WMH Segmentation Challenge: A Texture-Based Classification Approach

被引：4
|
作者：
Bento, Mariana [1 ,2 ,3 ]
de Souza, Roberto [1 ,3 ]
Lotufo, Roberto [3 ]
Frayne, Richard [1 ,2 ]
Rittner, Leticia [3 ]
机构：
[1] Univ Calgary, Hotchkiss Brain Inst, Radiol & Clin Neurosci, Calgary, AB, Canada
[2] Foothills Med Ctr, Calgary Image Proc & Anal Ctr, Calgary, AB, Canada
[3] Univ Estadual Campinas, Sch Elect & Comp Engn, Campinas, SP, Brazil
来源：
BRAINLESION: GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES, BRAINLES 2017 | 2018年 / 10670卷
基金：
巴西圣保罗研究基金会;
关键词：
White matter hyperintensity; MR imaging; Texture features; Segmentation; WHITE-MATTER LESIONS; MULTIPLE-SCLEROSIS; BRAIN MRI; IMAGES; HYPERINTENSITIES;
D O I：
10.1007/978-3-319-75238-9_41
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
This Grand Challenge at MICCAI 2017 aims to directly compare methods for the automatic segmentation of White Matter Hyperintensities (WMH) of presumed vascular origin. Our method automatically segment WMH by using texture-based classification of pixels within the brain white matter. It uses no a priori information about the WMH size, contrast or location. The main goal is to compute the probability of each pixel being normal or WMH tissue, by generating a probability map. Based on this probability map, we can automatically segment the WMHs.
引用
收藏
页码：489 / 500
页数：12
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