Modified radial-search algorithm for segmentation of tibiofemoral cartilage in MR images of patients with subchondral lesion

  • Rafeek Thaha
  • Sandeep P. Jogi
  • Sriram Rajan
  • Vidur Mahajan
  • Vasantha K. Venugopal
  • Amit Mehndiratta
  • Anup SinghEmail author
Original Article



The quantitative analysis of weight-bearing articular cartilage superficial to subchondral abnormality is important in osteoarthritis (OA) progression studies. The current study aimed to address the challenges of a semi-automatic segmentation of tibiofemoral cartilage in MR images of OA patient with and without subchondral bone abnormalities (SBA).


In this study, knee MRI data [fat-suppressed proton density-weighted, multi-echo T2-weighted (CartiGram) images] of 29 OA patients, acquired at 3.0T MR scanner, were retrospectively collected. Out of 29 data, 9 had SBA in femur bone. Initially, a semi-automatic femur cartilage segmentation based on radial intensity search approach by Akhtar et al. was implemented in-house. This algorithm was considered as the radial-search method for further comparison. In this current study, the reported radial-search (RS)-based semi-automatic cartilage segmentation method was modified using thresholding, connected component labelling, convex-hull operation and spline-based curve fitting for the improved segmentation of tibiofemoral cartilage. Cartilage was manually segmented by two experienced radiologists, and inter-reader variability was estimated using coefficient of variation (CV). The segmentation results were validated using dice coefficient (DC), Jaccard coefficient (JC) and sensitivity index measurements.


DC values for segmented femur cartilage in patients with SBA were 64.6 ± 7.8% and 81.4 ± 2.8% using reported RS method and modified radial-search method, respectively. DC values for segmented femur cartilage in patients without SBA were 82.5 ± 4.5% and 84.8 ± 2.0% using RS method and modified radial method, respectively. Similarly, DC values for tibial cartilage in all OA patients were 80.4 ± 1.6% and 81.9 ± 2.4% using RS method and modified radial method, respectively. Similar segmentation results were also obtained from the T2-weighted images. Inter-reader variability result based on CV in femur cartilage was 3.40 ± 2.12% (without SBA) and 4.18 ± 3.18% (with SBA).


In the current study, a semi-automated segmentation of tibiofemoral cartilage was presented. Modified radial-search approach can successfully segment tibiofemoral cartilage, and the results were tested and validated on knee MRI data of OA patients with and without SBA.


Articular cartilage Osteoarthritis MRI Segmentation Subchondral bone abnormality 



Authors acknowledge the MRI data acquisition support from Mahajan Imaging centre, New Delhi, India. The authors would like to thank Dr. Harsh Mahajan for providing the clinical insights, Ms. Madhuri Bansal, an application analyst in Mahajan Imaging Centre for her support in data collection.

Author contributions

RT, AS and AM conceived and designed the experiments. RT, SPJ, SR and AS performed the experiments. RT, AS and SPJ analysed the data. RT, SPJ, SR, VKV, VM, AS and AM contributed materials/analysis tools. RT, SPJ, SR, VKV, VM, AS and AM contributed to the writing of the manuscript.


This study was supported by the Industrial Research & Development Unit, Indian Institute of Technology Delhi (FIRP Project Number-MI01422). The funding body had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.


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Copyright information

© CARS 2020

Authors and Affiliations

  • Rafeek Thaha
    • 1
  • Sandeep P. Jogi
    • 1
    • 2
  • Sriram Rajan
    • 3
  • Vidur Mahajan
    • 3
  • Vasantha K. Venugopal
    • 3
  • Amit Mehndiratta
    • 1
    • 4
  • Anup Singh
    • 1
    • 4
    Email author
  1. 1.Centre for Biomedical EngineeringIndian Institute of Technology DelhiNew DelhiIndia
  2. 2.Department of Biomedical EngineeringASET, Amity University HaryanaGurgaonIndia
  3. 3.Mahajan Imaging CentreNew DelhiIndia
  4. 4.Department of Biomedical EngineeringAll India Institute of Medical SciencesNew DelhiIndia

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