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Semiautomatic Extraction Algorithm for Images of the Ciliary Muscle

Kao, Chiu-Yen*; Richdale, Kathryn†; Sinnott, Loraine T.*; Grillott, Lauren E.‡; Bailey, Melissa D.§

Optometry & Vision Science:
doi: 10.1097/OPX.0b013e3182044b94
Original Article

Purpose. To develop and evaluate a semiautomatic algorithm for segmentation and morphological assessment of the dimensions of the ciliary muscle in Visante Anterior Segment Optical Coherence Tomography images.

Methods. Geometric distortions in Visante images analyzed as binary files were assessed by imaging an optical flat and human donor tissue. The appropriate pixel/mm conversion factor to use for air (n = 1) was estimated by imaging calibration spheres. A semiautomatic algorithm was developed to extract the dimensions of the ciliary muscle from Visante images. Measurements were also made manually using Visante software calipers. Interclass correlation coefficients and Bland-Altman analyses were used to compare the methods. A multilevel model was fitted to estimate the variance of algorithm measurements that was due to differences within- and between-examiners in scleral spur selection vs. biological variability.

Results. The optical flat and the human donor tissue were imaged and appeared without geometric distortions in binary file format. Bland-Altman analyses revealed that caliper measurements tended to underestimate ciliary muscle thickness at 3 mm posterior to the scleral spur in subjects with the thickest ciliary muscles (t = 3.6, p < 0.001). The percent variance due to within- or between-examiner differences in scleral spur selection was found to be small (6%) when compared with the variance because of biological difference across subjects (80%). Using the mean of measurements from three images, achieved an estimated interclass correlation coefficient of 0.85.

Conclusions. The semiautomatic algorithm successfully segmented the ciliary muscle for further measurement. Using the algorithm to follow the scleral curvature to locate more posterior measurements is critical to avoid underestimating thickness measurements. This semiautomatic algorithm will allow for repeatable, efficient, and masked ciliary muscle measurements in large datasets.

Author Information





Department of Mathematics and Mathematical Biosciences Institute, College of Mathematics and Physical Sciences (C-YK), and College of Optometry (KR, LTS, LEG, MDB), The Ohio State University, Columbus, Ohio.

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The research was supported by Award Number KL2 RR025754 (MDB) from the National Center for Research Resources, funded by the Office of the Director, National Institutes of Health (OD).

None of the authors have a commercial association with any products or instruments discussed in this manuscript.

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Center for Research Resources or the National Institutes of Health.

Received September 28, 2009; accepted September 14, 2010.

© 2011 American Academy of Optometry