ArticleComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society2009
Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field.
Article in Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
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- 3D AGSE-VNet: an automatic brain tumor MRI data segmentation framework.BMC medical imaging · 2022Article
- Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review.BMJ open · 2021Article
- A Deep Multi-Task Learning Framework for Brain Tumor Segmentation.Frontiers in oncology · 2021Article
- A Novel Radial Basis Neural Network-Leveraged Fast Training Method for Identifying Organs in MR Images.Computational and mathematical methods in medicine · 2020Article
- Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization.BMC medical imaging · 2017Article
- Semiautomatic tumor segmentation with multimodal images in a conditional random field framework.Journal of medical imaging (Bellingham, Wash.) · 2016Article
- Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI.NeuroImage. Clinical · 2016Article
- Diseased Region Detection of Longitudinal Knee Magnetic Resonance Imaging Data.IEEE transactions on medical imaging · 2015Article
- Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification.PloS one · 2015Article
- Rough-fuzzy clustering and unsupervised feature selection for wavelet based MR image segmentation.PloS one · 2015Article
- Automated liver lesion detection in CT images based on multi-level geometric features.International journal of computer assisted radiology and surgery · 2014Article
- Automated identification of brain tumors from single MR images based on segmentation with refined patient-specific priors.Frontiers in neuroscience · 2013Article
- MRI internal segmentation of optic pathway gliomas: clinical implementation of a novel algorithm.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2011Article
- A New Markov Random Field Segmentation Method for Breast Lesion Segmentation in MR images.Journal of medical signals and sensors · 2011Article
- Development of image-processing software for automatic segmentation of brain tumors in MR images.Journal of medical physics · 2011Article
- Quantification of SPIO nanoparticles in vivo using the finite perturber method.Magnetic resonance in medicine · 2011Article
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7 authors.
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Abstract
A variety of algorithms have been proposed for brain tumor segmentation from multi-channel sequences, however, most of them require isotropic or pseudo-isotropic resolution of the MR images. Although co-registration and interpolation of low-resolution sequences, such as T2-weighted images, onto the space of the high-resolution image, such as T1-weighted image, can be performed prior to the segmentation, the results are usually limited by partial volume effects due to interpolation of low-resolution images. To improve the quality of tumor segmentation in clinical applications where low-resolution sequences are commonly used together with high-resolution images, we propose the algorithm based on Spatial accuracy-weighted Hidden Markov random field and Expectation maximization (SHE) approach for both automated tumor and enhanced-tumor segmentation. SHE incorporates the spatial interpolation accuracy of low-resolution images into the optimization procedure of the Hidden Markov Random Field (HMRF) to segment tumor using multi-channel MR images with different resolutions, e.g., high-resolution T1-weighted and low-resolution T2-weighted images. In experiments, we evaluated this algorithm using a set of simulated multi-channel brain MR images with known ground-truth tissue segmentation and also applied it to a dataset of MR images obtained during clinical trials of brain tumor chemotherapy. The results show that more accurate tumor segmentation results can be obtained by comparing with conventional multi-channel segmentation algorithms.
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