ArticleInternational Conference on Bioinformatics and Biomedical Engineering : [proceedings]. International Conference on Bioinformatics and Biomedical Engineering2011
A MR Brain Classification Method Based on Multiscale and Multiblock Fuzzy C-means.
Article in International Conference on Bioinformatics and Biomedical Engineering : [proceedings]. International Conference on Bioinformatics and Biomedical Engineering, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- MRI Brain Images Classification: A Multi-Level Threshold Based Region Optimization Technique.Journal of medical systems · 2018Article
- Nonrigid Registration and Classification of the Kidneys in 3D Dynamic Contrast Enhanced (DCE) MR Images.Proceedings of SPIE--the International Society for Optical Engineering · 2012Article
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Authors and funding
2 authors.
Funding
Abstract
A fully automatic, multiscale and multiblock fuzzy C-means (MsbFCM) classification method with intensity correction for MR images is presented in this paper. We use a bilateral filter to process MR images and to build a multiscale image series by increasing the standard deviation of spatial function and reducing the standard deviation of range function. We separate every scale image into multiple blocks and for every block a multiscale fuzzy C-means classification method is applied along the scales from the coarse to fine levels to overcome the effect of intensity inhomogeneity. The method is robust for noise MR images with intensity inhomogeneity because of its multiscale and multiblock bilateral filtering scheme. Our method was compared with the conventional FCM, a modified FCM (MFCM) and multiscale FCM (MsFCM) method on synthesized images, simulated brain MR images, and real MR images. The MsbFCM method achieved an overlap ratio of greater than 91% as validated by the ground truth even if original images have 9% noise and 40% intensity inhomogeneity. Experimental results using real MR images demonstrate the effectiveness of the proposed method. Our MsbFCM classification method is accurate and robust for various MR images.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.