Literature DB >> 11008183

MRI brain image segmentation by multi-resolution edge detection and region selection.

H Tang1, E X Wu, Q Y Ma, D Gallagher, G M Perera, T Zhuang.   

Abstract

Combining both spatial and intensity information in image, we present an MRI brain image segmentation approach based on multi-resolution edge detection, region selection, and intensity threshold methods. The detection of white matter structure in brain is emphasized in this paper. First, a multi-resolution brain image representation and segmentation procedure based on a multi-scale image filtering method is presented. Given the nature of the structural connectivity and intensity homogeneity of brain tissues, region-based methods such as region growing and subtraction to segment the brain tissue structure from the multi-resolution images are utilized. From the segmented structure, the region-of-interest (ROI) image in the structure region is derived, and then a modified segmentation of the ROI based on an automatic threshold method using our threshold selection criterion is presented. Examples on both T1 and T2 weighted MRI brain image segmentation is presented, showing finer brain tissue structures.

Mesh:

Year:  2000        PMID: 11008183     DOI: 10.1016/s0895-6111(00)00037-9

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  9 in total

1.  Development of identification of the central sulcus in brain magnetic resonance imaging.

Authors:  Norio Hayashi; Keita Sakuta; Kaori Minehiro; Masako Takanaga; Shigeru Sanada; Masayuki Suzuki; Tosiaki Miyati; Tomoyuki Yamamoto; Osamu Matsui
Journal:  Radiol Phys Technol       Date:  2010-09-29

2.  Automatic segmentation of the human brain ventricles from MR images by knowledge-based region growing and trimming.

Authors:  Jimin Liu; Su Huang; Wieslaw L Nowinski
Journal:  Neuroinformatics       Date:  2009-05-16

3.  LoAd: a locally adaptive cortical segmentation algorithm.

Authors:  M Jorge Cardoso; Matthew J Clarkson; Gerard R Ridgway; Marc Modat; Nick C Fox; Sebastien Ourselin
Journal:  Neuroimage       Date:  2011-02-23       Impact factor: 6.556

4.  Assessment of Glioma Response to Radiotherapy Using Multiple MRI Biomarkers with Manual and Semiautomated Segmentation Algorithms.

Authors:  Yang Yu; Dong-Hoon Lee; Shin-Lei Peng; Kai Zhang; Yi Zhang; Shanshan Jiang; Xuna Zhao; Hye-Young Heo; Xiangyang Wang; Min Chen; Hanzhang Lu; Haiyun Li; Jinyuan Zhou
Journal:  J Neuroimaging       Date:  2016-04-29       Impact factor: 2.486

5.  Semiautomated volumetry of the cerebrum, cerebellum-brain stem, and temporal lobe on brain magnetic resonance images.

Authors:  Norio Hayashi; Shigeru Sanada; Masayuki Suzuki; Yukihiro Matsuura; Kazuhiro Kawahara; Hideo Tsujii; Tomoyuki Yamamoto; Osamu Matsui
Journal:  Radiat Med       Date:  2008-02-27

Review 6.  Functional nanoparticles for magnetic resonance imaging.

Authors:  Xinpei Mao; Jiadi Xu; Honggang Cui
Journal:  Wiley Interdiscip Rev Nanomed Nanobiotechnol       Date:  2016-04-04

7.  Metasurface enabled quantum edge detection.

Authors:  Junxiao Zhou; Shikai Liu; Haoliang Qian; Yinhai Li; Hailu Luo; Shuangchun Wen; Zhiyuan Zhou; Guangcan Guo; Baosen Shi; Zhaowei Liu
Journal:  Sci Adv       Date:  2020-12-16       Impact factor: 14.136

8.  Multiple regression analysis of the craniofacial region of Chinese Han people using linear and angular measurements based on MRI.

Authors:  Chengzhi Li; Wei Wu; Bo Zhu; Xuefeng Liu; Ping Huang; Zhenyuan Wang; Ya Tuo; Fu Ren
Journal:  Forensic Sci Res       Date:  2017-03-30

9.  Frontal Cortex Segmentation of Brain PET Imaging Using Deep Neural Networks.

Authors:  Qianyi Zhan; Yuanyuan Liu; Yuan Liu; Wei Hu
Journal:  Front Neurosci       Date:  2021-12-08       Impact factor: 4.677

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.