WML Segmentation Methods Comparison and Strategy Improvement

Chinese

您当前的位置：

首页 >

文章列表页 >

WML Segmentation Methods Comparison and Strategy Improvement

更新时间：2026-09-04

- WML Segmentation Methods Comparison and Strategy Improvement
- Vol. 28, Issue 10, Pages: 84-86(2011)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2011
- 稿件说明：
移动端阅览
脑白质损伤分割方法比较和策略改进[J]. 微电子学与计算机, 2011, 28(10): 84-86,90.

WML Segmentation Methods Comparison and Strategy Improvement[J]. 2011, 28(10): 84-86.
脑白质损伤分割方法比较和策略改进[J]. 微电子学与计算机, 2011, 28(10): 84-86,90. DOI：

WML Segmentation Methods Comparison and Strategy Improvement[J]. 2011, 28(10): 84-86. DOI：

摘要

当前对白质损伤分割研究的缺陷就是不能真正用于实时分割任务

其主要原因是所用特征的表现性能较低.针对此问题

提出了新的数据处理方法和损伤分割特征.方法通过划分数据减少处理数据量的同时采用分治策略

区分处理白质灰质区域

从而改善了整体白质损伤分割的性能.在ACCORD-MIND MRI数据集上

使用SVM分类器将该新特征和传统特征的分割性能作了比较.实验结果表明

该特征在白质损伤分割中优于传统的特征

并能获得较好的分割结果和较快的分割速度.

Abstract

The shortage of current algorithm of White Matter Lesion Segmentation is not really can be used in realtime tasks.The main reason is the low performance characteristics to be used.To solve this problem

this paper proposes a new White Matter Lesion Segmentation feature and method of data processing to improve the performance of the White Matter Lesion Segmentation.This method uses Divide and Conquer and treats WM and GM differently.With the ACCORD-MIND data set of MRI and SVM classifier

we compare this new algorithm with traditional segmentation performance.The experimental results show that this feature is superior to the traditional features

and can obtain good segmentation results and fast segmentation speed.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A review of large model development: technological evolution from BERT to Deepseek

Aerial small object detection algorithm based on improved RT-DETR

Implementation and verification of a dual-chain power gating strategy for low power design

A feature-oriented context understanding and slice analysis network for tuberculosis detection

Aerial vehicle detection algorithm based on multi-scale fusion and small-object enhancement

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