Segmentation of brain MR images by using fully convolutional network and Gaussian mixture model with spatial constraints
type: Item
instance of: scholarly article
title: Segmentation of brain MR images by using fully convolutional network and Gaussian mixture model with spatial constraints
zbMATH Open document ID: 1435.94028
DOI: 10.1155/2019/4625371
author: Jiawei Lai
author: Hongqing Zhu
author: Xiaofeng Ling
published in: Mathematical Problems in Engineering
publication date: 2020-02-20T00:00:00Z
review text: Summary: Accurate segmentation of brain tissue from magnetic resonance images (MRIs) is a critical task for diagnosis, treatment, and clinical research. In this paper, a novel algorithm (GMMD-U) that incorporates the modified full convolutional neural network U-net and Gaussian-Dirichlet mixture model (GMMD) with spatial constraints is presented. The proposed GMMD-U considers the local spatial relationships by assuming that the prior probability obeys the Dirichlet distribution. Specifically, GMMD is applied for extracting brain tissue that has a distinct intensity region and modified U-net is exploited to correct the wrong-classification areas caused by GMMD or other conventional approaches. The proposed GMMD-U is designed to take advantage of the statistical model-based segmentation techniques and deep neural network. We evaluate the performance of GMMD-U on a publicly available brain MRI dataset by comparing it with several existing algorithms, and the results reported reveal that the proposed framework can accurately detect the brain tissue from MRIs. The proposed learning-based integrated framework could be effective for brain tissue segmentation, which will be helpful for surgeons in brain disease diagnosis.
Mathematics Subject Classification ID: 94A08
Mathematics Subject Classification ID: 92C55
zbMATH DE Number: 7170857
describes a project that uses: PRMLT
describes a project that uses: TensorFlow
describes a project that uses: U-Net
describes a project that uses: GitHub
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2019/4625371
OpenAlex ID: W2945275406
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Wikidata QID: Q127938776
description: scientific article