Visibility recovery on images acquired in attenuating media. Application to underwater, fog, and mammographic imaging

136 p.When acquired in attenuating media, digital images of ten suffer from a particularly complex degradation that reduces their visual quality, hindering their suitability for further computational applications, or simply decreasing the visual pleasan tness for the user. In these cases, mathematical image processing reveals it self as an ideal tool to recover some of the information lost during the degradation process. In this dissertation,we deal with three of such practical scenarios in which this problematic is specially relevant, namely, underwater image enhancement, fogremoval and mammographic image processing. In the case of digital mammograms,X-ray beams traverse human tissue, and electronic detectorscapture them as they reach the other side. However, the superposition on a bidimensional image of three-dimensional structures produces low contraste dimages in which structures of interest suffer from a diminished visibility, obstructing diagnosis tasks. Regarding fog removal, the loss of contrast is produced by the atmospheric conditions, and white colour takes over the scene uniformly as distance increases, also reducing visibility.For underwater images, there is an added difficulty, since colour is not lost uniformly; instead, red colours decay the fastest, and green and blue colours typically dominate the acquired images. To address all these challenges,in this dissertation we develop new methodologies that rely on: a)physical models of the observed degradation, and b) the calculus of variations.Equipped with this powerful machinery, we design novel theoreticaland computational tools, including image-dependent functional energies that capture the particularities of each degradation model. These energie sare composed of different integral terms that are simultaneous lyminimized by means of efficient numerical schemes, producing a clean,visually-pleasant and use ful output image, with better contrast and increased visibility. In every considered application, we provide comprehensive qualitative (visual) and quantitative experimental results to validateour methods, confirming that the developed techniques out perform other existing approaches in the literature

On the Duality Between Retinex and Image Dehazing

Image dehazing deals with the removal of undesired loss of visibility in outdoor images due to the presence of fog. Retinex is a color vision model mimicking the ability of the Human Visual System to robustly discount varying illuminations when observing a scene under different spectral lighting conditions. Retinex has been widely explored in the computer vision literature for image enhancement and other related tasks. While these two problems are apparently unrelated, the goal of this work is to show that they can be connected by a simple linear relationship. Specifically, most Retinex-based algorithms have the characteristic feature of always increasing image brightness, which turns them into ideal candidates for effective image dehazing by directly applying Retinex to a hazy image whose intensities have been inverted. In this paper, we give theoretical proof that Retinex on inverted intensities is a solution to the image dehazing problem. Comprehensive qualitative and quantitative results indicate that several classical and modern implementations of Retinex can be transformed into competing image dehazing algorithms performing on pair with more complex fog removal methods, and can overcome some of the main challenges associated with this problem

