Evaluation of fractal dimension effectiveness for damage detection in retinal background

[EN] This work investigates the characterization of bright lesions in retinal fundus images using texture analysis techniques. Exudates and drusen are evidences of retinal damage in diabetic retinopathy (DR) and age-related macular degeneration (AMD) respectively. An automatic detection of pathological tissues could make possible an early detection of these diseases. In this work, fractal analysis is explored in order to discriminate between pathological and healthy retinal texture. After a deep preprocessing step, in which spatial and colour normalization are performed, the fractal dimension is extracted locally by computing the Hurst exponent (H) along different directions. The greyscale image is described by the increments of the fractional Brownian motion model and the H parameter is computed by linear regression in the frequency domain. The ability of fractal dimension to detect pathological tissues is demonstrated using a home-made system, based on fractal analysis and Support Vector Machine, able to achieve around a 70% and 83% of accuracy in E-OPHTHA and DIARETDB1 public databases respectively. In a second experiment, the fractal descriptor is combined with texture information, extracted by the Local Binary Patterns, improving the bright lesion detection. Accuracy, sensitivity and specificity values higher than 89%, 80% and 90% respectively suggest that the method presented in this paper is a robust algorithm for describing retina texture and can be useful in the automatic detection of DR and AMD.This paper was supported by the European Union's Horizon 2020 research and innovation programme under the Project GALAHAD [H2020-ICT-2016-2017, 732613]. In addition, this work was partially funded by the Ministerio de Economia y Competitividad of Spain, Project SICAP [DPI2016-77869-C2-1-R]. The work of Adrian Colomer has been supported by the Spanish Government under a FPI Grant [BES-2014-067889]. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.Colomer, A.; Naranjo Ornedo, V.; Janvier, T.; Mossi García, JM. (2018). Evaluation of fractal dimension effectiveness for damage detection in retinal background. Journal of Computational and Applied Mathematics. 337:341-353. https://doi.org/10.1016/j.cam.2018.01.005S34135333

A Robust Face Recognition System Based on Curvelet and Fractal Dimension Transforms

yesn this paper, a powerful face recognition system for authentication and identification tasks is presented and a new facial feature extraction approach is proposed. A novel feature extraction method based on combining the characteristics of the Curvelet transform and Fractal dimension transform is proposed. The proposed system consists of four stages. Firstly, a simple preprocessing algorithm based on a sigmoid function is applied to standardize the intensity dynamic range in the input image. Secondly, a face detection stage based on the Viola-Jones algorithm is used for detecting the face region in the input image. After that, the feature extraction stage using a combination of the Digital Curvelet via wrapping transform and a Fractal Dimension transform is implemented. Finally, the K-Nearest Neighbor (K-NN) and Correlation Coefficient (CC) Classifiers are used in the recognition task. Lastly, the performance of the proposed approach has been tested by carrying out a number of experiments on three well-known datasets with high diversity in the facial expressions: SDUMLA-HMT, Faces96 and UMIST datasets. All the experiments conducted indicate the robustness and the effectiveness of the proposed approach for both authentication and identification tasks compared to other established approaches

Topics on Multiresolution Signal Processing and Bayesian Modeling with Applications in Bioinformatics

