Automatic MRI brain tissue extraction algorithm based on three-dimensional gray-scale transformation model

Jinzhu Yang; Wenjun Tan; Shuang Ma; Qi Sun; Mengjia Xu; Nan Chen; Dazhe Zhao

doi:10.1166/jmihi.2014.1340

Automatic MRI brain tissue extraction algorithm based on three-dimensional gray-scale transformation model

Jinzhu Yang, Wenjun Tan, Shuang Ma, Qi Sun, Mengjia Xu, Nan Chen, Dazhe Zhao

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

In this paper, an automatic MRI brain tissue extraction method is proposed. Firstly, the thresholds of the brain skull, brain tissue, and cerebrospinal fluid are computed by the adaptive threshold algorithm. Then the thresholds are adopted to estimate the center of gravity (COG) and radius of the brain tissue in MRI images. Thirdly, a grayscale transformation model is proposed to enhance the contrast of the boundary between the brain and non-brain tissue. Finally, a pre-judgment 3D region growing algorithm is used to extract the brain tissue, which effectively reduces the number of segmentation errors. The results show that the proposed method is more accurate and robust than BET (Brain extraction Tool).

Original language	English (US)
Pages (from-to)	907-911
Number of pages	5
Journal	Journal of Medical Imaging and Health Informatics
Volume	4
Issue number	6
DOIs	https://doi.org/10.1166/jmihi.2014.1340
State	Published - Dec 1 2014
Externally published	Yes

All Science Journal Classification (ASJC) codes

Radiology Nuclear Medicine and imaging
Health Informatics

Keywords

Brain Tissue Extraction
Grayscale Transformation Model
MRI
Pre-Judgment Region Growing

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10.1166/jmihi.2014.1340

Cite this

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title = "Automatic MRI brain tissue extraction algorithm based on three-dimensional gray-scale transformation model",

abstract = "In this paper, an automatic MRI brain tissue extraction method is proposed. Firstly, the thresholds of the brain skull, brain tissue, and cerebrospinal fluid are computed by the adaptive threshold algorithm. Then the thresholds are adopted to estimate the center of gravity (COG) and radius of the brain tissue in MRI images. Thirdly, a grayscale transformation model is proposed to enhance the contrast of the boundary between the brain and non-brain tissue. Finally, a pre-judgment 3D region growing algorithm is used to extract the brain tissue, which effectively reduces the number of segmentation errors. The results show that the proposed method is more accurate and robust than BET (Brain extraction Tool).",

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T1 - Automatic MRI brain tissue extraction algorithm based on three-dimensional gray-scale transformation model

AU - Yang, Jinzhu

AU - Tan, Wenjun

AU - Ma, Shuang

AU - Sun, Qi

AU - Xu, Mengjia

AU - Chen, Nan

AU - Zhao, Dazhe

PY - 2014/12/1

Y1 - 2014/12/1

N2 - In this paper, an automatic MRI brain tissue extraction method is proposed. Firstly, the thresholds of the brain skull, brain tissue, and cerebrospinal fluid are computed by the adaptive threshold algorithm. Then the thresholds are adopted to estimate the center of gravity (COG) and radius of the brain tissue in MRI images. Thirdly, a grayscale transformation model is proposed to enhance the contrast of the boundary between the brain and non-brain tissue. Finally, a pre-judgment 3D region growing algorithm is used to extract the brain tissue, which effectively reduces the number of segmentation errors. The results show that the proposed method is more accurate and robust than BET (Brain extraction Tool).

AB - In this paper, an automatic MRI brain tissue extraction method is proposed. Firstly, the thresholds of the brain skull, brain tissue, and cerebrospinal fluid are computed by the adaptive threshold algorithm. Then the thresholds are adopted to estimate the center of gravity (COG) and radius of the brain tissue in MRI images. Thirdly, a grayscale transformation model is proposed to enhance the contrast of the boundary between the brain and non-brain tissue. Finally, a pre-judgment 3D region growing algorithm is used to extract the brain tissue, which effectively reduces the number of segmentation errors. The results show that the proposed method is more accurate and robust than BET (Brain extraction Tool).

KW - Brain Tissue Extraction

KW - Grayscale Transformation Model

KW - MRI

KW - Pre-Judgment Region Growing

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Automatic MRI brain tissue extraction algorithm based on three-dimensional gray-scale transformation model

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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