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Literature DB >> 16240091

Implementation of high-dimensional feature map for segmentation of MR images.

Renjie He¹, Balasrinivasa Rao Sajja, Ponnada A Narayana.

Abstract

A method that considerably reduces the computational and memory complexities associated with the generation of high-dimensional (> or =3) feature maps for image segmentation is described. The method is based on the K-nearest neighbor (KNN) classification and consists of two parts: preprocessing of feature space and fast KNN. This technique is implemented on a PC and applied for generating 3D and 4D feature maps for segmenting MR brain images of multiple sclerosis patients.

Entities: Disease Species

Mesh：

Year: 2005 PMID： 16240091 PMCID： PMC1409759 DOI： 10.1007/s10439-005-5888-3

Source DB: PubMed Journal: Ann Biomed Eng ISSN： 0090-6964 Impact factor: 3.934

11 in total

1. Time-series analysis of MRI intensity patterns in multiple sclerosis.

Authors: Dominik S Meier; Charles R G Guttmann
Journal: Neuroimage Date: 2003-10 Impact factor: 6.556

2. Segmentation of magnetic resonance brain images through discriminant analysis.

Authors: Umberto Amato; Michele Larobina; Anestis Antoniadis; Bruno Alfano
Journal: J Neurosci Methods Date: 2003-12-30 Impact factor: 2.390

3. Global optimization of mutual information: application to three-dimensional retrospective registration of magnetic resonance images.

Authors: Renjie He; Ponnada A Narayana
Journal: Comput Med Imaging Graph Date: 2002 Jul-Aug Impact factor: 4.790

Implementation of high-dimensional feature map for segmentation of MR images.

1. Time-series analysis of MRI intensity patterns in multiple sclerosis.

2. Segmentation of magnetic resonance brain images through discriminant analysis.

3. Global optimization of mutual information: application to three-dimensional retrospective registration of magnetic resonance images.

4. Probabilistic segmentation of white matter lesions in MR imaging.

5. Three-dimensional segmentation of MR images of the head using probability and connectivity.

6. Volumetric analysis of white matter, gray matter, and CSF using fractional volume analysis.

7. A dual approach for minimizing false lesion classifications on magnetic resonance images.

8. MRI: stability of three supervised segmentation techniques.

9. Comparison of supervised MRI segmentation methods for tumor volume determination during therapy.

10. New variants of a method of MRI scale standardization.

1. Volume and shape in feature space on adaptive FCM in MRI segmentation.

2. Generalized fuzzy clustering for segmentation of multi-spectral magnetic resonance images.