Image Superresolution Reconstruction via Granular Computing Clustering

The problem of generating a superresolution (SR) image from a single low-resolution (LR) input image is addressed via granular computing clustering in the paper. Firstly, and the training images are regarded as SR image and partitioned into some SR patches, which are resized into LS patches, the training set is composed of the SR patches and the corresponding LR patches. Secondly, the granular computing (GrC) clustering is proposed by the hypersphere representation of granule and the fuzzy inclusion measure compounded by the operation between two granules. Thirdly, the granule set (GS) including hypersphere granules with different granularities is induced by GrC and used to form the relation between the LR image and the SR image by lasso. Experimental results showed that GrC achieved the least root mean square errors between the reconstructed SR image and the original image compared with bicubic interpolation, sparse representation, and NNLasso

Imparting 3D representations to artificial intelligence for a full assessment of pressure injuries.

During recent decades, researches have shown great interest to machine learning techniques in order to extract meaningful information from the large amount of data being collected each day. Especially in the medical field, images play a significant role in the detection of several health issues. Hence, medical image analysis remarkably participates in the diagnosis process and it is considered a suitable environment to interact with the technology of intelligent systems. Deep Learning (DL) has recently captured the interest of researchers as it has proven to be efficient in detecting underlying features in the data and outperformed the classical machine learning methods. The main objective of this dissertation is to prove the efficiency of Deep Learning techniques in tackling one of the important health issues we are facing in our society, through medical imaging. Pressure injuries are a dermatology related health issue associated with increased morbidity and health care costs. Managing pressure injuries appropriately is increasingly important for all the professionals in wound care. Using 2D photographs and 3D meshes of these wounds, collected from collaborating hospitals, our mission is to create intelligent systems for a full non-intrusive assessment of these wounds. Five main tasks have been achieved in this study: a literature review of wound imaging methods using machine learning techniques, the classification and segmentation of the tissue types inside the pressure injury, the segmentation of these wounds and the design of an end-to-end system which measures all the necessary quantitative information from 3D meshes for an efficient assessment of PIs, and the integration of the assessment imaging techniques in a web-based application

Mobile Wound Assessment and 3D Modeling from a Single Image

The prevalence of camera-enabled mobile phones have made mobile wound assessment a viable treatment option for millions of previously difficult to reach patients. We have designed a complete mobile wound assessment platform to ameliorate the many challenges related to chronic wound care. Chronic wounds and infections are the most severe, costly and fatal types of wounds, placing them at the center of mobile wound assessment. Wound physicians assess thousands of single-view wound images from all over the world, and it may be difficult to determine the location of the wound on the body, for example, if the wound is taken at close range. In our solution, end-users capture an image of the wound by taking a picture with their mobile camera. The wound image is segmented and classified using modern convolution neural networks, and is stored securely in the cloud for remote tracking. We use an interactive semi-automated approach to allow users to specify the location of the wound on the body. To accomplish this we have created, to the best our knowledge, the first 3D human surface anatomy labeling system, based off the current NYU and Anatomy Mapper labeling systems. To interactively view wounds in 3D, we have presented an efficient projective texture mapping algorithm for texturing wounds onto a 3D human anatomy model. In so doing, we have demonstrated an approach to 3D wound reconstruction that works even for a single wound image

DETECTION OF GRANULATION TISSUE FOR HEALING ASSESSMENT OF CHRONIC ULCERS

Wounds that fail to heal within an expected period develop into ulcers that cause severe pain and expose patients to limb amputation. Ulcer appearance changes gradually as ulcer tissues evolve throughout the healing process. Dermatologists assess the progression of ulcer healing based on visual inspection of ulcer tissues, which is inconsistent and subjective. The ability to measure objectively early stages of ulcer healing is important to improve clinical decisions and enhance the effectiveness of the treatment. Ulcer healing is indicated by the growth of granulation tissue that contains pigment haemoglobin that causes the red colour of the tissue. An approach based on utilising haemoglobin content as an image marker to detect regions of granulation tissue on ulcers surface using colour images of chronic ulcers is investigated in this study. The approach is utilised to develop a system that is able to detect regions of granulation tissue on ulcers surface using colour images of chronic ulcers

