ArticleJournal of medical systems2014
Brain MR image segmentation with spatial constrained K-mean algorithm and dual-tree complex wavelet transform.
Article in Journal of medical systems, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.Academic radiology · 2019Pooled it
- Molecular Dynamics Simulation Combined with Neural Relationship Inference and Markov Model to Reveal the Relationship between Conformational Regulation and Bioluminescence Properties ofMolecules (Basel, Switzerland) · 2024Article
- Segmentation of intrinsically very low contrast magnetic resonance brain images using tensor-based DTI registration.Neuroimage. Reports · 2022Article
- Comparison of Tongue Characteristics Classified According to Ultrasonographic Features Using a K-Means Clustering Algorithm.Diagnostics (Basel, Switzerland) · 2022Article
- An Efficient Implementation of Deep Convolutional Neural Networks for MRI Segmentation.Journal of digital imaging · 2018Article
- Spatial Fuzzy C Means and Expectation Maximization Algorithms with Bias Correction for Segmentation of MR Brain Images.Journal of medical systems · 2017Article
- Detection of Cardiac Abnormalities from Multilead ECG using Multiscale Phase Alternation Features.Journal of medical systems · 2016Article
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Authors and funding
4 authors.
Funding
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Abstract
In brain MR images, the noise and low-contrast significantly deteriorate the segmentation results. In this paper, we propose an automatic unsupervised segmentation method integrating dual-tree complex wavelet transform (DT-CWT) with K-mean algorithm for brain MR image. Firstly, a multi-dimensional feature vector is constructed based on the intensity, the low-frequency subband of DT-CWT and spatial position information. Then, a spatial constrained K-mean algorithm is presented as the segmentation system. The proposed method is validated by extensive experiments using both simulated and real T1-weighted MR images, and compared with the state-of-the-art algorithms.
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Registered trials
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