Enhancement and automated segmentation of ultrasound knee cartilage for early diagnosis of knee osteoarthritis

Prajna Ramesh Desai, Ilker Hacihaliloglu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Ultrasound(US) has emerged as a valid imaging modality for quantitative assessment of femoral cartilage thickness for early diagnosis of knee osteoarthritis (OA). In this work, we are presenting a framework for automated segmentation of knee cartilage from enhanced US images. The proposed framework involves enhancement of US bone surfaces by calculating local phase image features, dynamic programming for bone segmentation and the use of segmented bone surfaces as initial seeds to random walker (RW) algorithm. Qualitative and quantitative validation was performed on 100 scans obtained from eight healthy volunteers. Validation against expert manual segmentation achieved a mean dice similarity coefficient (DSC) of 0.8758.

Original languageAmerican English
Title of host publication2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PublisherIEEE Computer Society
Pages1471-1474
Number of pages4
ISBN (Electronic)9781538636367
DOIs
StatePublished - May 23 2018
Event15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States
Duration: Apr 4 2018Apr 7 2018

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2018-April

Other

Other15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Country/TerritoryUnited States
CityWashington
Period4/4/184/7/18

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Keywords

  • Local phase
  • Osteoarthritis
  • Random walker
  • Segmentation
  • Ultrasound

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