Study Of Motion Artifacts Correction Algorithm In Optical Coherence Tomography Images Based On C-Scan Of Optic Disc

Optical coherence tomography (OCT) is a novel lossless optical image technology, which can be utilized to realize 2D imaging and 3D visualization of morphologies and structures in tissues. OCT has the advantages of high sensitivity and high resolution, and it can be used to implement non-contacting real-time vivo imaging. Besides, the transverse resolution and the longitudinal resolution of OCT are independent. OCT makes enormous contributions to ophthalmology and has become a standard instrument in early diagnosis of eye diseases for its ability to obtain images of retina sections that traditional ophthalmic noninvasive diagnosis cannot acquire.During the dynamic 3D imaging implemented by OCT system, the gathered volume data will generate dislocations and distortions because of the involuntary motion of eyeball, which results in unreality of volume dataset and misdiagnosis in clinical diagnosis. To solve this problem, methods aiming to correct distortions in X and Z directions have been proposed, in which cross-correlation algorithm is used to align every adjacent two images. However, the existing correction methods perform well in tissues like macular region where vascularity is evenly distributed while they are incapable to areas with complicated vessels. And on account of its complex physiological structures and abundant vessels, the optic disc images cannot be corrected appropriately by conventional image rectification methods.In this paper, a correction algorithm aiming at dislocations, repetitions and deficiencies of volume data is proposed to rectify motion artifacts of OCT system. The correction algorithm directs against characteristics of optic disc OCT images. During the correcting course, linear correlation matching algorithm, which can be used to correct motion artifacts of X direction and seek out faults of image sequences, is firstly utilized to process pixel row data of C-scan images. Then, row correlation matching algorithm will be used to ascertain the number and locations of repeated data, if any. And if there are missing data, a reference Y direction scan image is needed to estimate the number and locations. Model validations indicate that the proposed correction algorithm can effectively restore the gathered image sequences and present the real physiologic structures of retina.In summary, the proposed correction algorithm realizes good effects in correcting image data and the corrected images reflect the real physiologic structures of retina. With this correction algorithm, accuracy and scientificity of clinical diagnosis will be enhanced, which leads to effective reduction of misdiagnosis.