Diabetic retinopathy diagnosis through multi-agent approaches

Programa Doutoral em Engenharia BiomédicaDiabetic retinopathy has been revealed as a serious public health problem in occidental world, since it is the most common cause of vision impairment among people of working age. The early diagnosis and an adequate treatment can prevent loss of vision. Thus, a regular screening program to detect diabetic retinopathy in the early stages could be efficient for the prevention of blindness. Due to its characteristics, digital color fundus photographs have been the preferred eye examination method adopted in these programs. Nevertheless, due to the growing incidence of diabetes in population, ophthalmologists have to observe a huge number of images. Therefore, the development of computational tools that can assist the diagnosis is of major importance. Several works have been published in the recent past years for this purpose; but an automatic system for clinical practice has yet to come. In general, these algorithms are used to normalize, segment and extract information from images to be utilized by classifiers which aim to classify the regions of the fundus image. These methods are mostly based on global approaches that cannot be locally adapted to the image properties and therefore, none of them perform as needed because of fundus images complexity. This thesis focuses on the development of new tools based on multi-agent approaches, to assist the diabetic retinopathy early diagnosis. The fundus image automatic segmentation concerning the diabetic retinopathy diagnosis should comprise both pathological (dark and bright lesions) and anatomical features (optic disc, blood vessels and fovea). In that way, systems for the optic disc detection, bright lesions segmentation, blood vessels segmentation and dark lesions segmentation were implemented and, when possible, compared to those approaches already described in literature. Two kinds of agent based systems were investigated and applied to digital color fundus photographs: ant colony system and multi-agent system composed of reactive agents with interaction mechanisms between them. The ant colony system was used to the optic disc detection and for bright lesion segmentation. Multi-agent system models were developed for the blood vessel segmentation and for small dark lesion segmentation. The multi-agent system models created in this study are not image processing techniques on their own, but they are used as tools to improve the traditional algorithms results at the micro level. The results of all the proposed approaches are very promising and reveal that the systems created perform better than other recent methods described in the literature. Therefore, the main scientific contribution of this thesis is to prove that multi-agent systems based approaches can be efficient in segmenting structures in retinal images. Such an approach overcomes the classic image processing algorithms that are limited to macro results and do not consider the local characteristics of images. Hence, multi-agent systems based approaches could be a fundamental tool, responsible for a very efficient system development to be used in screening programs concerning diabetic retinopathy early diagnosis.A retinopatia diabética tem-se revelado como um problema sério de saúde pública no mundo ocidental, uma vez que é a principal causa de cegueira entre as pessoas em idade ativa. Contudo, a perda de visão pode ser prevenida através da deteção precoce da doença e de um tratamento adequado. Por isso, um programa regular de rastreio e monitorização da retinopatia diabética pode ser eficiente na prevenção da deterioração da visão. Devido às suas características, a fotografia digital colorida do fundo do olho tem sido o exame adotado neste tipo de programas. No entanto, devido ao aumento da incidência da diabetes na população, o número de imagens a serem analisadas pelos oftalmologistas é elevado. Assim sendo, é muito importante o desenvolvimento de ferramentas computacionais para auxiliar no diagnóstico desta patologia. Nos últimos anos, têm sido vários os trabalhos publicados com este propósito; porém, não existe ainda um sistema automático (ou recomendável) para ser usado nas práticas clínicas. No geral, estes algoritmos são usados para normalizar, segmentar e extrair informação das imagens que vai ser utilizada por classificadores, cujo objetivo é identificar as regiões da imagem que se procuram. Estes métodos são maioritariamente baseados em abordagens globais que não podem ser localmente adaptadas às propriedades das imagens e, portanto, nenhum apresenta a performance necessária devido à complexidade das imagens do fundo do olho. Esta tese foca-se no desenvolvimento de novas ferramentas computacionais baseadas em sistemas multi-agente, para auxiliar na deteção precoce da retinopatia diabética. A segmentação automática das imagens do fundo do olho com o objetivo de diagnosticar a retinopatia diabética, deve englobar características patológicas (lesões claras e escuras) e anatómicas (disco ótico, vasos sanguíneos e fóvea). Deste modo, foram criados sistemas para a deteção do disco ótico e para a segmentação das lesões claras, dos vasos sanguíneos e das lesões escuras e, quando possível, estes foram comparados com abordagens já descritas na literatura. Dois tipos de sistemas baseados em agentes foram investigados e aplicados nas imagens digitais coloridas do fundo do olho: sistema de colónia de formigas e sistema multi-agente constituído por agentes reativos e com mecanismos de interação entre eles. O sistema de colónia de formigas foi usado para a deteção do disco ótico e para a segmentação das lesões claras. Modelos de sistemas multi-agente foram desenvolvidos para a segmentação dos vasos sanguíneos e das lesões escuras. Os modelos multi-agentes criados ao longo deste estudo não são por si só técnicas de processamento de imagem, mas são sim usados como ferramentas para melhorar os resultados dos algoritmos tradicionais no baixo nível. Os resultados de todas as abordagens propostas são muito promissores e revelam que os sistemas criados apresentam melhor performance que outras abordagens recentes descritas na literatura. Posto isto, a maior contribuição científica desta tese é provar que abordagens baseadas em sistemas multi-agente podem ser eficientes na segmentação de estruturas em imagens da retina. Uma abordagem deste tipo ultrapassa os algoritmos clássicos de processamento de imagem, que se limitam aos resultados de alto nível e não têm em consideração as propriedades locais das imagens. Portanto, as abordagens baseadas em sistemas multi-agente podem ser uma ferramenta fundamental, responsável pelo desenvolvimento de um sistema eficiente para ser usado nos programas de rastreio e monitorização da retinopatia diabética.Work supported by FEDER funds through the "Programa Operacional Factores de Competitividade – COMPETE" and by national funds by FCT- Fundação para a Ciência e a Tecnologia. C. Pereira thanks the FCT for the SFRH / BD / 61829 / 2009 grant

