Automatic Wide Field Registration and Mosaicking of OCTA Images Using Vascularity Information

[Abstract] Optical Coherence Tomography Angiography (OCTA) constitutes a novel ophthalmological image modality that is characterized for being a non-invasive capture technique that allows a profound analysis of the vascular characteristics of the eye fundus. Given the restricted field of view of the eye fundus that offers each scan, the specialists frequently capture several complementary images that may be simultaneously analyzed to offer a complete and accurate diagnosis of the patient. In this work, we propose a fully automatic method to register complementary OCTA images and provide compositions for the same patient, generating a wide field of representation that allows a simpler and more direct analysis than the traditional tedious manual procedures. To achieve this, we based our proposal in a robust combination of representative features that are filtered by an accurate identification of the main retinal vasculature. This way, given the characteristic high irregularity in the fundus of the OCTA images, we avoid many variable areas that may interfere in the registration process, restricting the analysis to the most representative and stable structure of this image modality, the main retinal vasculature. In particular, we use Speeded-Up Robust Features (SURF) algorithm to extract representative features in the main vascular region that is extracted using a method that combines the analysis of the Hessian matrix followed by an hysteresis threshold process. Then, using a K-NN model, we perform the registration of the resulting features from the different OCTA images to be analyzed. Finally, the Random sample consensus (RANSAC) method is exploited to produce the final target mosaic. The proposed method presented satisfactory results in the validation experiments, with accurate values for the MSE index of 1.2566 and 1.6725 pixels for the registration of paired images an mosaics, respectively.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2016-047This work is supported by the Instituto de Salud Carlos III, Government of Spain and FEDER funds of the European Union through the DTS18/00136 research projects and by the Ministerio de Economía y Competitividad, Government of Spain through the DPI2015-69948-R research project. Also, this work has received financial support from the European Union (European Regional Development Fund - ERDF) and the Xunta de Galicia, Centro singular de investigación de Galicia accreditation 2016-2019, Ref. ED431G/01; and Grupos de Referencia Competitiva, Ref. ED431C 2016-047

Methods for Medical Image Registration – a Laboratory Task

Bakalářská práce je zaměřena na zpracování reálných lékařských obrazových dat. První část práce se zabývá procesem pořízení a zpracování obrazu, metodami segmentace obrazu a především registrací obrazu, jejími metodami a aplikacemi. V práci je definován postup registrace a různá hlediska klasifikace registračních metod, základní typy transformací, interpolací a metrik podobnosti. Druhá část práce popisuje implementaci vybraných metod registrace na reálných sadách snímků. Cílem bakalářské práce je nalezení optimálních parametrů transformace. První implementací je registrace založená na rotaci, jejímž úkolem je zjištění optimálního úhlu otočení plovoucího snímku. Druhým výstupem práce je nalezení optimální kombinace vstupních parametrů pro registraci založenou na intenzitě. Třetím výstupem jsou laboratorní úlohy sloužící pro výukové účely. Laboratorní úlohy jsou založeny na úkolech v praktické části práce. Testování metod probíhalo v prostředí MATLAB R2018b společnosti MathWorks. Výsledky metod byly zpracovávány v excelu.The bachelor thesis is focused on the processing of real medical image data. The first part of the thesis deals with the process of image acquisition and processing, image segmentation methods and especially image registration, registration methods and applications. The registration process, various aspects of the classification of registration methods, basic types of transformations, interpolations and similarity metrics are defined in the thesis. The second part of the thesis describes the implementation of selected registration methods on real dataset. The aim of the bachelor thesis is to find the optimal parameters of the transformation. The first implementation is rotation based registration. The task of this type of registration is to determine the optimal rotation angle of the floating image. The second output of the thesis is to find the optimal combination of input parameters for registration based on intensity. The third output are laboratory tasks used for educational purposes. Laboratory tasks are based on tasks in the practical part of the work. Testing of the methods took place in the MATLAB R2018b by MathWorks. The method results were processed in Excel.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn