The purpose of our study is to develop an algorithm that would enable the automated detection of lacunar infarct on T1- and T2-weighted magnetic resonance (MR) images. Automated identification of the lacunar infarct regions is not only useful in assisting radiologists to detect lacunar infarcts as a computer-aided detection (CAD) system but is also beneficial in preventing the occurrence of cerebral apoplexy in high-risk patients. The lacunar infarct regions are classified into the following two types for detection: "isolated lacunar infarct regions" and "lacunar infarct regions adjacent to hyperintensive structures." The detection of isolated lacunar infarct regions was based on the multiple-phase binarization (MPB) method. Moreover, to detect lacunar infarct regions adjacent to hyperintensive structures, we used a morphological opening processing and a subtraction technique between images produced using two types of circular structuring elements. Thereafter, candidate regions were selected based on three features -- area, circularity, and gravity center. Two methods were applied to the detected candidates for eliminating false positives (FPs). The first method involved eliminating FPs that occurred along the periphery of the brain using the region-growing technique. The second method, the multi-circular regions difference method (MCRDM), was based on the comparison between the mean pixel values in a series of double circles on a T1-weighted image. A training dataset comprising 20 lacunar infarct cases was used to adjust the parameters. In addition, 673 MR images from 80 cases were used for testing the performance of our method; the sensitivity and specificity were 90.1% and 30.0% with 1.7 FPs per image, respectively. The results indicated that our CAD system for the automatic detection of lacunar infarct on MR images was effective.

The purpose of this study is to develop an automated scheme of carotid artery calcification (CAC) detection on dental panoramic radiographs (DPRs). The CAC is one of the indices for predicting the risk of arteriosclerosis. First, regions of interest (ROIs) that include carotid arteries are determined on the basis of inflection points of the mandibular contour. Initial CAC candidates are detected by using a grayscale top-hat filter and a simple grayscale thresholding technique. Finally, a rule-based approach and a support vector machine to reduce the number of false positive (FP) findings are applied using features such as area, location, and circularity. A hundred DPRs were used to evaluate the proposed scheme. The sensitivity for the detection of CACs was 90% with 4.3 FPs (80% with 1.9 FPs) per image. Experiments show that our computer-aided detection scheme may be useful to detect CACs.

The enlargement of the left lobe of the liver and the shrinkage of the right lobe are helpful signs at MR imaging in diagnosis of cirrhosis of the liver. To investigate whether the volume ratio of left-to-whole (LTW) is effective to differentiate cirrhosis from a normal liver, we developed an automatic algorithm for three-dimensional (3D) segmentation and volume calculation of the liver region in multi-detector row CT scans and MR imaging. From one manually selected slice that contains a large liver area, two edge operators are applied to obtain the initial liver area, from which the mean gray value is calculated as threshold value in order to eliminate the connected organs or tissues. The final contour is re-confirmed by using thresholding technique. The liver region in the next slice is generated by referring to the result from the last slice. After continuous procedure of this segmentation on each slice, the 3D liver is reconstructed from all the extracted slices and the surface image can be displayed from different view points by using the volume rendering technique. The liver is then separated into the left and the right lobe by drawing an inter-segmental plane manually, and the volume in each part is calculated slice by slice. The degree of cirrhosis can be defined as the ratio of volume in these two lobes. Four cases including normal and cirrhotic liver with MR and CT slices are used for 3D segmentation and visualization. The volume ratio of LTW was relatively higher in cirrhosis than in the normal cases in both MR and CT cases. The average error rate on liver segmentation was within 5.6% after employing in 30 MR cases. These results demonstrate that the performance in our 3D segmentation was satisfied and the LTW ratio may be effective to differentiate cirrhosis.

Our purpose in this study is to develop a scheme to segment the rectus abdominis muscle region in X-ray CT images. We propose a new muscle recognition method based on the shape model. In this method, three steps are included in the segmentation process. The first is to generate a shape model for representing the rectus abdominis muscle. The second is to recognize anatomical feature points corresponding to the origin and insertion of the muscle, and the third is to segment the rectus abdominis muscles using the shape model. We generated the shape model from 20 CT cases and tested the model to recognize the muscle in 10 other CT cases. The average value of the Jaccard similarity coefficient (JSC) between the manually and automatically segmented regions was 0.843. The results suggest the validity of the model-based segmentation for the rectus abdominis muscle.