NON-INVASIVE IMAGE ENHANCEMENT OF COLOUR RETINAL FUNDUS IMAGES FOR A COMPUTERISED DIABETIC RETINOPATHY MONITORING AND GRADING SYSTEM

Diabetic Retinopathy (DR) is a sight threatening complication due to diabetes mellitus affecting the retina. The pathologies of DR can be monitored by analysing colour fundus images. However, the low and varied contrast between retinal vessels and the background in colour fundus images remains an impediment to visual analysis in particular in analysing tiny retinal vessels and capillary networks. To circumvent this problem, fundus fluorescein angiography (FF A) that improves the image contrast is used. Unfortunately, it is an invasive procedure (injection of contrast dyes) that leads to other physiological problems and in the worst case may cause death. The objective of this research is to develop a non-invasive digital Image enhancement scheme that can overcome the problem of the varied and low contrast colour fundus images in order that the contrast produced is comparable to the invasive fluorescein method, and without introducing noise or artefacts. The developed image enhancement algorithm (called RETICA) is incorporated into a newly developed computerised DR system (called RETINO) that is capable to monitor and grade DR severity using colour fundus images. RETINO grades DR severity into five stages, namely No DR, Mild Non Proliferative DR (NPDR), Moderate NPDR, Severe NPDR and Proliferative DR (PDR) by enhancing the quality of digital colour fundus image using RETICA in the macular region and analysing the enlargement of the foveal avascular zone (F AZ), a region devoid of retinal vessels in the macular region. The importance of this research is to improve image quality in order to increase the accuracy, sensitivity and specificity of DR diagnosis, and to enable DR grading through either direct observation or computer assisted diagnosis system

Retinal drug delivery: rethinking outcomes for the efficient replication of retinal behavior

