Detección Automática de Microaneurismas en Retinografías para Diagnóstico Precoz de Retinopatía Diabética

En este trabajo presentamos un prototipo de herramienta de detección automática de microaneurismas (MA) en retinografías en color. Este algoritmo evoluciona a partir de trabajos anteriores como la detección de microcalcificaciones en mamografías [1] o la detección de MA en angiografías fluoresceínicas (AF) [2][3]. El método para la detección automática de MA se divide en cinco partes: preprocesado de la retinografía, algoritmo de detección basado en la umbralización del error de predicción lineal en 2D, crecimiento de regiones, selección de características, y clasificación de los candidatos mediante una red neuronal del tipo Fuzzy ARTMAP. En total disponemos de 30 imágenes con 421 MA diagnosticados, de los cuales 101 se han utilizado para la clasificación. El algoritmo detecta correctamente 78 MA, presentando una sensibilidad del 77.23% y una media de 19.25 falsos positivos por imagen.Ministerio de Sanidad PI07/90379Ministerio de Sanidad PI07/9037

Automated microaneurysm detection algorithms applied to diabetic retinopathy retinal images

Diabetic retinopathy is the commonest cause of blindness in working age people. It is characterised and graded by the development of retinal microaneurysms, haemorrhages and exudates. The damage caused by diabetic retinopathy can be prevented if it is treated in its early stages. Therefore, automated early detection can limit the severity of the disease, improve the follow-up management of diabetic patients and assist ophthalmologists in investigating and treating the disease more efficiently. This review focuses on microaneurysm detection as the earliest clinically localised characteristic of diabetic retinopathy, a frequently observed complication in both Type 1 and Type 2 diabetes. Algorithms used for microaneurysm detection from retinal images are reviewed. A number of features used to extract microaneurysm are summarised. Furthermore, a comparative analysis of reported methods used to automatically detect microaneurysms is presented and discussed. The performance of methods and their complexity are also discussed

Identificação de estruturas retinianas para a deteção de retinopatia diabética

A Diabetes Mellitus constitui um grave problema mundial que está a crescer exponencialmente com o passar dos anos em todo o mundo, tanto nos países desenvolvidos como nos países em desenvolvimento. Esta patologia tem repercussões por todo o organismo sendo que a nível ocular a patologia mais preocupante é a Retinopatia Diabética, uma vez que pode levar à cegueira. Esta patologia só é detetada a partir de certos exames médicos e não apresenta sintomas até um que a doença esteja num estado muito avançado. A Retinopatia Diabética cursa entre vários estágios clínicos, que são definidos pelas diferentes características oftalmológicas encontradas. Os microaneurismas são as primeiras características a aparecer, seguidas de hemorragias e exsudatos. A progressão da doença leva a um estágio mais avançado no qual surge inícios de isquémia que culmina com o crescimento de novos vasos sanguíneos. O tratamento passa primariamente pela prevenção através de uma monitorização cuidadosa em pacientes com Diabetes, que devem fazer exames oftalmológicos pelo menos uma vez por ano. Em estados mais avançados tem que se recorrer a tratamentos por laser. Como estas técnicas não recuperam totalmente a visão a prevenção é muito importante e a automatização de processos de rastreio são um tema em crescimento e de importância elevada para a sociedade. Devido à importância deste assunto, decidi focar a minha tese de mestrado neste assunto. Sendo assim foi feita uma pesquisa bibliográfica de forma a tentar entender o problema em mãos e ainda a execução de um algoritmo capaz de ajudar a resolver este problema.Diabetes Mellitus is a serious global problem that is growing exponentially over the years all over the world, both in developed countries and in developing countries. This condition has effects throughout the body and on the eye level the most disturbing disease is diabetic retinopathy, since it can lead to blindness. This condition is detected only from certain medical tests and no symptomsare detected until the disease is in a very advanced state. The Diabetic Retinopathy has various clinical stages, which are defined by different ophthalmic features found in the retinal fundus images. The microaneurysms are the first features to appear, followed by hemorrhages and exudates. Progression of the disease leads to a more advanced stage in which arises ischemia culminating in the growth of new blood vessels. The treatment goes primarily by prevention through careful monitoring in patients with diabetes, who have their eyes examined at least once a year. In later stages it has to resort to laser treatments. As these techniques do not fully recover the prevention vision is very important the automation of screening processes. This is a subject of growing and highly importance to society. Because of the importance of this subject, I decided to focus my master's thesis on this subject. Thus it was made a literature search in order to try to understand the problem at hand and even the execution of an algorithm able to help solve this problem

