Muhammad Reza Pourshahabi;M. Omair Ahmad;M. N. S. Swamy
{"title":"A Very Fast and Robust Method for Refinement of Putative Matches of Features in MIS Images for Robotic-Assisted Surgery","authors":"Muhammad Reza Pourshahabi;M. Omair Ahmad;M. N. S. Swamy","doi":"10.1109/TMRB.2024.3369769","DOIUrl":null,"url":null,"abstract":"Robotic-assisted minimally invasive surgery (MIS) has a very important place in the landscape of modern surgical practices. Simultaneous localization and mapping (SLAM), 3D visualization, augmented reality, image registration and mosaicking are some of the image processing operations, which are often feature-based, used in robotic-assisted surgery. Feature matching refinement (FMR) is a crucial task in such operations. FMR is more critical, in cases where the percentage of true matches is very low, which is generally the case for MIS images. Since real-time is a requisite of MIS tasks, an FMR scheme must be very fast. In this paper we propose a very fast and accurate FMR scheme. The main idea used in developing the proposed scheme is on deciding the size of a local neighborhood and on devising a mechanism for checking feature topology preservation in the local neighborhood. To assess the effectiveness of the proposed scheme, we compare its performance with that of several state-of-the-art methods on different MIS image datasets, which shows its superiority in terms of both the processing time and performance.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on medical robotics and bionics","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10445304/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Robotic-assisted minimally invasive surgery (MIS) has a very important place in the landscape of modern surgical practices. Simultaneous localization and mapping (SLAM), 3D visualization, augmented reality, image registration and mosaicking are some of the image processing operations, which are often feature-based, used in robotic-assisted surgery. Feature matching refinement (FMR) is a crucial task in such operations. FMR is more critical, in cases where the percentage of true matches is very low, which is generally the case for MIS images. Since real-time is a requisite of MIS tasks, an FMR scheme must be very fast. In this paper we propose a very fast and accurate FMR scheme. The main idea used in developing the proposed scheme is on deciding the size of a local neighborhood and on devising a mechanism for checking feature topology preservation in the local neighborhood. To assess the effectiveness of the proposed scheme, we compare its performance with that of several state-of-the-art methods on different MIS image datasets, which shows its superiority in terms of both the processing time and performance.
机器人辅助微创手术(MIS)在现代外科手术中占有非常重要的地位。同时定位和绘图(SLAM)、三维可视化、增强现实、图像注册和镶嵌是机器人辅助手术中使用的一些图像处理操作,这些操作通常基于特征。特征匹配细化(FMR)是此类操作中的一项关键任务。在真实匹配率非常低的情况下,FMR 就显得更为重要,而 MIS 图像通常就是这种情况。由于实时性是 MIS 任务的必要条件,因此 FMR 方案必须非常快速。在本文中,我们提出了一种非常快速和准确的 FMR 方案。开发该方案的主要思路是决定局部邻域的大小,并设计一种机制来检查局部邻域中特征拓扑结构的保持情况。为了评估所提方案的有效性,我们在不同的 MIS 图像数据集上比较了该方案与几种最先进方法的性能,结果显示该方案在处理时间和性能方面都更胜一筹。