The retina is a highly organized structure that is considered to be "an approachable part of the brain." It is attracting the interest of development scientists, as it provides a model neurovascular system. Over the last few years, we have been witnessing significant development in the knowledge of the mechanisms that induce the shape of the retinal vascular system, as well as knowledge of disease processes that lead to retina degeneration. Knowledge and understanding of how our vision works are crucial to creating a hardware-adaptive computational model that can replicate retinal behavior. The neuronal system is nonlinear and very intricate. It is thus instrumental to have a clear view of the neurophysiological and neuroanatomic processes and to take into account the underlying principles that govern the process of hardware transformation to produce an appropriate model that can be mapped to a physical device. The mechanistic and integrated computational models have enormous potential toward helping to understand disease mechanisms and to explain the associations identified in large model-free data sets. The approach used is modulated and based on different models of drug administration, including the geometry of the eye. This work aimed to review the recently used mathematical models to map a directed retinal network.The authors acknowledge the financial support received from the Portuguese Science and Technology Foundation (FCT/MCT) and the European Funds (PRODER/COMPETE) for the project UIDB/04469/2020 (strategic fund), co-financed by FEDER, under the Partnership Agreement PT2020. The authors also acknowledge FAPESP – São Paulo Research Foundation, for the financial support for the publication of the article.info:eu-repo/semantics/publishedVersio

Automated Retinal Lesion Detection via Image Saliency Analysis

Background and objective:The detection of abnormalities such as lesions or leakage from retinal images is an important health informatics task for automated early diagnosis of diabetic and malarial retinopathy or other eye diseases, in order to prevent blindness and common systematic conditions. In this work, we propose a novel retinal lesion detection method by adapting the concepts of saliency. Methods :Retinal images are firstly segmented as superpixels, two new saliency feature representations: uniqueness and compactness, are then derived to represent the superpixels. The pixel level saliency is then estimated from these superpixel saliency values via a bilateral filter. These extracted saliency features form a matrix for low-rank analysis to achieve saliency detection. The precise contour of a lesion is finally extracted from the generated saliency map after removing confounding structures such as blood vessels, the optic disc, and the fovea. The main novelty of this method is that it is an effective tool for detecting different abnormalities at pixel-level from different modalities of retinal images, without the need to tune parameters. Results:To evaluate its effectiveness, we have applied our method to seven public datasets of diabetic and malarial retinopathy with four different types of lesions: exudate, hemorrhage, microaneurysms, and leakage. The evaluation was undertaken at pixel-level, lesion-level, or image-level according to ground truth availability in these datasets. Conclusions:The experimental results show that the proposed method outperforms existing state-of-the-art ones in applicability, effectiveness, and accuracy

NON-INVASIVE IMAGE DENOISING AND CONTRAST ENHANCEMENT TECHNIQUES FOR RETINAL FUNDUS IMAGES

The analysis of retinal vasculature in digital fundus images is important for diagnosing eye related diseases. However, digital colour fundus images suffer from low and varied contrast, and are also affected by noise, requiring the use of fundus angiogram modality. The Fundus Fluorescein Angiogram (FFA) modality gives 5 to 6 time’s higher contrast. However, FFA is an invasive method that requires contrast agents to be injected and this can lead other physiological problems. A reported digital image enhancement technique named RETICA that combines Retinex and ICA (Independent Component Analysis) techniques, reduces varied contrast, and enhances the low contrast blood vessels of model fundus images

Extraction of Blood Vessels Geometric Shape Features with Catheter Localization and Geodesic Distance Transform for Right Coronary Artery Detection.

X-ray angiography is considered the standard imaging sensory system for diagnosing coronary artery diseases. For automated, accurate diagnosis of such diseases, coronary vessels’ detection from the captured low quality and noisy angiography images is challenging. It is essential to detect the main branch of the coronary artery, to resolve such limitations along with the problems due to the sudden changes in the lumen diameter, and the abrupt changes in local artery direction. Accordingly, this paper solved these limitations by proposing a computer-aided detection system for the right coronary artery (RCA) extraction, where geometric shape features with catheter localization and geodesic distance transform in the angiography images through two parts. In part 1, the captured image was initially preprocessed for contrast enhancement using singular value decomposition-based contrast adjustment, followed by generating the vesselness map using Jerman filter, and for further segmentation the K-means was introduced. Afterward, in part 2, the geometric shape features of the RCA, as well as the skeleton gradient transform, and the start/end points were determined to extract the main blood vessel of the RCA. The analysis of the skeletonize image was performed using Geodesic distance transform to examine all branches starting from the predetermined start point and cover the branching till the predefined end points. A ranking matrix, and the inverse of skeletonization were finally carried out to get the actual main branch. The performance of the proposed system was then evaluated using different evaluation metrics on the angiography images...

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise