Crohn's disease commonly affects the small and large intestines. Its symptoms include ulcers and intestinal stenosis, and its diagnosis is currently performed using an endoscope. However, because the endoscope cannot pass through the stenosed parts of the intestines, diagnosis of the entire intestines is difficult. A CT image-based method is expected to become an alternative way for the diagnosis of Crohn's disease because it enables observation of the entire intestine even if stenosis exists. To achieve efficient CT image-based diagnosis, diagnostic-aid by computers is required. This paper presents an automated detection method of the surface of ulcers in the small and large intestines from fecal tagging CT images. Ulcers cause rough surfaces on the intestinal wall and consist of small convex and concave (CC) regions. We detect them by blob and inverse-blob structure enhancement filters. A roughness value is utilized to reduce the false positives of the detection results. Many CC regions are concentrated in ulcers. The roughness value evaluates the concentration ratio of the detected regions. Detected regions with low roughness values are removed by a thresholding process. The thickness of the intestinal lumen and the CT values of the surrounding tissue of the intestinal lumen are also used to reduce false positives. Experimental results using ten cases of CT images showed that our proposed method detects 70.6% of ulcers with 12.7 FPs/case. The proposed method detected most of the ulcers.

We have proposed a recognition method for pulmonary nodules based on experimentally selected feature values (such as contrast, circularity, etc.) of pathologic candidate regions detected by our Variable N-Quoit (VNQ) filter. In this paper, we propose a new recognition method for pulmonary nodules by use of not experimentally selected feature values, but each CT value itself in a region of interest (ROI) as a feature value. The proposed method has 2 phases: learning and recognition. In the learning phase, first, the pathologic candidate regions are classified into several clusters based on a principal component score. This score is calculated from a set of CT values in the ROI that are regarded as a feature vector, and then eigen vectors and eigen values are calculated for each cluster by application of principal component analysis to the cluster. The eigen vectors (we call them "eigen-images") corresponding to the S-th largest eigen values are utilized as base vectors for subspaces of the clusters in a feature space. In the recognition phase, correlations are measured between the feature vector derived from testing data and the subspace which is spanned by the eigen-images. If the correlation with the nodule subspace is large, the pathologic candidate region is determined to be a nodule, otherwise, it is determined to be a normal organ. In the experiment, first, we decide on the optimal number of subspace dimensions. Then, we demonstrated the robustness of our algorithm by using simulated nodule images.