Liver Tumor Segmentation Using Implicit Surface Evolution

Author:

Hame Yrjo

Abstract

A method for automatic liver tumor segmentation from computer tomography (CT) images is presented in this paper. Segmentation is an important operation before surgery planning, and automatic methods offer an alternative to laborious manual segmentation. In addition, segmentations of automatic methods are reproducible, so they can be reliably evaluated and they do not depend on the performer of the segmentation. In this work, the segmentation is performed in two stages. First a rough segmentation of tumors is obtained by simple thresholding and morphological operations. The second stage refines the rough segmentation result using fuzzy clustering and a geometric deformable model (GDM) that is fitted on the clustering result. The method was evaluated with data provided by Liver Tumor Segmentation Challenge 08, to which the method also participated. The data included 10 images from which 20 tumors were segmented. The method showed promising results.

Publisher

NumFOCUS - Insight Software Consortium (ITK)

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3