Sistemas granulares evolutivos

Orientador: Fernando Antonio Campos GomideTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Recentemente tem-se observado um crescente interesse em abordagens de modelagem computacional para lidar com fluxos de dados do mundo real. Métodos e algoritmos têm sido propostos para obtenção de conhecimento a partir de conjuntos de dados muito grandes e, a princípio, sem valor aparente. Este trabalho apresenta uma plataforma computacional para modelagem granular evolutiva de fluxos de dados incertos. Sistemas granulares evolutivos abrangem uma variedade de abordagens para modelagem on-line inspiradas na forma com que os humanos lidam com a complexidade. Esses sistemas exploram o fluxo de informação em ambiente dinâmico e extrai disso modelos que podem ser linguisticamente entendidos. Particularmente, a granulação da informação é uma técnica natural para dispensar atenção a detalhes desnecessários e enfatizar transparência, interpretabilidade e escalabilidade de sistemas de informação. Dados incertos (granulares) surgem a partir de percepções ou descrições imprecisas do valor de uma variável. De maneira geral, vários fatores podem afetar a escolha da representação dos dados tal que o objeto representativo reflita o significado do conceito que ele está sendo usado para representar. Neste trabalho são considerados dados numéricos, intervalares e fuzzy; e modelos intervalares, fuzzy e neuro-fuzzy. A aprendizagem de sistemas granulares é baseada em algoritmos incrementais que constroem a estrutura do modelo sem conhecimento anterior sobre o processo e adapta os parâmetros do modelo sempre que necessário. Este paradigma de aprendizagem é particularmente importante uma vez que ele evita a reconstrução e o retreinamento do modelo quando o ambiente muda. Exemplos de aplicação em classificação, aproximação de função, predição de séries temporais e controle usando dados sintéticos e reais ilustram a utilidade das abordagens de modelagem granular propostas. O comportamento de fluxos de dados não-estacionários com mudanças graduais e abruptas de regime é também analisado dentro do paradigma de computação granular evolutiva. Realçamos o papel da computação intervalar, fuzzy e neuro-fuzzy em processar dados incertos e prover soluções aproximadas de alta qualidade e sumário de regras de conjuntos de dados de entrada e saída. As abordagens e o paradigma introduzidos constituem uma extensão natural de sistemas inteligentes evolutivos para processamento de dados numéricos a sistemas granulares evolutivos para processamento de dados granularesAbstract: In recent years there has been increasing interest in computational modeling approaches to deal with real-world data streams. Methods and algorithms have been proposed to uncover meaningful knowledge from very large (often unbounded) data sets in principle with no apparent value. This thesis introduces a framework for evolving granular modeling of uncertain data streams. Evolving granular systems comprise an array of online modeling approaches inspired by the way in which humans deal with complexity. These systems explore the information flow in dynamic environments and derive from it models that can be linguistically understood. Particularly, information granulation is a natural technique to dispense unnecessary details and emphasize transparency, interpretability and scalability of information systems. Uncertain (granular) data arise from imprecise perception or description of the value of a variable. Broadly stated, various factors can affect one's choice of data representation such that the representing object conveys the meaning of the concept it is being used to represent. Of particular concern to this work are numerical, interval, and fuzzy types of granular data; and interval, fuzzy, and neurofuzzy modeling frameworks. Learning in evolving granular systems is based on incremental algorithms that build model structure from scratch on a per-sample basis and adapt model parameters whenever necessary. This learning paradigm is meaningful once it avoids redesigning and retraining models all along if the system changes. Application examples in classification, function approximation, time-series prediction and control using real and synthetic data illustrate the usefulness of the granular approaches and framework proposed. The behavior of nonstationary data streams with gradual and abrupt regime shifts is also analyzed in the realm of evolving granular computing. We shed light upon the role of interval, fuzzy, and neurofuzzy computing in processing uncertain data and providing high-quality approximate solutions and rule summary of input-output data sets. The approaches and framework introduced constitute a natural extension of evolving intelligent systems over numeric data streams to evolving granular systems over granular data streamsDoutoradoAutomaçãoDoutor em Engenharia Elétric

EXPLOITING HIGHER ORDER UNCERTAINTY IN IMAGE ANALYSIS

Soft computing is a group of methodologies that works synergistically to provide flexible information processing capability for handling real-life ambiguous situations. Its aim is to exploit the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth in order to achieve tractability, robustness, and low-cost solutions. Soft computing methodologies (involving fuzzy sets, neural networks, genetic algorithms, and rough sets) have been successfully employed in various image processing tasks including image segmentation, enhancement and classification, both individually or in combination with other soft computing techniques. The reason of such success has its motivation in the fact that soft computing techniques provide a powerful tools to describe uncertainty, naturally embedded in images, which can be exploited in various image processing tasks. The main contribution of this thesis is to present tools for handling uncertainty by means of a rough-fuzzy framework for exploiting feature level uncertainty. The first contribution is the definition of a general framework based on the hybridization of rough and fuzzy sets, along with a new operator called RF-product, as an effective solution to some problems in image analysis. The second and third contributions are devoted to prove the effectiveness of the proposed framework, by presenting a compression method based on vector quantization and its compression capabilities and an HSV color image segmentation technique

Characterization and pattern recognition of color images of dermatological ulcers: a pilot study

We present color image processing methods for the char\-ac\-te\-ri\-za\-tion of images of dermatological lesions for the purpose of content-based image retrieval (CBIR) and computer-aided di\-ag\-no\-sis. The intended application is to segment the images and perform classification and analysis of the tissue composition of skin lesions or ulcers, in terms of granulation (red), fibrin (yel\-low), necrotic (black), callous (white), and mixed tissue composition. The images were analyzed and classified by an expert dermatologist following the red-yellow-black-white model. Automatic segmentation was performed by means of clustering using Gauss\-ian mixture modeling, and its performance was evaluated by deriving the Jaccard coefficient between the automatically and manually segmented images. Statistical texture features were derived from cooccurrence matrices of RGB, HSI, L$^*$a$^*$b$^*$, and L$^*$u$^*$v$^*$ color components. A retrieval engine was implemented using the k-nearest-neighbor classifier and the Euclidean, Man\-hat\-tan, and Chebyshev distance metrics. Classification was performed by means of a metaclassifier using logistic regression. The average Jaccard coefficient after the segmentation step between the automatically and manually segmented images was 0.560, with a standard deviation of 0.220. The performance in CBIR was mea\-sured in terms of precision of retrieval, with average values of up to 0.617 obtained with the Chebyshev distance. The metaclassifier yielded an average area under the receiver operating char\-ac\-ter\-is\-tic curve of 0.772