Analysis of multi-resolution signals and time-series data has wide applications in biology, medicine, engineering, etc. In many cases, the large-scale (low-frequency) features of a signal including basic descriptive statistics, trends, smoothed functional estimates, do not carry useful information about the phenomenon of interest. On the other hand, the study of small-scale (high-frequency) features that look like noise may be more informative even though extracting such informative features are not always straightforward. In this dissertation we try to address some of the issues pertaining to high-frequency features extraction and denoising of noisy signals. Another topic studied in this dissertation is focused on the integration of genome data with transatlantic voyage data of enslaved people from Africa to determine the ancestry origin of Afro-Americans. Chapter 2. Assessment of Scaling by Auto-Correlation Shells. In this chapter, we utilize the Auto-Correlation (AC) Shell to propose a feature extraction method that can effectively capture small-scale information of a signal. The AC Shell is a redundant shift-invariant and symmetric representation of the signal that is obtained by using Auto-Correlation function of compactly supported wavelets. The small-scale features are extracted by computing the energy of AC Shell coefficients at different levels of decomposition as well as the slope of the line fitted to these energy values using AC Shell spectra. We discuss the theoretical properties and verify them using extensive simulations. We compare the extracted features from AC Shell with those of Wavelets in terms of bias, variance, and mean square error (MSE). The results indicate that the AC Shell features tend to have smaller variance, hence more reliable. Moreover, to show its effectiveness, we validate our feature extraction method in the context of classification to identify patients with ovarian cancer through the analysis of their blood mass spectrum. For this study, we use the features extracted by AC Shell spectra along with a support vector machine classifier to distinguish control from cancer cases. Chapter 3. Bayesian Binary Regressions in Wavelet-based Function Estimation. Wavelet shrinkage has been widely used in nonparametric statistics and signal processing for a variety of purposes including denoising noisy signals and images, dimension reduction, and variable/feature selection. Although the traditional wavelet shrinkage methods are effective and popular, they have one major drawback. In these methods the shrinkage process only relies on the information of the coefficient being thresholded and the information contained in the neighboring coefficients is ignored. Similarly, the standard AC Shell denoising methods shrink the empirical coefficients independently, by comparing their magnitudes with a threshold value. The information of other coefficients has no influence on behavior of a particular coefficients. However, due to redundant representation of signals and coefficients obtained by AC Shells, the dependency of neighboring coefficients and the amount of shared information between them increases. Therefore, it would be vital to propose a new thresholding approach for AC Shells coefficients that considers the information of neighboring coefficients. In this chapter, we develop a new Bayesian denoising for AC Shell coefficients approach that integrates logistic regression, universal thresholding and Bayesian inference. We validate the proposed method using extensive simulations with various types of smooth and non-smooth signals. The results indicate that for all signal types including the neighbor coefficients would improve the denoising process, resulting in lower MSEs. Moreover, we applied our proposed methodology to a case study of denoising Atomic Force Microscopy (AFM) signals measuring the adhesion strength between two materials at the nano-newton scale to correctly identify the cantilever detachment point. Chapter 4. Bayesian Method in Combining Genetic and Historical Records of Transatlantic Slave Trade in the Americas. In the era between 1515 and 1865, more than 12 million people were enslaved and forced to move from Africa to North and Latin America. The shipping documents have recorded the origin and disembarkation of enslaved people. Traditionally, genealogy study has been done via the exploration of historical records, family tress and birth certificates. Due to recent advancements in the field of genetics, genealogy has been revolutionized and become more accurate. Although these methods can provide continental differentiation, they have poor spatial resolution that makes it hard to localize ancestry assignment as these markers are distributed across different sub-continental regions. To overcome the foregoing drawbacks, in this chapter, we propose a hybrid approach that combines the genetic markers results with the historical records of transatlantic voyage of enslaved people. Addition of the journey data can provide with substantially increased resolution in ancestry assignment, using a Bayesian modeling framework. The proposed Bayesian framework uses the voyage data from historical records available in the transatlantic slave trade database as prior probabilities and combine them with genetic markers of Afro-Americans, considered as the likelihood information to estimate the posterior (updated) probabilities of their ancestry assignments to geographical regions in Africa. We applied the proposed methodology to 60 Afro-American individuals and show that the prior information has increased the assignment probabilities obtained by the posterior distributions for some of the regions.Ph.D

Hardware/software co-design of fractal features based fall detection system

Falls are a leading cause of death in older adults and result in high levels of mortality, morbidity and immobility. Fall Detection Systems (FDS) are imperative for timely medical aid and have been known to reduce death rate by 80%. We propose a novel wearable sensor FDS which exploits fractal dynamics of fall accelerometer signals. Fractal dynamics can be used as an irregularity measure of signals and our work shows that it is a key discriminant for classification of falls from other activities of life. We design, implement and evaluate a hardware feature accelerator for computation of fractal features through multi-level wavelet transform on a reconfigurable embedded System on Chip, Zynq device for evaluating wearable accelerometer sensors. The proposed FDS utilises a hardware/software co-design approach with hardware accelerator for fractal features and software implementation of Linear Discriminant Analysis on an embedded ARM core for high accuracy and energy efficiency. The proposed system achieves 99.38% fall detection accuracy, 7.3× speed-up and 6.53× improvements in power consumption, compared to the software only execution with an overall performance per Watt advantage of 47.6×, while consuming low reconfigurable resources at 28.67%

Detection of Lung Nodules on Medical Images by the Use of Fractal Segmentation

In the present paper, a method for the detection of malignant and benign tumors on the CT scan images has been proposed. In the proposed method, firstly the area of interest in which the tumor may exist is selected on the original image and by the use of image segmentation and determination of the image’s threshold limit, the tumor’s area is specified and then edge detection filters are used for detection of the tumor’s edge. After detection of area and by calculating the fractal dimensions with less percent of errors and better resolution, the areas where contain the tumor are determined. The images used in the proposed method have been extracted from cancer imaging archive database that is made available for public. Compared to other methods, our proposed method recognizes successfully benign and malignant tumors in all cases that have been clinically approved and belong to the database

Fundus image analysis for automatic screening of ophthalmic pathologies

En los ultimos años el número de casos de ceguera se ha reducido significativamente. A pesar de este hecho, la Organización Mundial de la Salud estima que un 80% de los casos de pérdida de visión (285 millones en 2010) pueden ser evitados si se diagnostican en sus estadios más tempranos y son tratados de forma efectiva. Para cumplir esta propuesta se pretende que los servicios de atención primaria incluyan un seguimiento oftalmológico de sus pacientes así como fomentar campañas de cribado en centros proclives a reunir personas de alto riesgo. Sin embargo, estas soluciones exigen una alta carga de trabajo de personal experto entrenado en el análisis de los patrones anómalos propios de cada enfermedad. Por lo tanto, el desarrollo de algoritmos para la creación de sistemas de cribado automáticos juga un papel vital en este campo. La presente tesis persigue la identificacion automática del daño retiniano provocado por dos de las patologías más comunes en la sociedad actual: la retinopatía diabética (RD) y la degenaración macular asociada a la edad (DMAE). Concretamente, el objetivo final de este trabajo es el desarrollo de métodos novedosos basados en la extracción de características de la imagen de fondo de ojo y clasificación para discernir entre tejido sano y patológico. Además, en este documento se proponen algoritmos de pre-procesado con el objetivo de normalizar la alta variabilidad existente en las bases de datos publicas de imagen de fondo de ojo y eliminar la contribución de ciertas estructuras retinianas que afectan negativamente en la detección del daño retiniano. A diferencia de la mayoría de los trabajos existentes en el estado del arte sobre detección de patologías en imagen de fondo de ojo, los métodos propuestos a lo largo de este manuscrito evitan la necesidad de segmentación de las lesiones o la generación de un mapa de candidatos antes de la fase de clasificación. En este trabajo, Local binary patterns, perfiles granulométricos y la dimensión fractal se aplican de manera local para extraer información de textura, morfología y tortuosidad de la imagen de fondo de ojo. Posteriormente, esta información se combina de diversos modos formando vectores de características con los que se entrenan avanzados métodos de clasificación formulados para discriminar de manera óptima entre exudados, microaneurismas, hemorragias y tejido sano. Mediante diversos experimentos, se valida la habilidad del sistema propuesto para identificar los signos más comunes de la RD y DMAE. Para ello se emplean bases de datos públicas con un alto grado de variabilidad sin exlcuir ninguna imagen. Además, la presente tesis también cubre aspectos básicos del paradigma de deep learning. Concretamente, se presenta un novedoso método basado en redes neuronales convolucionales (CNNs). La técnica de transferencia de conocimiento se aplica mediante el fine-tuning de las arquitecturas de CNNs más importantes en el estado del arte. La detección y localización de exudados mediante redes neuronales se lleva a cabo en los dos últimos experimentos de esta tesis doctoral. Cabe destacar que los resultados obtenidos mediante la extracción de características "manual" y posterior clasificación se comparan de forma objetiva con las predicciones obtenidas por el mejor modelo basado en CNNs. Los prometedores resultados obtenidos en esta tesis y el bajo coste y portabilidad de las cámaras de adquisión de imagen de retina podrían facilitar la incorporación de los algoritmos desarrollados en este trabajo en un sistema de cribado automático que ayude a los especialistas en la detección de patrones anomálos característicos de las dos enfermedades bajo estudio: RD y DMAE.In last years, the number of blindness cases has been significantly reduced. Despite this promising news, the World Health Organisation estimates that 80% of visual impairment (285 million cases in 2010) could be avoided if diagnosed and treated early. To accomplish this purpose, eye care services need to be established in primary health and screening campaigns should be a common task in centres with people at risk. However, these solutions entail a high workload for trained experts in the analysis of the anomalous patterns of each eye disease. Therefore, the development of algorithms for automatic screening system plays a vital role in this field. This thesis focuses on the automatic identification of the retinal damage provoked by two of the most common pathologies in the current society: diabetic retinopathy (DR) and age-related macular degeneration (AMD). Specifically, the final goal of this work is to develop novel methods, based on fundus image description and classification, to characterise the healthy and abnormal tissue in the retina background. In addition, pre-processing algorithms are proposed with the aim of normalising the high variability of fundus images and removing the contribution of some retinal structures that could hinder in the retinal damage detection. In contrast to the most of the state-of-the-art works in damage detection using fundus images, the methods proposed throughout this manuscript avoid the necessity of lesion segmentation or the candidate map generation before the classification stage. Local binary patterns, granulometric profiles and fractal dimension are locally computed to extract texture, morphological and roughness information from retinal images. Different combinations of this information feed advanced classification algorithms formulated to optimally discriminate exudates, microaneurysms, haemorrhages and healthy tissues. Through several experiments, the ability of the proposed system to identify DR and AMD signs is validated using different public databases with a large degree of variability and without image exclusion. Moreover, this thesis covers the basics of the deep learning paradigm. In particular, a novel approach based on convolutional neural networks is explored. The transfer learning technique is applied to fine-tune the most important state-of-the-art CNN architectures. Exudate detection and localisation tasks using neural networks are carried out in the last two experiments of this thesis. An objective comparison between the hand-crafted feature extraction and classification process and the prediction models based on CNNs is established. The promising results of this PhD thesis and the affordable cost and portability of retinal cameras could facilitate the further incorporation of the developed algorithms in a computer-aided diagnosis (CAD) system to help specialists in the accurate detection of anomalous patterns characteristic of the two diseases under study: DR and AMD.En els últims anys el nombre de casos de ceguera s'ha reduït significativament. A pesar d'este fet, l'Organització Mundial de la Salut estima que un 80% dels casos de pèrdua de visió (285 milions en 2010) poden ser evitats si es diagnostiquen en els seus estadis més primerencs i són tractats de forma efectiva. Per a complir esta proposta es pretén que els servicis d'atenció primària incloguen un seguiment oftalmològic dels seus pacients així com fomentar campanyes de garbellament en centres regentats per persones d'alt risc. No obstant això, estes solucions exigixen una alta càrrega de treball de personal expert entrenat en l'anàlisi dels patrons anòmals propis de cada malaltia. Per tant, el desenrotllament d'algoritmes per a la creació de sistemes de garbellament automàtics juga un paper vital en este camp. La present tesi perseguix la identificació automàtica del dany retiniano provocat per dos de les patologies més comunes en la societat actual: la retinopatia diabètica (RD) i la degenaración macular associada a l'edat (DMAE) . Concretament, l'objectiu final d'este treball és el desenrotllament de mètodes novedodos basats en l'extracció de característiques de la imatge de fons d'ull i classificació per a discernir entre teixit sa i patològic. A més, en este document es proposen algoritmes de pre- processat amb l'objectiu de normalitzar l'alta variabilitat existent en les bases de dades publiques d'imatge de fons d'ull i eliminar la contribució de certes estructures retinianas que afecten negativament en la detecció del dany retiniano. A diferència de la majoria dels treballs existents en l'estat de l'art sobre detecció de patologies en imatge de fons d'ull, els mètodes proposats al llarg d'este manuscrit eviten la necessitat de segmentació de les lesions o la generació d'un mapa de candidats abans de la fase de classificació. En este treball, Local binary patterns, perfils granulometrics i la dimensió fractal s'apliquen de manera local per a extraure informació de textura, morfologia i tortuositat de la imatge de fons d'ull. Posteriorment, esta informació es combina de diversos modes formant vectors de característiques amb els que s'entrenen avançats mètodes de classificació formulats per a discriminar de manera òptima entre exsudats, microaneurismes, hemorràgies i teixit sa. Per mitjà de diversos experiments, es valida l'habilitat del sistema proposat per a identificar els signes més comuns de la RD i DMAE. Per a això s'empren bases de dades públiques amb un alt grau de variabilitat sense exlcuir cap imatge. A més, la present tesi també cobrix aspectes bàsics del paradigma de deep learning. Concretament, es presenta un nou mètode basat en xarxes neuronals convolucionales (CNNs) . La tècnica de transferencia de coneixement s'aplica per mitjà del fine-tuning de les arquitectures de CNNs més importants en l'estat de l'art. La detecció i localització d'exudats per mitjà de xarxes neuronals es du a terme en els dos últims experiments d'esta tesi doctoral. Cal destacar que els resultats obtinguts per mitjà de l'extracció de característiques "manual" i posterior classificació es comparen de forma objectiva amb les prediccions obtingudes pel millor model basat en CNNs. Els prometedors resultats obtinguts en esta tesi i el baix cost i portabilitat de les cambres d'adquisión d'imatge de retina podrien facilitar la incorporació dels algoritmes desenrotllats en este treball en un sistema de garbellament automàtic que ajude als especialistes en la detecció de patrons anomálos característics de les dos malalties baix estudi: RD i DMAE.Colomer Granero, A. (2018). Fundus image analysis for automatic screening of ophthalmic pathologies [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/99745TESI

