Retinal Image Fusion and Registration

1. DWT Fusion rules = orthogonalisation HRT IDWT red green blue • 2. Image Fusion • via discrete wavelet decomposition (DWT) • fusion decision map – orthogonalisation by computation of maximal direction of linear operator L(I) (e.g. gradient of the vector-valued image I) from the norm of the first differential of L(I): • Final orthogonalised value of the linear operator (given wavelet), l – highest eigenvalue of matrix G, q – its relevant eigenvector: Introduction HRT (Heildelberg Retina Thomograph) is widely used for imaging and following examination of the shape of the optic nerve head (ONH) and therefore for diagnosis glaucoma. For this purpose, correct segmentation of ONH is necessary but when manually performed also highly subjective and time consuming task. Unfortunately, current automatic segmentation algorithms are not sufficiently robust especially because the information of the ONH contour is sometimes missing in HRT images. Therefore, we hope that fusion of HRT image and colour fundus photograph will provide additional useable information. Here, the algorithms of image registration and fusion are described and results of ONH segmentation in fused images are presented. Retinal Image Fusion and Registration • 1. Image Registration • Type: 2-dimensional, bi-modal, multi-resolutional, performed by optimization of global similarity metrics. • Spatial transformation model: affine, perspective. • Criterion: mutual information. • Optimizers: controlled random search (CRS), Powell. • Interpolation: nearest neighbour, partial volume. • Registration results Fusion schema Registration schema Libor Kubecka*, Jiri Jan* * Department of Biomedical Engineering, FEEC, Brno University of Technology, CzechRepublic 3. Segmentation For the purpose of segmentation, we modified Chrastek’s method for the case of fused image.This method consists of morphological operations for image preprocessing, Hough transform for circle fitting and anchored active contour model for the final fine segmentation of the optic nerve head. The qualitative evaluation of this step should be performed in the future. 4. Results We have successfully designed a registration method making use of robust optimization (controlled random search) of modified mutual information similarity criterion. Quality of the registration step has been evaluated by human observer and reaches 98% of successfully registered images. Further we applied a method for fusion of registered images based on wavelet transform and computation of maximal length of gradient of vector image. Finally, successful results of segmentation of optic disc contour has been presented. HRT with overlaid edges from CFP Mosaic (HRT, CFP) Acknowledgements Authors sincerely acknowledge the contribution of Prof. G. Michelson, Augen-Klinik Erlangen (Germany) who provided Kowa fundus camera images, data from Heidelberg Retina Tomograph and valuable consultations. Also Radim Chrastek’s contribution in optic disc segmentation phase is highly acknowledged. This project has been completed with support by the grant FRVŠ 3118/2005 (Ministry of Education, Czech republic) and also by the support of the research centre DAR (Ministry of Education, Czech Republic), proj. no. 1M6798555601. EMBEC 2005, Prague, CZECH REPUBLIC 20.-25.11.2005