The identification of cellular automata

Although cellular automata have been widely studied as a class of the spatio temporal systems, very few investigators have studied how to identify the CA rules given observations of the patterns. A solution using a polynomial realization to describe the CA rule is reviewed in the present study based on the application of an orthogonal least squares algorithm. Three new neighbourhood detection methods are then reviewed as important preliminary analysis procedures to reduce the complexity of the estimation. The identification of excitable media is discussed using simulation examples and real data sets and a new method for the identification of hybrid CA is introduced

Combination of multiple image segmentations

Die Arbeit betrachtet die Kombination von mehreren Bildsegmentierungen im Bereich von contour detection und regionenbasierter Bildsegmentierung. Das Ziel ist die Kombination von mehreren Segmentierungen in eine verbesserte finale Segmentierung. Im Fall der regionenbasierten Kombination von Segmentierungen wird das generalized median Konzept verwendet, um automatisch die endgueltige Anzahl von Regionen zu bestimmen. Umfangreiche Experimente zeigen, dass die vorgeschlagene Kombinationsmethode bessere Ergebnisse erzielt als der Lernansatz unter Verwendung von Ground Truth Daten. Schliesslich untersuchen Experimente mit Evaluationsmassen fuer Segmentierungen das Verhalten sowie die Metrik-Eigenschaft der Masse. Die Studie soll als Leitlinie fuer die geeignete Wahl von Evaluationsmassen dienen. The thesis concerns combination of multiple image segmentations in the domains of contour detection and region-based image segmentation. The goal is to combine multiple segmentations into a final improved result. In the case of region-based image segmentation combination, a generalized median concept is proposed to automatically determine the final number of regions. Extensive experiments demonstrate that our combination method outperforms the ground truth based training approach. In addition, experimental investigation of existing segmentation evaluation measures on the metric property and the evaluating behaviors is presented. This study is intended to be as a guideline for appropriately choosing the evaluation measures

Ranking Median Regression: Learning to Order through Local Consensus

This article is devoted to the problem of predicting the value taken by a random permutation $\Sigma$, describing the preferences of an individual over a set of numbered items $\{1,\; \ldots,\; n\}$ say, based on the observation of an input/explanatory r.v. $X$ e.g. characteristics of the individual), when error is measured by the Kendall $\tau$ distance. In the probabilistic formulation of the 'Learning to Order' problem we propose, which extends the framework for statistical Kemeny ranking aggregation developped in \citet{CKS17}, this boils down to recovering conditional Kemeny medians of $\Sigma$ given $X$ from i.i.d. training examples $(X_1, \Sigma_1),\; \ldots,\; (X_N, \Sigma_N)$. For this reason, this statistical learning problem is referred to as \textit{ranking median regression} here. Our contribution is twofold. We first propose a probabilistic theory of ranking median regression: the set of optimal elements is characterized, the performance of empirical risk minimizers is investigated in this context and situations where fast learning rates can be achieved are also exhibited. Next we introduce the concept of local consensus/median, in order to derive efficient methods for ranking median regression. The major advantage of this local learning approach lies in its close connection with the widely studied Kemeny aggregation problem. From an algorithmic perspective, this permits to build predictive rules for ranking median regression by implementing efficient techniques for (approximate) Kemeny median computations at a local level in a tractable manner. In particular, versions of $k$-nearest neighbor and tree-based methods, tailored to ranking median regression, are investigated. Accuracy of piecewise constant ranking median regression rules is studied under a specific smoothness assumption for $\Sigma$'s conditional distribution given $X$

Endoscopic image analysis of aberrant crypt foci

Tese de Mestrado Integrado. Bioengenharia. Faculdade de Engenharia. Universidade do Porto. 201

Non-Linear Optimization Applied to Angle-of-Arrival Satellite Based Geo-Localization for Biased and Time Drifting Sensors

Multiple sensors are used in a variety of geolocation systems. Many use Time Difference of Arrival (TDOA) or Received Signal Strength (RSS) measurements to locate the most likely location of a signal. When an object does not emit a classical RF signal, Angle of Arrival (AOA) measurements become more feasible than TDOA or RSS measurements. AOA measurements can be created from any sensor platform with any sort of camera. When location and attitude knowledge of the sensor passive objects can be tracked. A Non-Linear Optimization (NLO) method for calculating the most likely estimate from AOA measurements has been created in previous work. This thesis, modifies that algorithm to automatically correct AOA measurement errors by estimating the inherent bias and timedrift in the Inertial Measurement Unit (IMU) of the AOA sensing platform. Two methods are created to correct the sensor bias. One method corrects the sensor bias in post processing while treating the previous NLO method as a module. The other method directly corrects the sensor bias within the NLO algorithm by incorporating the bias parameters as a state vector in the estimation process. These two methods are analyzed using various Monte-Carlo simulations to check the general performance of the two modifications in comparison to the original NLO algorithm. These methods appear to improve performance by 10 − 60% depending on the data