Mathematical Morphological Processing for Retinal Image Analysis

Diabetic retinopathy is the leading cause of the blindness in the western world. Digital retinal imaging with remote image evaluations is a promising new solution to accurately and precisely stage patients conveniently. The spot lesion detection is the primary step. Based on the mathematical morphology, we discussed two lesion extraction algorithms. To avoid over-segmentation, inner and outer markers are introduced into the marker controlled watershed segmentation method. Gradient image is generated by multi-color channels. Marked lesions can be successfully extracted with clear boundaries. The second method, the adaptive multiscale morphological processing, is a novel procedure to efficiently extract spot lesions in the fundus image. The relative contrast of lesions with the surrounding background is used as criteria, which are similar to the human vision property. Entropy-based thresholding can well distinguish lesions. Post processing removes misclassified areas and produces vascular tree. Both algorithms have been tested in the Clemson University's database.School of Electrical & Computer Engineerin

The Role of Ecological Interactions in Polymicrobial Biofilms and their Contribution to Multiple Antibiotic Resistance

The primary objectives of this research were to demonstrate that: 1.) antibiotic resistant bacteria can promote the survival of antibiotic sensitive organisms when grown simultaneously as biofilms in antibiotics, 2.) community-level multiple antibiotic resistance of polymicrobial consortia can lead to biofilm formation despite the presence of multiple antibiotics, and 3.) biofilms may benefit plasmid retention and heterologous protein production in the absence of selective pressure. Quantitative analyses of confocal data showed that ampicillin resistant organisms supported populations of ampicillin sensitive organisms in steady state ampicillin concentrations 13 times greater than that which would inhibit sensitive cells inoculated alone. The rate of reaction of the resistance mechanism influenced the degree of protection. Spectinomycin resistant organisms did not support their sensitive counterparts, although flow cytometry indicated that GFP production by the sensitive strain was improved. When both organisms were grown in both antibiotics, larger numbers of substratum-attached pairs at 2 hours resulted in greater biofilm formation at 48 hours. For biofilms grown in both antibiotics, a benefit to spectinomycin resistant organism’s population size was detectable, but the only benefit to ampicillin resistant organisms was in terms of GFP production. Additionally, an initial attachment ratio of 5 spectinomycin resistant organisms to 1 ampicillin resistant organism resulted in optimal biofilm formation at 48 hours. Biofilms also enhanced the stability of high-copy number plasmids and heterologous protein production. In the absence of antibiotic selective pressure, plasmid DNA was not detected after 48 hours in chemostats, where the faster growth rate of plasmid-free cells contributed to the washout of plasmid retaining cells. The plasmid copy number per cell in biofilms grown without antibiotic selective pressure steadily increased over a six day period. Flow cytometric monitoring of bacteria grown in biofilms indicated that 95 percent of the population was producing GFP at 48 hours. This research supports the idea that ecological interactions between bacteria contribute to biofilm development in the presence of antibiotics, and demonstrates that community-level multiple antibiotic resistance is a factor in biofilm recalcitrance against antibiotics. Additionally, biofilms may provide an additional tool for stabilizing high copy number plasmids used for heterologous protein production

Modeling, Pattern Analysis and Feature-Based Retrieval on Retinal Images

Inexpensive high quality fundus camera systems enable imaging of retina for vision related health management and diagnosis at large scale. A computer based analysis system can help establish the general baseline of normal conditions vs. anomalous ones, so that different classes of retinal conditions can be classified. Advanced applications, ranging from disease screening algorithms, aging vs. disease trend modeling and prediction, and content-based retrieval systems can be developed. In this dissertation, I propose an analytical framework for the modeling of retina blood vessels to capture their statistical properties, so that based on these properties one can develop blood vessel mapping algorithms with self-optimized parameters. Then, other image objects can be registered based on vascular topology modeling techniques. On the basis of these low level analytical models and algorithms, the third major element of this dissertation is a high level population statistics application, in which texture classification of macular patterns is correlated with vessel structures, which can also be used for retinal image retrieval. The analytical models have been implemented and tested based on various image sources. Some of the algorithms have been used for clinical tests. The major contributions of this dissertation are summarized as follows: (1) A concise, accurate feature representation of retinal blood vessel on retinal images by proposing two feature descriptors Sp and Ep derived from radial contrast transform. (2) A new statistical model of lognormal distribution, which captures the underlying physical property of the levels of generations of the vascular network on retinal images. (3) Fast and accurate detection algorithms for retinal objects, which include retinal blood vessel, macular-fovea area and optic disc, and (4) A novel population statistics based modeling technique for correlation analysis of blood vessels and other image objects that only exhibit subtle texture changes