As such, more advanced methods are needed to segment the myofibre regions. Although methods exist to estimate the threshold automatically, or manually, thresholding is an inefficient method for myofibre segmentation as the fibres cannot be split from the background, or from each other, using a single threshold value. The central step of such an approach is the selection of a threshold value to separate the two regions such that they are well represented. The simplest automatic approach is to perform thresholding to segment the image into foreground and background regions, one corresponding to myofibres and the other representing background and connective tissue. However, these methods may fail in cases where there are weak fibre boundaries or an increased presence of noise due to inconsistencies in the staining process. These approaches either identify the pixels in the image associated with the fibres themselves or identify the boundaries of the fibres - the perimysium and endomysium (see Figure 1). There are numerous existing approaches to automatic myofibre segmentation ranging from simple thresholding to more advanced methods that use deformable models. As such, there is a need for automatic segmentation methods that provide accurate results and are robust to different imaging conditions. Inaccuracies in the segmentation of the myofibre boundary will lead to errors at later stages of analysis. One of the key steps for such automatic methods is the accurate segmentation of the myofibres from the surrounding connective structures (Figure 1). These drawbacks have led to the development of various techniques for automatic myofibre segmentation. Manual identification and measurement of myofibres and their boundaries can be time consuming, error prone, and can often suffer from intra and inter operator variability. When performing analysis on myofibre images it is useful to identify the myofibre boundary so that morphometric measures can be obtained. Further analysis of fibre size variation for those which exhibit a continuous distribution or biopsies showing a biphasic pattern can further assist in obtaining a diagnosis. Muscle biopsies in which there is no fibre size variation are most often normal. Detection of variation in fibre size is seen as a first step in the diagnosis of the majority of neuromuscular disorders including myopathic, dystrophic, neurogenic, and inflammatory conditions examples of which include mitochondrial cytopathies, muscular dystrophies, motor neuron disease, and dermatomyositis. The examination of stained biopsies from skeletal muscle is a vital component in the diagnostic pathway for the vast majority of neuromuscular disorders and remains essential as a preliminary investigation despite the availability of electron microscopic, genetic, and molecular tests for specific conditions. The results show that the proposed approach achieves a segmentation accuracy of 89% which is a significant improvement over existing methods. Evaluation was done in terms of segmentation accuracy and other clinical metrics. The method has been tested on a set of adult cases with a total of 2,832 fibres. 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. 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. 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. The correct segmentation of myofibres in histological muscle biopsy images is a critical step in the automatic analysis process.
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