Risk prediction analysis for post-surgical complications in cardiothoracic surgery

Cardiothoracic surgery patients have the risk of developing surgical site infections (SSIs), which causes hospital readmissions, increases healthcare costs and may lead to mortality. The first 30 days after hospital discharge are crucial for preventing these kind of infections. As an alternative to a hospital-based diagnosis, an automatic digital monitoring system can help with the early detection of SSIs by analyzing daily images of patient’s wounds. However, analyzing a wound automatically is one of the biggest challenges in medical image analysis. The proposed system is integrated into a research project called CardioFollowAI, which developed a digital telemonitoring service to follow-up the recovery of cardiothoracic surgery patients. This present work aims to tackle the problem of SSIs by predicting the existence of worrying alterations in wound images taken by patients, with the help of machine learning and deep learning algorithms. The developed system is divided into a segmentation model which detects the wound region area and categorizes the wound type, and a classification model which predicts the occurrence of alterations in the wounds. The dataset consists of 1337 images with chest wounds (WC), drainage wounds (WD) and leg wounds (WL) from 34 cardiothoracic surgery patients. For segmenting the images, an architecture with a Mobilenet encoder and an Unet decoder was used to obtain the regions of interest (ROI) and attribute the wound class. The following model was divided into three sub-classifiers for each wound type, in order to improve the model’s performance. Color and textural features were extracted from the wound’s ROIs to feed one of the three machine learning classifiers (random Forest, support vector machine and K-nearest neighbors), that predict the final output. The segmentation model achieved a final mean IoU of 89.9%, a dice coefficient of 94.6% and a mean average precision of 90.1%, showing good results. As for the algorithms that performed classification, the WL classifier exhibited the best results with a 87.6% recall and 52.6% precision, while WC classifier achieved a 71.4% recall and 36.0% precision. The WD had the worst performance with a 68.4% recall and 33.2% precision. The obtained results demonstrate the feasibility of this solution, which can be a start for preventing SSIs through image analysis with artificial intelligence.Os pacientes submetidos a uma cirurgia cardiotorácica tem o risco de desenvolver infeções no local da ferida cirúrgica, o que pode consequentemente levar a readmissões hospitalares, ao aumento dos custos na saúde e à mortalidade. Os primeiros 30 dias após a alta hospitalar são cruciais na prevenção destas infecções. Assim, como alternativa ao diagnóstico no hospital, a utilização diária de um sistema digital e automático de monotorização em imagens de feridas cirúrgicas pode ajudar na precoce deteção destas infeções. No entanto, a análise automática de feridas é um dos grandes desafios em análise de imagens médicas. O sistema proposto integra um projeto de investigação designado CardioFollow.AI, que desenvolveu um serviço digital de telemonitorização para realizar o follow-up da recuperação dos pacientes de cirurgia cardiotorácica. Neste trabalho, o problema da infeção de feridas cirúrgicas é abordado, através da deteção de alterações preocupantes na ferida com ajuda de algoritmos de aprendizagem automática. O sistema desenvolvido divide-se num modelo de segmentação, que deteta a região da ferida e a categoriza consoante o seu tipo, e num modelo de classificação que prevê a existência de alterações na ferida. O conjunto de dados consistiu em 1337 imagens de feridas do peito (WC), feridas dos tubos de drenagem (WD) e feridas da perna (WL), provenientes de 34 pacientes de cirurgia cardiotorácica. A segmentação de imagem foi realizada através da combinação de Mobilenet como codificador e Unet como decodificador, de forma a obter-se as regiões de interesse e atribuir a classe da ferida. O modelo seguinte foi dividido em três subclassificadores para cada tipo de ferida, de forma a melhorar a performance do modelo. Caraterísticas de cor e textura foram extraídas da região da ferida para serem introduzidas num dos modelos de aprendizagem automática de forma a prever a classificação final (Random Forest, Support Vector Machine and K-Nearest Neighbors). O modelo de segmentação demonstrou bons resultados ao obter um IoU médio final de 89.9%, um dice de 94.6% e uma média de precisão de 90.1%. Relativamente aos algoritmos que realizaram a classificação, o classificador WL exibiu os melhores resultados com 87.6% de recall e 62.6% de precisão, enquanto o classificador das WC conseguiu um recall de 71.4% e 36.0% de precisão. Por fim, o classificador das WD teve a pior performance com um recall de 68.4% e 33.2% de precisão. Os resultados obtidos demonstram a viabilidade desta solução, que constitui o início da prevenção de infeções em feridas cirúrgica a partir da análise de imagem, com recurso a inteligência artificial