Adaptive fuzzy segmentation of magnetic resonance images

By Dzung L. Pham, Jerry L Prince. 1999

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Citations in the corpus

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, neutral): “…protocols included a) skull removal using the BET algorithm (Smith, 2002) b) n3 bias correction using N3(Sled et al., 1998) c) segmentation using adaptive k-means (Pham and Prince, 1999) d) registration to a common template and generation of RAVENS maps using the (Davatzikos et al., 2001) protocol.”
HAMMER: hierarchical attribute matching mechanism for elastic registration (2002, unknown): “For the fuzzy segmentation case [50], each voxelhas the tissue classification result such as a vector , where , and take the real values between 0 and 1.”
Fast and robust multi-atlas segmentation of brain magnetic resonance images (2010, background): “As such, esign and implementation of alysis or writing of this report. at http://www.loni.ucla.edu/ l rights reserved. t al., Fast and robust multi-a 026 matter (WM), grey matter (GM), or cerebral spinal fluid (CSF) (Ashburner and Friston, 2005; Bazin and Pham, 2007; Pham and Prince, 1999; Scherrer ”
BrainSuite: An Automated Cortical Surface Identification Tool (2000, methodology): “Active contour methods often perform best after initialization with a low-level segmentation, as described by Xu et al. and Pham et al. [9,10].”
Magnetic resonance image tissue classification using a partial volume model. (2001, methodology): “In a similar manner, Pham and Prince (1999) incorporate a gain field into an adaptive fuzzy C-means algorithm to produce a spatially adaptive method for classification that compensates for image nonuniformity.”
Measurement of atrophy in multiple sclerosis: pathological basis, methodological aspects and clinical relevance. (2002, methodology): “Reliable segmentation is, in many cases, aided by the application of image non-uniformity correction (De Carli et al., 1992; Pham and Prince, 1999; Ashburner and Friston 2000).”
Spatial Models for Fuzzy Clustering (2001, methodology): “This is especially true because the penalty function can be applied in a straightforward manner to nearly all variants of FCM, such as the adaptive fuzzy C-means algorithm [13] and the possibilistic C-means algorithm [12], with minimal changes to the resulting implementation.”
Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation (2011, methodology): “The experiments and performance evaluation were carried on medical images from different modalities, including an ultrasound image of the carotid artery [24], a CT scan of liver tumors [9] and an MRI slice of cerebral tissues [25].”
Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline (2008, methodology): “…tissue segmentation into grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF), using a brain tissue segmentation method proposed in Pham and Prince (1999); (4) high-dimensional image warping (Shen and Davatzikos, 2002) to a standardized coordinate system, a brain atlas (template)…”
MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization (2005, result): “…an ar-tiﬁcial neural network (ANN), and compared the performance with FCM. FCM was shown to be superior on normal brains, but worse on abnormal brains with edema, tumor, etc. Pham and Prince [10] extended the traditional FCM algorithm to deal with MR images corrupted by intensity inhomogeneities.”
COMPARE: Classification of Morphological Patterns Using Adaptive Regional Elements (2007, methodology): “Each skull-stripped MR brain image is first segmented into three tissues, namely gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF), by a brain tissue segmentation method proposed in [23].”
A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data (2002, unknown): “This introduced some errors in the classification results and was found to be sensitive to a considerable amount of salt and pepper noise [20].”
Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study (2008, methodology): “…3) tissue segmentation into grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF), using a brain tissue segmentation method proposed in Pham and Prince (1999); 4) high-dimensional image warping (Shen and Davatzikos, 2002) to a standardized coordinate system, a brain atlas (template)…”
Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification (2013, unknown): “…raw T1-structural MR images using a pipeline that involved skull stripping using BET (Smith, 2002), followed by bias correction using N3 (Sled et al., 1998) and segmentation into the gray matter (GM), white matter(WM) and ventricular(VN) CSF using fuzzy c-means clustering (Pham and Prince, 1999).”
Anatomically motivated modeling of cortical laminae (2014, methodology): “For segmentation of the post-mortem images into gray and white matter (GM, WM), an automatic algorithm, Fantasm (Pham and Prince, 1999), was used to get a first crude estimation.”
Fuzzy c-means clustering with spatial information for image segmentation (2006, methodology): “Several investigators have developed methods to automate such measurements by segmentation [1–4].”
Retrospective correction of MR intensity inhomogeneity by information minimization (2000, methodology): “The authors of [24] believe that further research on incorporating spatial information through Markov random fields or atlas information may enhance the robustness.”
3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles (2019, methodology): “In the 1990s, fuzzy c-mean methods had been used to parcellate a brain MRI to three tissues: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) [1].”