Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 914124, 9 pages
Research Article

Computer Aided Quantification of Pathological Features for Flexor Tendon Pulleys on Microscopic Images

1Department of Computer Science & Information Engineering, National Cheng Kung University, Tainan 701, Taiwan
2Medical Device Innovation Center, National Cheng Kung University, Tainan 701, Taiwan
3Department of Neurosurgery, University of Pittsburgh, Pittsburgh, PA 15213, USA
4Department of Biomedical Engineering, National Cheng Kung University, Tainan 701, Taiwan
5Orthopedic Biomechanics Laboratory, Division of Orthopedic Research, Mayo Clinic Rochester, Rochester, MN 55905, USA
6Department of Pathology, Medical College, National Cheng Kung University, Tainan 701, Taiwan
7Department of Pathology, Ton-Yen General Hospital, Hsinchu 302, Taiwan
8Department of Orthopedic Surgery, Ton-Yen General Hospital, Hsinchu 302, Taiwan

Received 18 January 2013; Revised 22 May 2013; Accepted 26 May 2013

Academic Editor: Norio Tagawa

Copyright © 2013 Yung-Chun Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.