A learning-based CT prostate segmentation method via joint transductive feature selection and regression

In1 recent years, there has been a great interest in prostate segmentation, which is a important and challenging task for CT image guided radiotherapy. In this paper, a learning-based segmentation method via joint transductive feature selection and transductive regression is presented, which incorporates the physician’s simple manual specification (only taking a few seconds), to aid accurate segmentation, especially for the case with large irregular prostate motion. More specifically, for the current treatment image, experienced physician is first allowed to manually assign the labels for a small subset of prostate and non-prostate voxels, especially in the first and last slices of the prostate regions. Then, the proposed method follows the two step: in prostate-likelihood estimation step, two novel algorithms: tLasso and wLapRLS, will be sequentially employed for transductive feature selection and transductive regression, respectively, aiming to generate the prostate-likelihood map. In multi-atlases based label fusion step, the final segmentation result will be obtained according to the corresponding prostate-likelihood map and the previous images of the same patient. The proposed method has been substantially evaluated on a real prostate CT dataset including 24 patients with 330 CT images, and compared with several state-of-the-art methods. Experimental results show that the proposed method outperforms the state-of-the-arts in terms of higher Dice ratio, higher true positive fraction, and lower centroid distances. Also, the results demonstrate that simple manual specification can help improve the segmentation performance, which is clinically feasible in real practice

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

NOVA INFORMACIJSKA TEHNOLOGIJA PROCJENE KORISTI IZDVAJANJA CESTA POMOĆU SATELITSKIH SNIMKI VISOKE REZOLUCIJE TEMELJENE NA PCNN I C-V MODELU

Road extraction from high resolution satellite images has been an important research topic for analysis of urban areas. In this paper road extraction based on PCNN and Chan-Vese active contour model are compared. It is difficult and computationally expensive to extract roads from the original image due to presences of other road-like features with straight edges. The image is pre-processed using median filter to reduce the noise. Then road extraction is performed using PCNN and Chan-Vese active contour model. Nonlinear segments are removed using morphological operations. Finally the accuracy for the road extracted images is evaluated based on quality measures.Izdvajanje cesta pomoću satelitskih slika visoke rezolucije je važna istraživačka tema za analizu urbanih područja. U ovom radu ekstrakcije ceste se uspoređuju na PCNN i Chan-Vese aktivnom modelu. Teško je i računalno skupo izdvojiti ceste iz originalne slike zbog prisutnosti drugih elemenata ravnih rubova sličnih cestama. Slika je prethodno obrađena korištenjem filtera za smanjenje smetnji. Zatim se ekstrakcija ceste izvodi pomoću PCNN i Chan-Vese aktivnog modela konture. Nelinearni segmenti su uklonjeni primjenom morfoloških operacija. Konačno, točnost za ceste izdvojene iz slika se ocjenjuje na temelju kvalitativnih mjera

Fast parameter-free region growing segmentation with application to surgical planning

In this paper, we propose a self-assessed adaptive region growing segmentation algorithm. In the context of an experimental virtual-reality surgical planning software platform, our method successfully delineates main tissues relevant for reconstructive surgery, such as fat, muscle, and bone. We rely on a self-tuning approach to deal with a great variety of imaging conditions requiring limited user intervention (one seed). The detection of the optimal parameters is managed internally using a measure of the varying contrast of the growing region, and the stopping criterion is adapted to the noise level in the dataset thanks to the sampling strategy used for the assessment function. Sampling is referred to the statistics of a neighborhood around the seed(s), so that the sampling period becomes greater when images are noisier, resulting in the acquisition of a lower frequency version of the contrast function. Validation is provided for synthetic images, as well as real CT datasets. For the CT test images, validation is referred to manual delineations for 10 cases and to subjective assessment for another 35. High values of sensitivity and specificity, as well as Dice’s coefficient and Jaccard’s index on one hand, and satisfactory subjective evaluation on the other hand, prove the robustness of our contrast-based measure, even suggesting suitability for calibration of other region-based segmentation algorithms