenspace as the training data set. The cross-correlation coefficients between the test ROI and all the training ROIs were calculated on the kernel eigenspace. By comparing the two maxima of coefficients with a lacunar ROI and an FP ROI, the test ROI was classified. By using the proposed method, the quantity of the templates became 1.9% of that in template matching on the real space and 31. 9% of FPs could be eliminated while keeping the same sensitivity; nevertheless 30.3% of FPs were eliminated when we employed the eigenspace template matching under the same condition. Therefore, kernel eigenspace template matching could improve FP rate without a significant reduction in the true positive rate.