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Automatic brain and tumor segmentation
- Medical Image Computing and Computer-Assisted Intervention MICCAI 2002. Volume 2489 of LNCS
, 2002
"... Combining image segmentation based on statistical classification with a geometric prior has been shown to significantly increase robustness and reproducibility. Using a probabilistic geometric model of sought structures and image registration serves both initialization of probability density functio ..."
Abstract
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Cited by 14 (3 self)
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of existing structures not explained by the model. Our driving application is the segmentation of brain tissue and tumors from three-dimensional magnetic resonance imaging (MRI). Our goal is a high-quality segmentation of healthy tissue and a precise delineation of tumor boundaries. We present an extension
Segmentation and Quantification of Brain Tumor
, 2004
"... Nowadays, the inside situation could be obtained by medical machines, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) scanner, etc. These noninvasive diagnosis means increase the precision of the diagnoses, at the same time decrease the pain of the patients. Brain tumor diagnoses ..."
Abstract
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Cited by 5 (0 self)
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benefits from these devises very much. In the brain MR image, the tumor is shown clearly. For the treatment, the physician also needs the quantification of the tumor area. This requires the abnormal part in the image to be segmented accurately; afterward the segmented area can be measured. This task could
Robust estimation for brain tumor segmentation
- In: MICCAI 2003
, 2003
"... Abstract. Given models for healthy brains, tumor segmentation can be seen as a process of detecting abnormalities or outliers that are present with certain image intensity and geometric properties. In this paper, we propose a method that segments brain tumor and edema in two stages. We first detect ..."
Abstract
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Cited by 13 (0 self)
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Abstract. Given models for healthy brains, tumor segmentation can be seen as a process of detecting abnormalities or outliers that are present with certain image intensity and geometric properties. In this paper, we propose a method that segments brain tumor and edema in two stages. We first detect
Model-Based Brain and Tumor Segmentation
, 2002
"... Combining image segmentation based on statistical classification with a geometric prior has been shown to significantly increase robustness and reproducibility. Using a probabilistic geometric model of sought structures and image registration serves both initialization of probability density functi ..."
Abstract
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Cited by 11 (2 self)
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of existing structures. Our driving application is the segmentation of brain tissue and tumors from three-dimensional magnetic resonance imaging (MRI). We aim at both obtaining a high-quality segmentation of healthy tissue and a precise delineation of tumor boundaries. We present an extension to an existing
Automatic tumor segmentation using knowledge-based techniques
- IEEE Transactions on Medical Imaging
, 1998
"... A system that automatically segments and labels glioblastoma-multiforme tumors in mag-netic resonance images of the human brain is presented. The magnetic resonance images consist of T1-weighted, proton density, and T2-weighted feature images and are processed by a system which integrates knowledge- ..."
Abstract
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Cited by 69 (7 self)
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A system that automatically segments and labels glioblastoma-multiforme tumors in mag-netic resonance images of the human brain is presented. The magnetic resonance images consist of T1-weighted, proton density, and T2-weighted feature images and are processed by a system which integrates knowledge
A Review on Brain Tumor Segmentation
"... Abstract- Manually detecting and segmenting brain tumors from brain MRI, in cases where a large number of MRI scans are taken for each patient, is tedious and subjected to inter and intra observer detection and segmentation variability. Therefore, there is a need for computer aided brain tumor detec ..."
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Abstract- Manually detecting and segmenting brain tumors from brain MRI, in cases where a large number of MRI scans are taken for each patient, is tedious and subjected to inter and intra observer detection and segmentation variability. Therefore, there is a need for computer aided brain tumor
Recognizing Deviations from Normalcy for Brain Tumor Segmentation
"... Abstract. A framework is proposed for the segmentation of brain tumors from MRI. Instead of training on pathology, the proposed method trains exclusively on healthy tissue. The algorithm attempts to recognize deviations from normalcy in order to compute a fitness map over the image associated with t ..."
Abstract
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Cited by 26 (1 self)
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Abstract. A framework is proposed for the segmentation of brain tumors from MRI. Instead of training on pathology, the proposed method trains exclusively on healthy tissue. The algorithm attempts to recognize deviations from normalcy in order to compute a fitness map over the image associated
Automatic Brain and Tumor Segmentation 1
"... Combining image segmentation based on statistical classification with a geometric prior has been shown to significantly increase robustness and reproducibility. Using a probabilistic geometric model of sought structures and image registration serves both initialization of probability density functio ..."
Abstract
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of existing structures not explained by the model. Our driving application is the segmentation of brain tissue and tumors from three-dimensional magnetic resonance imaging (MRI). Our goal is a high-quality segmentation of healthy tissue and a precise delineation of tumor boundaries. We present an extension
Results 1 - 10
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382
