A Benchmark Framework for Multiregion Analysis of Vesselness Filters

计算机科学 人工智能 水准点(测量) 滤波器(信号处理) 分割 计算机视觉 图像处理 模式识别(心理学) 图像(数学) 大地测量学 地理
作者
Jonas Lamy,Odyssée Merveille,Bertrand Kerautret,Nicolas Passat
出处
期刊:IEEE Transactions on Medical Imaging [Institute of Electrical and Electronics Engineers]
卷期号:41 (12): 3649-3662 被引量:11
标识
DOI:10.1109/tmi.2022.3192679
摘要

Vessel enhancement (aka vesselness) filters, are part of angiographic image processing for more than twenty years. Their popularity comes from their ability to enhance tubular structures while filtering out other structures, especially as a preliminary step of vessel segmentation. Choosing the right vesselness filter among the many available can be difficult, and their parametrization requires an accurate understanding of their underlying concepts and a genuine expertise. In particular, using default parameters is often not enough to reach satisfactory results on specific data. Currently, only few benchmarks are available to help the users choosing the best filter and its parameters for a given application. In this article, we present a generic framework to compare vesselness filters. We use this framework to compare seven gold standard filters. Our experiments are performed on three public datasets: the hepatic Ircad dataset (CT images), the Bullit dataset (brain MRA images) and the synthetic VascuSynth dataset. We analyse the results of these seven filters both quantitatively and qualitatively. In particular, we assess their performances in key areas: the organ of interest, the whole vascular network neighbourhood and the vessel neighbourhood split into several classes, based on their diameters. We also focus on the vessels bifurcations, which are often missed by vesselness filters. We provide the code of the benchmark, which includes up-to-date C++ implementations of the seven filters, as well as the experimental setup (parameter optimization, result analysis, etc.). An online demonstrator is also provided to help the community apply and visually compare these vesselness filters.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
lc完成签到,获得积分10
3秒前
6秒前
李健的小迷弟应助anna采纳,获得10
7秒前
量子星尘发布了新的文献求助10
7秒前
9秒前
9秒前
嘀嘀咕咕发布了新的文献求助10
9秒前
大观天下完成签到,获得积分10
9秒前
科研通AI2S应助科研通管家采纳,获得10
10秒前
英姑应助科研通管家采纳,获得10
10秒前
科研通AI5应助科研通管家采纳,获得10
10秒前
10秒前
科研通AI2S应助科研通管家采纳,获得10
10秒前
共享精神应助科研通管家采纳,获得10
10秒前
科目三应助科研通管家采纳,获得10
10秒前
bkagyin应助科研通管家采纳,获得10
10秒前
SciGPT应助科研通管家采纳,获得10
10秒前
CodeCraft应助科研通管家采纳,获得10
10秒前
orixero应助科研通管家采纳,获得10
11秒前
脑洞疼应助科研通管家采纳,获得10
11秒前
11秒前
兴奋千兰发布了新的文献求助10
12秒前
有机发布了新的文献求助10
13秒前
yukang发布了新的文献求助10
13秒前
15秒前
大观天下发布了新的文献求助30
16秒前
16秒前
18秒前
19秒前
小盘子完成签到,获得积分10
19秒前
20秒前
今后应助务实的大神采纳,获得10
20秒前
anna发布了新的文献求助10
23秒前
23秒前
Elaine完成签到,获得积分10
23秒前
25秒前
nolan完成签到 ,获得积分10
25秒前
27秒前
彭于晏应助嘀嘀咕咕采纳,获得10
27秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989069
求助须知:如何正确求助?哪些是违规求助? 3531351
关于积分的说明 11253589
捐赠科研通 3269939
什么是DOI,文献DOI怎么找? 1804851
邀请新用户注册赠送积分活动 882074
科研通“疑难数据库(出版商)”最低求助积分说明 809073