Retinal image quality assessment using deep convolutional neural networks

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Informática Médica)Diabetic Retinopathy (DR) and diabetic macular edema (DME) are the damages caused to the retina and are complications that can affect the diabetic population. Diabetic retinopathy (DR), is the most common disease due to the presence of exudates and has three levels of severity, such as mild, moderate and severe, depending on the exudates distribution in the retina. For screening of diabetic retinopathy or a population-based clinical study, a large number of digital fundus images are captured and to be possible to recognize the signs of DR and DME, it is necessary that the images have quality, because low-quality images may force the patient to return for a second examination, wasting time and possibly delaying treatment. These images are evaluated by trained human experts, which can be a time-consuming and expensive task due to the number of images that need to be examined. Therefore, this is a field that would be hugely benefited with the development of an automated eye fundus quality assessment and analysis systems. It can potentially facilitate health care in remote regions and in developing countries where reading skills are scarce. Deep Learning is a kind of Machine Learning method that involves learning multi-level representations that begin with raw data entry and gradually moves to more abstract levels through non-linear transformations. With enough training data and sufficiently deep architectures, neural networks, such as Convolutional Neural Networks (CNN), can learn very complex functions and discover complex structures in the data. Thus, Deep Learning emerges as a powerful tool for medical image analysis and evaluation of retinal image quality using computer-aided diagnosis. Therefore, the aim of this study is to automatically assess all the three quality parameters alone (focus, illumination and color), and then an overall quality of fundus images assessment, classifying the images into the classes “accept” or “reject with a Deep Learning approach using convolutional neural networks (CNN). For the overall classification, the following results were obtained: test accuracy=97.89%, SN=97.9%, AUC=0.98 and 1-score=97.91%.A retinopatia diabética (RD) e o edema macular diabético (EMD) são patologias da retina e são uma complicação que pode afetar a população diabética. A retinopatia diabética é a doença mais comum devido à presença de exsudatos e possui três níveis de gravidade, como leve, moderado e grave, dependendo da distribuição dos exsudatos na retina. Para triagem da retinopatia diabética ou estudo clínico de base populacional, um grande número de imagens digitais de fundo do olho são capturadas e para ser possível reconhecer os sinais da RD e EMD, é necessário que as imagens tenham qualidade, pois imagens de baixa qualidade podem forçar o paciente a retornar para um segundo exame, perdendo tempo e, possivelmente, retardando o tratamento. Essas imagens são avaliadas por especialistas humanos treinados, o que pode ser uma tarefa demorada e cara devido ao número de imagens que precisam de ser examinadas. Portanto, este é um campo que seria enormemente beneficiado com o desenvolvimento de sistemas automatizados de avaliação e análise da qualidade da imagem do fundo de olho. Pode potencialmente facilitar a assistência médica em regiões remotas e em países em desenvolvimento, onde as habilidades de leitura são escassas. Deep Learning é um tipo de método de Machine Learning que envolve a aprendizagem de representações em vários níveis que começam com a entrada de dados brutos e gradualmente se transformam para níveis mais abstratos através de transformações não lineares, para se obterem as previsões. Com dados de treino suficientes e arquiteturas suficientemente profundas, as redes neuronais, como as Convolutional Neural Networks (CNN), podem aprender funções muito complexas e descobrir estruturas complexas nos dados. Assim, o Deep Learning surge como uma ferramenta poderosa para analisar imagens médicas para avaliação da qualidade da retina, usando diagnóstico auxiliado por computador a partir do fundo do olho. Portanto, o objetivo deste estudo é avaliar automaticamente a qualidade geral das imagens do fundo, classificando as imagens em “aceites” ou “rejeitadas”, com base em três parâmetros principais, como o foco, a iluminação e cor com abordagem de Deep Learning usando convolutional neural networks (CNN). Para a classificação geral da qualidade das imagens, obtiveram-se os seguintes resultados: acurácia do teste = 97,89%, SN = 97,9%, AUC = 0,98 e 1-score=97.91%

Spectral imaging in preclinical research and clinical pathology.

Spectral imaging methods are attracting increased interest from researchers and practitioners in basic science, pre-clinical and clinical arenas. A combination of better labeling reagents and better optics creates opportunities to detect and measure multiple parameters at the molecular and cellular level. These tools can provide valuable insights into the basic mechanisms of life, and yield diagnostic and prognostic information for clinical applications. There are many multispectral technologies available, each with its own advantages and limitations. This chapter will present an overview of the rationale for spectral imaging, and discuss the hardware, software and sample labeling strategies that can optimize its usefulness in clinical settings

Color inference from semantic labeling for person search in videos

We propose an explainable model to generate semantic color labels for person search. In this context, persons are described from their semantic parts, such as hat, shirt, etc. Person search consists in looking for people based on these descriptions. In this work, we aim to improve the accuracy of color labels for people. Our goal is to handle the high variability of human perception. Existing solutions are based on hand-crafted features or learnt features that are not explainable. Moreover most of them only focus on a limited set of colors. We propose a method based on binary search trees and a large peer-labelled color name dataset. This allows us to synthesize the human perception of colors. Using semantic segmentation and our color labeling method, we label segments of pedestrians with their associated colors. We evaluate our solution on person search on datasets such as PCN, and show a precision as high as 80.4%.Comment: 8 pages, 7 figures ICIAR 202

Intraocular Lens

The first clinical application of intraocular lens (IOL) goes back to 1949 when Dr. Harold Ridley successfully implanted a PMMA IOL into an eye on 29 November 1949. This innovation is a big step forward for cataract surgery. With development of the IOL material and biocompatibility, more and more IOL types have been used in clinical ophthalmology. This book is the fruit of worldwide cooperation between clinical teams. In this book we discuss the IOL materials and design, aberration and astigmatism correction with IOL, entopic phenomenon of IOL, myopia and phakic IOL, and secondary IOL techniques. We believe that this content provides the readers with a comprehensive knowledge of the latest developments of IOL

Retina implant project : chronic implantation of active epiretinal implants

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