Detecção e segmentação de estruturas em imagens médicas de retina

• Autor(a) principal: Veras, Rodrigo de Melo Souza
• Data de Publicação: 2014
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Repositório Institucional da Universidade Federal do Ceará (UFC)
• Texto Completo: http://www.repositorio.ufc.br/handle/riufc/10851
• topic: Teleinformática; Olho - Doenças - Tratamento

Fundus images are valuable resource in diagnosis because they often present indications about retinal, ophthalmic, and even systemic diseases such as diabetes, hypertension, and arteriosclerosis. This thesis focuses on algorithms to detect fovea, exudates and optic disk (OD) in retina images. Regarding fovea detection algorithms in colored retina images, we propose an algorithm and furthermore a set of rules to assess them. Automatic detection of this anatomical structure is a prerequisite for computer-aided diagnosis of several retinal diseases, such as macular degeneration. However, the small dimension and weak contrast of the fovea area on retina images make difficult this task detection, directly. The proposed algorithm determines a region of interest taking into account OD coordinates and the fact that the fovea is a homogeneous dark area without blood vessels. Then, the method performs the vessel segmentation step and searches for the lowest mean color intensity window in the image that results from the fusion between the red and green channels. Tests were carried out on three public benchmark databases. In addition, this thesis proposes an algorithm for exudate detection in retina images. The proposed methodology combines fuzzy clustering and mathematical morphology techniques. The results confirm the performance improvement provided by the proposed methodology, when comparing it to other methods available in the literature. In this work, we compare the results of six different automatic algorithms for OD detection, using the public benchmark image database named ARIA, STARE, DRIVE and MESSIDOR. We aimed to test the robustness of the algorithms in detecting the OD in healthy and pathological retina images. In general, we observed that these methods performed better in less challenging databases as the two last ones, i.e. they achieved the highest success rates in DRIVE and MESSIDOR.