Learning from healthy and stable eyes: A new approach for detection of glaucomatous progression.
Glaucoma is a chronic neurodegenerative disease characterized by loss of retinal ganglion cells, resulting in distinctive changes in the optic nerve head (ONH) and retinal nerve fiber layer. Important advances in technology for non-invasive imaging of the eye have been made providing quantitative tools to measure structural changes in ONH topography, a crucial step in diagnosing and monitoring glaucoma. Three dimensional (3D) spectral domain optical coherence tomography (SD-OCT), an optical imaging technique, is now the standard of care for diagnosing and monitoring progression of numerous eye diseases.


METHODS
This paper aims to detect changes in multi-temporal 3D SD-OCT ONH images using a hierarchical fully Bayesian framework and then to differentiate between changes reflecting random variations or true changes due to glaucoma progression. To this end, we propose the use of kernel-based support vector data description (SVDD) classifier. SVDD is a well-known one-class classifier that allows us to map the data into a high-dimensional feature space where a hypersphere encloses most patterns belonging to the target class.


RESULTS
The proposed glaucoma progression detection scheme using the whole 3D SD-OCT images detected glaucoma progression in a significant number of cases showing progression by conventional methods (78%), with high specificity in normal and non-progressing eyes (93% and 94% respectively).


CONCLUSIONS
The use of the dependency measurement in the SVDD framework increased the robustness of the proposed change-detection scheme with comparison to the classical support vector machine and SVDD methods. The validation using clinical data of the proposed approach has shown that the use of only healthy and non-progressing eyes to train the algorithm led to a high diagnostic accuracy for detecting glaucoma progression compared to other methods.
University of California San Diego, Hamilton Glaucoma Center, 9500 Gilman Drive, La Jolla, CA 92093-0946, United States. Electronic address: abelghith@ucsd.edu.
Akram
A
Belghith
University of California San Diego, Hamilton Glaucoma Center, 9500 Gilman Drive, La Jolla, CA 92093-0946, United States. Electronic address: cbowd@ucsd.edu.
Christopher
C
Bowd
University of California San Diego, Hamilton Glaucoma Center, 9500 Gilman Drive, La Jolla, CA 92093-0946, United States. Electronic address: fmedeiros@ucsd.edu.
Felipe A
FA
Medeiros
University of Memphis, Department of Electrical and Computer Engineering, 3815 Central Avenue, Memphis, TN 38152, United States. Electronic address: mblsbrmn@memphis.edu.
Madhusudhanan
M
Balasubramanian
University of California San Diego, Hamilton Glaucoma Center, 9500 Gilman Drive, La Jolla, CA 92093-0946, United States. Electronic address: rweinreb@ucsd.edu.
Robert N
RN
Weinreb
University of California San Diego, Hamilton Glaucoma Center, 9500 Gilman Drive, La Jolla, CA 92093-0946, United States. Electronic address: lzangwill@ucsd.edu.
Linda M
LM
Zangwill
D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D018450:Disease Progression; D005123:Eye / Q000473:pathology*; D005901:Glaucoma / Q000473:pathology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional; D015233:Models, Statistical; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D013997:Time Factors; D041623:Tomography, Optical Coherence / Q000145:classification / Q000379:methods*
Copula theory; Glaucoma progression detection; Kernel classifier; Spatial dependency modeling