AI Article Synopsis

  • The study highlights the importance of quantifying flexor tendon pulley features for accurately grading trigger finger, providing objective data from microscopic images.
  • A color-based image segmentation system is developed to automatically extract pathological features, improving efficiency and reducing reliance on observer skill.
  • Results demonstrated strong alignment with expert pathologist evaluations, suggesting this system could significantly enhance routine histopathological assessments.

Article Abstract

Quantifying the pathological features of flexor tendon pulleys is essential for grading the trigger finger since it provides clinicians with objective evidence derived from microscopic images. Although manual grading is time consuming and dependent on the observer experience, there is a lack of image processing methods for automatically extracting pulley pathological features. In this paper, we design and develop a color-based image segmentation system to extract the color and shape features from pulley microscopic images. Two parameters which are the size ratio of abnormal tissue regions and the number ratio of abnormal nuclei are estimated as the pathological progression indices. The automatic quantification results show clear discrimination among different levels of diseased pulley specimens which are prone to misjudgments for human visual inspection. The proposed system provides a reliable and automatic way to obtain pathological parameters instead of manual evaluation which is with intra- and interoperator variability. Experiments with 290 microscopic images from 29 pulley specimens show good correspondence with pathologist expectations. Hence, the proposed system has great potential for assisting clinical experts in routine histopathological examinations.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3690227PMC
http://dx.doi.org/10.1155/2013/914124DOI Listing

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