Research On Key Technology Of Fundus Image Processing And Analysis

Medical imaging and correlation technique widely used in medical field has been developed as theobjective basis of clinical diagnosis and treatment, medical image processing and analysis technologyas the key auxiliary diagnosis and treatment, which has important clinical and research value. Fundusimage obtained by fundus camera is a standard objective diagnostic imaging in ophthalmology, andthe fundus is located in internal ocular. Fundus oculi disease is the leading cause of blindness, whichcan cause vision loss. Therefore, fundus image processing and analysis is of great significance to earlydetection, diagnosis, treatment, auxiliary diagnosis and treatment of various kinds of retinal disease,such as diabetes, hypertension, maculopathy, arteriosclerosis, retinopathy, and so on.The paper focuson fundus image acquisition, image preprocessing, image registration, image fusion and mosaic,important target segmentation and measurement of fundus image, and the realization of fundus imageprocessing and analysis system. The main points are as follow:(1) According to the fundus camera imaging principle, establish the human eye optical model byGullstrand I model parameters, and establish fundus camera optical model by optical design, thenanalyses the existing aberration and distortion of human eye and fundus camera imaging opticalsystem, fundus image geometric distortion correction algorithm based on fundus camera calibrationand color fundus image radiation distortion correction algorithm based on RGB and HSI color spacesand homomorphic filter are proposed, which can effectively correct the geometric distortion (about20pixels) and radiation distortion of color fundus image, preserve color fundus image detailsinformation, and improve the precision of parameters measurement; According to the characteristicsof human eye optical model and fundus camera imaging optical system, proposes a color fundusimage restoration method based on blind deconvolution and Lucy Richardson, which can effectivelyimprove the color or gray fundus image resolution, increased by16.4%and15.8%respectively,improve the accuracy of subsequent processing and analysis. And the specific fundus imagepreprocessing methods are used to dealing with specific problems.(2) In the basis of the fundus camera imaging field limited, low contrast, illumination uneven andgeometric distortion, a semi-automatic image registration method based on SSDA is applied toregister low contrast fundus images; And propose an automatic fundus image registration algorithmbased on improved Harris corner detection to register better contrast fundus images; An automaticfundus image registration algorithm based on SIFT features is proposed, which improves thealgorithm parameters of feature detection, matching and purification, optimize the image registration model, can automatic register fundus images quickly with high precision, and the average registrationerror is no more than2.1pixels; An automatic fundus image registration algorithm based onSURF-128features is proposed, which improves the algorithm parameters of feature detection,matching and purification, optimize the image registration model, can automatic register fundusimages quickly with high precision, and the average registration error is no more than2.2pixels.(3) The spatial relationship and registration strategy in different fundus images fields is analysed.And it is derived and verified that the quadratic polynomial transformation is suitable for the funduspanoramic image registration. And color fundus image fusion method based on pixel nonlinear,Laplacian pyramid and wavelet (in RGB and Lab space) is applied to ensure the fundus panoramicimage quality. The effectiveness of the fundus image subjective and objective evaluation is verified,and improve the fundus image clarity evaluation function, determine the various parameters of theimage fusion method by comparison and analysis of the various color fundus image fusion method.An automatic fundus image panoramic mosaic algorithm based on the prior distribution knowledge ofoptic disk and blood vessels is proposed, and adding intermediate mosaic image steps, can automaticachieved high precision fundus panoramic image registration quickly.(4) In the basis of the characteristics of fundus image, analyzes the important target for retinalimage (blood vessels, optic disk and optic cup, et al.), a retinal vascular multi-scale segmentationalgorithm based on gaussian kernel function and Hessian matrix is proposed to quickly and effectivelydetect retinal vascular center line coordinates, line direction, boundary point coordinates, width andlength, and other information in fundus image. An automatic optic disk location algorithm based onlarge scale vessel segemention and optic disk segemention method based on edge (or area)segmentation and Hough transform circle fitting is proposed to segment and measure optic disk withhigh precision. Optic disk and optic cup segmentation method based on active contour is proposed,which can accuracy segment and measure optic disk and optic cup with image distortion correction.(5) To meet the requirements of fundus image processing and analysis system, a fast automaticfundus image registration and mosaic algorithm based on CUDA is proposed, and the algorithm speedupgrade10to30times. To meet the needs of clinical application, the fundus image processing andanalysis system is established, and realized fundus image acquisition, registration and fusion, mosaic,segmentation and parameter measurement of optic disk and vessels, and provide a powerful tool forretinal disease prevention, auxiliary diagnosis and treatment.