The Application of Image Processing for Fourier Domain Optical Coherence Tomography Image in Glaucoma diagnosis for High Myopia Patient

• Main Authors: Chih-Huan Liu, 劉智桓
• Other Authors: Jenn-Lung Su
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/15024544003611495644

碩士 === 中原大學 === 生物醫學工程研究所 === 100 === Glaucoma is one of familiar blind diseases in the ophthalmology. Glaucoma only can temporarily decelerate worse degree after treatments, thus, it’s important to discover and treat this disease in early stages. The Ganglion Cell Complex (GCC) thickness which can be detected by Fourier Domain Optical Coherence Tomography (FD-OCT) is the most important indicator for early glaucoma patient. For a patient with high myopia, the axial length of eyeball will be stretched, furthermore as the retinal have been extent as thinning. It may cause the calculation of the thickness of GCC has be failing, and interfere to the diagnosis of glaucoma. Therefore, in order to increase the accuracy in diagnosis the glaucoma for high myopia patient, a myopic correction model based on mathematical calculations and image processing technology was developed in this study, to correct the thickness in macular retinal tomography images for FD-OCT. The FD-OCT scans of macular, and simulating the point out of scans by Cubic Spline Interpolation was used to complete full data of GCC thickness of macular. And then two characteristic parameters, mean GCC thickness and index of asymmetric, were obtained by using myopic correction model to modify the GCC thickness. Finally, two kind of classification method, classifying by threshold and classifying by neuron network, were used. This procedure was applied to 18 patients (36 eyes) with glaucoma and 15 normal subjects (30 eyes) which diagnosed by physician. In 36 eyes with glaucoma include 18 eyes with early glaucoma (12 eyes with high myopia and 6 eyes with myopia less -6D) and 18 eyes with advance glaucoma (8 eyes with high myopia and 10 eyes with myopia less -6D). In 30 eyes of normal subjects include 12 eyes with high myopia and 18 eyes with myopia less -6D. In system evaluation, the material was divided into training group and the test group, to classify normal subjects and glaucoma (include early glaucoma and advance glaucoma). After the myopia modification and in classifying by threshold method, beside sensitivity remained at 1, accuracy, specificity, and kappa value increased from 0.7 to 0.91, from 0.33 to 0.8, and from 0.35 to 0.81, respectively; And in classifying by neuron network method, beside specificity remained at 1, accuracy, sensitivity, and kappa value increased from 0.88 to 0.97, 0.78 to 0.94, and upgrade from 0.76 to 0.94, respectively. Results show that the ability can be enhancing of classification of normal subjects and glaucoma by myopia modification. Moreover, the effect of myopia modification for classification of normal subject and early glaucoma shows much better performance. A system based on the myopic correction model for modification GCC thickness of macular and two characteristic parameters (mean GCC thickness and index of asymmetric) was developed in this study. By using this system, it is easier to distinguish normal and early glaucoma patients. However, the ability to classify between early glaucoma and advance glaucoma was not significant as expected.