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Non-rigid registration between CT and ultrasound images is a difficult task due to the low resolution and contrast of ultrasound images. We present a method incorporating the shape information of contours extracted from the image pairs for the registration. Firstly, the shapes are represented by the automatically detected landmarks along the contour of segmented object on CT and ultrasound images...
This paper presents a novel approach to automatically detect the fracture of skull in CT images. The approach consists of 5 steps: 1) skull segmentation, 2) skull extraction, 3) edge detection, 4) noise removal and, 5) image classification. Experiments show that the recognition rate is 99% for 100 images that are randomly chosen from a medical image database contributed by Hospital Putrajaya, Malaysia...
In this paper, we propose a methodology consists of several unsupervised clustering techniques to acquire a satisfactory segmentation of computed tomography (CT) brain images. The ultimate goal of segmentation is to obtain three segmented images, which are the abnormalities, cerebrospinal fluid (CSF) and brain matter respectively. The proposed approach contains of two phase-segmentation methods. In...
Non-rigid registration of monomodal image often takes an important role in image-guided radiotherapy and surgery. Viscous fluid model is widely used to enforce the topological properties on the deformation, and thus constrain the enormous solution space. Intensity-based method is popular in non-rigid registration, but it is sensitive to intensity variations. In this paper, we develop a new algorithm...
Automatic kidney segmentation from abdominal computed tomography (CT) images is a key step in computer-aided diagnosis for kidney CT. However, due to gray levels similarities of adjacent organ??s positions and shapes, automatically identifying abdominal organs has always been a high challenging task. In this paper, we proposed an automatic segmentation method integrated some prior knowledge into traditional...
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