Myofibre Segmentation in H&E Stained Adult Skeletal Muscle Images using Coherence-Enhancing Diffusion Filtering

Harry Strange, Ian Scott, Reyer Zwiggelaar

Research output: Contribution to journalArticlepeer-review

4 Citations (SciVal)
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Abstract

Background
The correct segmentation of myofibres in histological muscle biopsy images is a critical step in the automatic analysis process. Errors occurring as a result of incorrect segmentations have a compounding effect on latter morphometric analysis and as such it is vital that the fibres are correctly segmented. This paper presents a new automatic approach to myofibre segmentation in H&E stained adult skeletal muscle images that is based on Coherence-Enhancing Diffusion filtering.

Methods
The procedure can be broadly divided into four steps: 1) pre-processing of the images to extract only the eosinophilic structures, 2) performing of Coherence-Enhancing Diffusion filtering to enhance the myofibre boundaries whilst smoothing the interior regions, 3) morphological filtering to connect unconnected boundary regions and remove noise, and 4) marker controlled watershed transform to split touching fibres.

Results
The method has been tested on a set of adult cases with a total of 2,832 fibres. Evaluation was done in terms of segmentation accuracy and other clinical metrics.

Conclusions
The results show that the proposed approach achieves a segmentation accuracy of 89% which is a significant improvement over existing methods.
Original languageEnglish
Article number38
JournalBMC Medical Imaging
Volume14
DOIs
Publication statusPublished - 2014

Keywords

  • digital pathology
  • muscle biopsy
  • image segmentation

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