Microscopic image analysis for quantitative characterization of muscle fiber type composition

Sertel, Olcay; Dogdas, Belma; Chiu, Chi Sung; Gürcan, Metin N.

doi:10.1016/j.compmedimag.2011.01.009

Cited by 29 publications

(22 citation statements)

References 35 publications

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“…This threshold is automatically estimated using the well known Otsu method [6]. Following a similar methodology to [8], once the boundary segmentation has been produced, connected components with a total area of less than 750 pixels (674 μ m ) are removed. This ensures that any erroneous regions corresponding to staining or preparation artefacts are ignored from the final segmentation.…”

Section: Methodsmentioning

confidence: 99%

“…Once identified, the edges could be used as a priori information for further segmentation; it is worth noting however that the approach to such a segmentation is not mentioned in the original paper. Sertel et al [8] present a more established framework that uses edge detection as the first step in myofibre segmentation. Ridge analysis [9] is applied to ATPase stained cross-sections to segment the connective tissue so that thresholding the ridge likelihood image produces a rough segmentation of the connective tissue regions.…”

Section: Introductionmentioning

confidence: 99%

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Myofibre segmentation in H&E stained adult skeletal muscle images using coherence-enhancing diffusion filtering

2014

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BackgroundThe 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.MethodsThe 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.ResultsThe 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.ConclusionsThe results show that the proposed approach achieves a segmentation accuracy of 89% which is a significant improvement over existing methods.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Myofibre segmentation in H&E stained adult skeletal muscle images using coherence-enhancing diffusion filtering

2014

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“…Distance transform based methods that combine geometric and intensity information together were proposed in [1]. A ridge detection based muscle fiber segmentation algorithm is proposed in [2]. Radial voting based methods [3,4] were proposed to locate the center of cells with a major assumption on cell roundness, which does not hold in our case because myonuclei in muscle fibers contain both elongated ellipses and round circles.…”

Section: Introductionmentioning

confidence: 99%

Learning based automatic detection of myonuclei in isolated single skeletal muscle fibers using multi-focus image fusion

Xing

Lee

et al. 2013

2013 IEEE 10th International Symposium on Biomedical Imaging

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Accurate and robust detection of myonuclei in single muscle fiber is required to calculate myonuclear domain size. However, this task is challenging because: 1) The myonuclei have a variety of sizes and shapes. 2) Imaging techniques exhibit myonuclei that are often overlapping. 3) Inhomogeneous intensity due to DAPI concentration in heterochromatin, abundant in mouse nuclei, results in a speckled appearance inside each myonucleus. In this paper, we propose a novel automatic approach to robustly detect the myonuclei in isolated single skeletal muscle fibers. The original z-stack images are first fused into one all-in-focus image. A sufficient number of ellipse fitting hypotheses are then generated using the myonuclei contour segments. A set of morphological features are calculated from the ellipses and utilized to train a support vector machine (SVM) classifier to choose the best candidates. A modified inner geodesic distance based clustering algorithm is used to produce the final results. The proposed method was extensively tested using 42 sets of z-stack images containing about 1500 myonuclei. The method demonstrates excellent results outperforming current state-of-the-arts.

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“…The work of Sertel et al (2011) uses ridge detection for segmentation of ATPase stained muscle tissue. Similarly, edge-based active contour techniques have been used for individual muscle cell segmentation by Brox et al (2006); Kim et al (2007).…”

Section: Related Workmentioning

confidence: 99%

Charisma: An integrated approach to automatic H&E-stained skeletal muscle cell segmentation using supervised learning and novel robust clump splitting

Janssens

Antanas

Derde

et al. 2013

Medical Image Analysis

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Histological image analysis plays a key role in understanding the effects of disease and treatment responses at the cellular level. However, evaluating histology images by hand is time-consuming and subjective. While semi-automatic and automatic approaches for image segmentation give acceptable results in some branches of histological image analysis, until now this has not been the case when applied to skeletal muscle histology images. We introduce Charisma, a new top-down cell segmentation framework for histology images which combines image processing techniques, a supervised trained classifier and a novel robust clump splitting algorithm. We evaluate our framework on real-world data from intensive care unit patients. Considering both segmentation and cell property distributions, the results obtained by our method correspond well to the ground truth, outperforming other examined methods.

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Microscopic image analysis for quantitative characterization of muscle fiber type composition

Cited by 29 publications

References 35 publications

Myofibre segmentation in H&E stained adult skeletal muscle images using coherence-enhancing diffusion filtering

Myofibre segmentation in H&E stained adult skeletal muscle images using coherence-enhancing diffusion filtering

Learning based automatic detection of myonuclei in isolated single skeletal muscle fibers using multi-focus image fusion

Charisma: An integrated approach to automatic H&E-stained skeletal muscle cell segmentation using supervised learning and novel robust clump splitting

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