ArticleComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society2014
An image processing approach to analyze morphological features of microscopic images of muscle fibers.
Article in Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- A review: oxidative stress in skeletal muscle and the non-coding RNAs behind it.Molecular and cellular biochemistry · 2025Review
- Integrated Cells and Collagen Fibers Spatial Image Analysis.Frontiers in bioinformatics · 2021Article
- Characterization of heterogeneous primary human cartilage-derived cell population using non-invasive live-cell phase-contrast time-lapse imaging.Cytotherapy · 2021Article
- A neural network approach to analyze cross-sections of muscle fibers in pathological images.Computers in biology and medicine · 2019Article
- A computational approach to detect and segment cytoplasm in muscle fiber images.Microscopy research and technique · 2015Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
Abstract
We present an image processing approach to automatically analyze duo-channel microscopic images of muscular fiber nuclei and cytoplasm. Nuclei and cytoplasm play a critical role in determining the health and functioning of muscular fibers as changes of nuclei and cytoplasm manifest in many diseases such as muscular dystrophy and hypertrophy. Quantitative evaluation of muscle fiber nuclei and cytoplasm thus is of great importance to researchers in musculoskeletal studies. The proposed computational approach consists of steps of image processing to segment and delineate cytoplasm and identify nuclei in two-channel images. Morphological operations like skeletonization is applied to extract the length of cytoplasm for quantification. We tested the approach on real images and found that it can achieve high accuracy, objectivity, and robustness.
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Registered trials
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