Segmentation of 3D brain MR using an adaptive K-means clustering algorithm

Background: Converging neuroanatomic, neurophysiological, and neurobehavioral evidence implicate prefrontal subregions in schizophrenia. Neuroanatomic studies with magnetic resonance (MR) imaging enable regional volume parcellation. Inconsistent reports may relate to variable methods and small samples. We attempted to resolve volume differences within sectors of the prefrontal lobe in a large sample, relating volumes to clinical and neurocognitive features.

show abstract

“…41,42 The stripped parenchyma was segmented into GM and WM using adaptive Bayesian algorithms. 28,43,44 …”

Section: Image Processingmentioning

confidence: 99%

Reduced Dorsal and Orbital Prefrontal Gray Matter Volumes in Schizophrenia

Gur¹,

Cowell²,

Latshaw³

et al. 2000

Arch Gen Psychiatry

346

193

View full text Add to dashboard Cite

show abstract

“…2 The brain volume was extracted by automatically stripping scalp, skull, and meninges using optimal thresholding and morphological operations on the image intensity and chamfer distance (an easily computed approximation of the distance from any given point to the head surface). 33,34 Some nonbrain regions, such as bone marrow and the eyeballs, could not be reliably stripped by this algorithm and were removed manually in an interactive program. The stripped MRI image was segmented into GM, WM, and CSF using an adaptive Bayesian algorithm 35,36 that models the image as a collection of tissue compartments with slowly varying mean intensity plus white gaussian noise.…”

Section: Clinical Measuresmentioning

confidence: 99%

Reduced Gray Matter Volume in Schizophrenia

Gur¹,

Turetsky²,

Bilker³

et al. 1999

Arch Gen Psychiatry

152

View full text Add to dashboard Cite

show abstract

“…K-means clustering is one of the most popular statistical clustering techniques used in segmentation of medical images [43], [44], [45]. The name K-means originates from the means of the k clusters that are created from n objects.…”

Section: G Adaptation Of C-means To Rough Set Theorymentioning

confidence: 99%

Rough Sets and Near Sets in Medical Imaging: A Review

Hassanien¹,

Abraham²,

Peters³

et al. 2009

IEEE Trans. Inform. Technol. Biomed.

148

View full text Add to dashboard Cite

Abstract-This paper presents a review of the current literature on rough set and near set-based approaches to solving various problems in medical imaging such as medical image segmentation, object extraction and image classification. Rough set frameworks hybridized with other computational intelligence technologies that include neural networks, particle swarm optimization, support vector machines and fuzzy sets are also presented. In addition, a brief introduction to near sets and near images with an application to MRI images is given. Near sets offer a generalization of traditional rough set theory and a new approach to classifying perceptual objects by means of features in solving medical imaging problems. Challenges to be addressed and future directions of research are identified and an extensive bibliography is also included.

show abstract

Segmentation of 3D brain MR using an adaptive K-means clustering algorithm

Cited by 23 publications

References 13 publications

Reduced Dorsal and Orbital Prefrontal Gray Matter Volumes in Schizophrenia

Reduced Dorsal and Orbital Prefrontal Gray Matter Volumes in Schizophrenia

Reduced Gray Matter Volume in Schizophrenia

Rough Sets and Near Sets in Medical Imaging: A Review

Contact Info

Product

Resources

About