Detecção de artefactos em imagens da retina

Dissertação para obtenção do Grau de Mestre em Engenharia BiomédicaA evolução da tecnologia na área da medicina tem permitido ao ser humano aumentar a qualidade e a esperança média de vida. A visão é um dos sentidos mais importantes, uma vez que nos dá a percepção visual do mundo que nos rodeia. Ao longo dos tempos têm sido diagnosticadas várias patologias associadas à retina, sendo por isso alvo de muito interesse científico. Nos últimos anos foram estudados vários algoritmos de detecção automática para permitir um rastreio mais uniforme e conciso das doenças retinianas. Apesar de alguns algoritmos já implementados apresentarem uma taxa de sucesso bastante elevada, os mesmos, apenas fazem um diagnóstico correcto em imagens de boa qualidade, isto é, sem nenhum artefacto na imagem. Os artefactos surgem naturalmente nas imagens da retina devido, por exemplo, ao paciente estar em contacto directo com a câmara que capta a imagem fazendo com que surjam alguns artefactos indesejáveis na imagem final. Nesta dissertação foi estudado um método de detecção automática de artefactos nas imagens do fundo ocular. Um dos primeiros entraves no processo de criação do algoritmo para a detecção foi o facto de as imagens apresentarem uma não uniformização da luminosidade, sendo por isso estudados alguns dos processos de equalização de iluminação. O método apresentado para a detecção de artefactos baseia-se na caracterização da forma e cor dos artefactos que surgem nas imagens da retina. Para tal, foi utilizado uma imagem padrão pré-definida que irá “procurar” em toda a imagem as zonas com maiores coincidências com a imagem padrão. Devido a esta estratégia não ser condição suficiente para encontrar os artefactos, foi criado um classificador com várias características particulares dos artefactos e de seguida dá-se um processo de validação, eliminando os falsos candidatos e validando os verdadeiros artefactos. O trabalho foi testado com um conjunto de 48 imagens recolhidas através de vários equipamentos diferentes e apresentou uma percentagem de sucesso de 92,6% para a detecção de artefactos

Efficient Processing of Corneal Confocal Microscopy Images. Development of a computer system for the pre-processing, feature extraction, classification, enhancement and registration of a sequence of corneal images.

Corneal diseases are one of the major causes of visual impairment and blindness worldwide. Used for diagnoses, a laser confocal microscope provides a sequence of images, at incremental depths, of the various corneal layers and structures. From these, ophthalmologists can extract clinical information on the state of health of a patient’s cornea. However, many factors impede ophthalmologists in forming diagnoses starting with the large number and variable quality of the individual images (blurring, non-uniform illumination within images, variable illumination between images and noise), and there are also difficulties posed for automatic processing caused by eye movements in both lateral and axial directions during the scanning process. Aiding ophthalmologists working with long sequences of corneal image requires the development of new algorithms which enhance, correctly order and register the corneal images within a sequence. The novel algorithms devised for this purpose and presented in this thesis are divided into four main categories. The first is enhancement to reduce the problems within individual images. The second is automatic image classification to identify which part of the cornea each image belongs to, when they may not be in the correct sequence. The third is automatic reordering of the images to place the images in the right sequence. The fourth is automatic registration of the images with each other. A flexible application called CORNEASYS has been developed and implemented using MATLAB and the C language to provide and run all the algorithms and methods presented in this thesis. CORNEASYS offers users a collection of all the proposed approaches and algorithms in this thesis in one platform package. CORNEASYS also provides a facility to help the research team and Ophthalmologists, who are in discussions to determine future system requirements which meet clinicians’ needs.The data and image files accompanying this thesis are not available online

Fully automated computer system for diagnosis of corneal diseases. Development of image processing technologies for the diagnosis of Acanthamoeba and Fusarium diseases in confocal microscopy images

Confocal microscopy demonstrated its value in the diagnosis of Acanthamoeba and fungal keratitis which considered sight-threatening corneal diseases. However, it can be difficult to find and train confocal microscopy graders to accurately detect Acanthamoeba cysts and fungal filaments in the images. Use of an automated system could overcome this problem and help to start the correct treatment more quickly. Also, response to treatment can be difficult to assess in infectious keratitis using clinical examination alone, but there is evidence that the morphology of filaments and cysts may change over time with the use of correct treatment. An automated system to analyse confocal microscopy images for such changes would also assist clinicians in determining whether the ulcer is improving, or whether a change of treatment is needed. This research proposes a fully automated novel system with GUI to detect cysts and hyphae (filaments) and measure useful quantitative parameters for them through many stages; Image enhancement, image segmentation, quantitative analysis for detected cysts and hyphae, and registration and tracking of ordered sequence of images. The performance of the proposed segmentation procedure is evaluated by comparing between the manual and the automated traced images of the dataset that was provided by the Manchester Royal Eye Hospital. The positive predictive values rate of cysts for Acanthamoeba images was 76%. For detected hyphae in Fusarium images, many standard measurements were computed. The accuracy of their values was quantified by calculating the percent error rate for each measurement and which ranged from 23% to 49%

Automatic Identification Of Medical Structures

A software tool for automatic identification in medical images should allow the identification of anatomical structures ^ and the presence of abnormalities in these structures, such as malformations and tumors. The automation of these tasks would help to decrease the time required for decision making in routine diagnosis and surgical planning. We have addressed the problem of identification of medical structures using a multiscale approach, the scale space, combined with a matching procedure that uses a priori information. The method can be divided in three steps: 1) construction of the linear scale space; 2) application of a feature detector that leads to a multiscale representation based on them; and 3) matching the elements present in the structure built in step 2 with a known pattern that describes the structure under study. We have built an application that uses geometrical information on the desired feature and its relations with other features present in the scene. Results have shown the method's ability to identify medical structures at several levels of resolution and noise. The method allows the generation of specific patterns to be matched by the target-structure with different diseases from a medical database. 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University of UtrechtThe Netherland