Tracking of tissue movement using distance-weighted log ratio similarity matching algorithm

Research output: Contribution to journalArticle

Abstract

Nowadays, the growth of health care quality awareness lead to the advancement of the medical technologies, especially for surgery technologies. In the field of computer vision, tracking of the tissues and internal organs (TDOD) movements have been beneficial to many surgical technologies such as computer-assisted surgery and minimally invasive surgery. TDOD tracking poses a challenging task due to the nature characteristic of TDOD which mainly has a homogenous surface and texture. We proposed a feature point tracking algorithm based on hypothesis testing t-test as a novel technique for TDOD tracking. This algorithm is based on the distance-weighted log ratio t-test similarity measurement. The algorithm has been tested and showed it can perform better compared with existing methods in all the test datasets.

Original languageEnglish
Pages (from-to)147-153
Number of pages7
JournalJournal of Telecommunication, Electronic and Computer Engineering
Volume10
Issue number1-2
Publication statusPublished - 1 Jan 2018

Fingerprint

Surgery
Tissue
Health care
Computer vision
Textures
Testing

Keywords

  • Distance
  • Feature Point
  • Hypothesis Test
  • Internal Organs
  • Matching
  • Tissues

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Cite this

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title = "Tracking of tissue movement using distance-weighted log ratio similarity matching algorithm",
abstract = "Nowadays, the growth of health care quality awareness lead to the advancement of the medical technologies, especially for surgery technologies. In the field of computer vision, tracking of the tissues and internal organs (TDOD) movements have been beneficial to many surgical technologies such as computer-assisted surgery and minimally invasive surgery. TDOD tracking poses a challenging task due to the nature characteristic of TDOD which mainly has a homogenous surface and texture. We proposed a feature point tracking algorithm based on hypothesis testing t-test as a novel technique for TDOD tracking. This algorithm is based on the distance-weighted log ratio t-test similarity measurement. The algorithm has been tested and showed it can perform better compared with existing methods in all the test datasets.",
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