Tomoko Tateyama

Learn More
In this paper, we propose a new method to detect liver tumors in CT images automatically. The proposed method is composed of two steps. In the first step, tumor candidates are extracted by EM/MPM algorithm; which is used to cluster liver tissue. To cluster a dataset, EM/MPM algorithm exploits both intensity of voxels and labels of the neighboring voxels. It(More)
An automatic segmentation system for MR imaging is necessary for studies and 3-dimensional visualization of anatomical structures in many clinical and research applications. Since conventional classification systems use a simple linear classifier, non-linear model is not taken into consideration. In this paper, we propose a new method based on kernel(More)
It is widely known that morphological changes of the liver and the spleen occur during the clinical course of chronic liver diseases. In this paper, we proposed a morphological analysis method based on statistical shape models (SSMs) of the liver and spleen for computer-aided diagnosis and quantification of the chronic liver. We constructed not only the(More)
Automatic tumor detection and segmentation is essential for the computer-aided diagnosis of live tumors in CT images. However, it is a challenging task in low-contrast images as the low-level images are too weak to detect. In this paper, we propose a new method for the automatic detection of liver tumors. We first adaptively enhance the intensity contrast(More)
Statistical shape model (SSM) is to model the shape variation of an object. In this paper, we propose an efficient shape representation method and a new 2D-PCA based statistical shape modeling. In our proposed method, we used the radii of these surface points as shape feature instead of their coordinates, and the shape is represented by a 2D matrices. We(More)
In computational anatomy, statistical shape model (SSM) is used for the quantitative evaluation of variations in the shapes of different organs. This paper focuses on the construction of a SSM of the liver and its application to computer-assisted diagnosis of cirrhosis. We prove the potential application of SSMs in the classification of normal and cirrhotic(More)
Accurate segmentation of abdominal organs is a key step in developing a computer-aided diagnosis (CAD) system. Probabilistic atlas based on human anatomical structure, used as a priori information in a Bayes framework, has been widely used for organ segmentation. How to register the probabilistic atlas to the patient volume is the main challenge.(More)
This paper examines images taken from IKONOS to extract several features such as road relations automatically. We propose a new method which combines color, texture information and shape information for segmentation of high resolution satellite images. The method uses color and texture information for global segmentation, and shape information for local(More)
In computational anatomy, statistical shape model is used for quantitative evaluation of the variations of an organ shape. Since liver cirrhosis will cause significant hepatic morphological changes, we applied statistical shape model of the liver to capture the morphological changes and recognize whether a liver is normal or abnormal. In this paper, we(More)