Bone segmentation in metacarpophalangeal MR data

Olga Kubassova*, Roger D. Boyle, Mike Pyatnizkiy

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (SciVal)

Abstract

A robust, efficient segmentation algorithm for automatic segmentation of MR images of the metacarpophalangeal joint is presented. A preliminary segmentation detects bones in MR scans and uses histogram analysis, morphological operations and knowledge based rules to classify various tissues in the joint. The second part of the algorithm improves the segmentation mask and refines boundaries of bones using minimization of a sum of square deviations, automatic signal segmentation into an optimum number of segments, graph theory, and statistical analysis. The algorithm has been tested on 9 MR patient studies and detects 97% of all existing bones correctly with an average exceeding 80% mutual overlap between ground truth and detected regions

Original languageEnglish
Pages (from-to)726-735
Number of pages10
JournalLecture Notes in Computer Science
Volume3687
Issue numberPART II
DOIs
Publication statusPublished - 2005
Externally publishedYes
EventThird International Conference on Advances in Patten Recognition, ICAPR 2005 - Bath, United Kingdom of Great Britain and Northern Ireland
Duration: 22 Aug 200525 Aug 2005

Fingerprint

Dive into the research topics of 'Bone segmentation in metacarpophalangeal MR data'. Together they form a unique fingerprint.

Cite this