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Object boundary extraction is an important task in brain image analysis. Acquiring detailed 3D representations of the brain structures could improve the detection rate of diseases at earlier stages. Deformable model based segmentation methods have been widely used with considerable success. Recently, 3D Active Volume Model (AVM) was proposed, which incorporates both gradient and region information...
The poor signal-to-noise ratio in transrectal ultrasound (TRUS) images makes the fully automatic segmentation of the prostate challenging and most approaches proposed in the literature still lack robustness and accuracy. However, it is relatively straightforward to obtain high quality segmentations in magnetic resonance (MR) images. In the context of MR to TRUS data fusion the information gathered...
In this paper we propose a new hybrid segmentation approach of the deep brain structures based on a multi-contrast deformable model of regions in competition, with deformations preserving the topology of the structures, as well as their shape and position, using a probabilistic atlas and some prior morphological information. The accuracy of our method was evaluated by comparing the results obtained...
We propose a new and clinically oriented approach to perform atlas-based segmentation of brain tumor images. A mesh-free method is used to model tumor-induced soft tissue deformations in a healthy brain atlas image with subsequent registration of the modified atlas to a pathologic patient image. The atlas is seeded with a tumor position prior and tumor growth simulating the tumor mass effect is performed...
The brain shape is deformed regionally by kinds of cerebral diseases and the degree of progress. Therefore quantitative evaluation of the deformation using MR images is effective for diagnosis of cerebral diseases. To evaluate the cerebral deformation, almost conventional methods are based on normalization of the brain shape which deforms the evaluating brain into the standardized brain. Because the...
Prostate cancer is the second leading cause of cancer death in American men. Current prostate MRI can benefit from automated tumor localization to help guide biopsy, radiotherapy and surgical planning. An important step of automated prostate cancer localization is the segmentation of the prostate. In this paper, we propose a fully automatic method for the segmentation of the prostate. We firstly apply...
Functional segmentation of brain images is important in understating the relationships between anatomy and mental diseases in brains. Volumetric analysis of various brain structures such as the cerebellum plays a critical role in studying the structural changes in brain regions as a function of development, trauma, or neurodegeneration. Although various segmentation methods in clinical studies have...
Segmentation of the brain internal structures is an important and a challenging task due to their small size, partial volume effects, and anatomical variability. In this paper we propose a method that segments automatically the deep brain internal structures from brain MRI images. It uses a combination of local affine transformation and optical flow based non-rigid registration, which has the advantages...
Segmentation using deformable model-based approaches has become a common application in 3D medical image analysis, as it provides a fast and robust method to detect structures, e.g. organs, fully-automated after initialization. However, some earlier approaches in some cases did not converge in the segmentation process even on rather good datasets or showed major variations in the segmentation results...
A new scheme for femur shape recovery from volumetric images using deformable models was proposed. First, prior 3D deformable femur models are created as templates using point distribution models technology. Second, active contour models are employed to segment the magnetic resonance imaging (MRI) volumetric images of the tibial and femoral joint and the deformable models are initialized based on...
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