Supervised and unsupervised segmentation of textured images by efficient multi-level pattern classification

This thesis proposes new, efficient methodologies for supervised and unsupervised image segmentation based on texture information. For the supervised case, a technique for pixel classification based on a multi-level strategy that iteratively refines the resulting segmentation is proposed. This strategy utilizes pattern recognition methods based on prototypes (determined by clustering algorithms) and support vector machines. In order to obtain the best performance, an algorithm for automatic parameter selection and methods to reduce the computational cost associated with the segmentation process are also included. For the unsupervised case, the previous methodology is adapted by means of an initial pattern discovery stage, which allows transforming the original unsupervised problem into a supervised one. Several sets of experiments considering a wide variety of images are carried out in order to validate the developed techniques.Esta tesis propone metodologías nuevas y eficientes para segmentar imágenes a partir de información de textura en entornos supervisados y no supervisados. Para el caso supervisado, se propone una técnica basada en una estrategia de clasificación de píxeles multinivel que refina la segmentación resultante de forma iterativa. Dicha estrategia utiliza métodos de reconocimiento de patrones basados en prototipos (determinados mediante algoritmos de agrupamiento) y máquinas de vectores de soporte. Con el objetivo de obtener el mejor rendimiento, se incluyen además un algoritmo para selección automática de parámetros y métodos para reducir el coste computacional asociado al proceso de segmentación. Para el caso no supervisado, se propone una adaptación de la metodología anterior mediante una etapa inicial de descubrimiento de patrones que permite transformar el problema no supervisado en supervisado. Las técnicas desarrolladas en esta tesis se validan mediante diversos experimentos considerando una gran variedad de imágenes

Probabilistic methods for high dimensional signal processing

This thesis investigates the use of probabilistic and Bayesian methods for analysing high dimensional signals. The work proceeds in three main parts sharing similar objectives. Throughout we focus on building data efficient inference mechanisms geared toward high dimensional signal processing. This is achieved by using probabilistic models on top of informative data representation operators. We also improve on the fitting objective to make it better suited to our requirements. Variational Inference We introduce a variational approximation framework using direct optimisation of what is known as the scale invariant Alpha-Beta divergence (sAB-divergence). This new objective encompasses most variational objectives that use the Kullback-Leibler, the Rényi or the gamma divergences. It also gives access to objective functions never exploited before in the context of variational inference. This is achieved via two easy to interpret control parameters, which allow for a smooth interpolation over the divergence space while trading-off properties such as mass-covering of a target distribution and robustness to outliers in the data. Furthermore, the sAB variational objective can be optimised directly by re-purposing existing methods for Monte Carlo computation of complex variational objectives, leading to estimates of the divergence instead of variational lower bounds. We show the advantages of this objective on Bayesian models for regression problems. Roof-Edge hidden Markov Random Field We propose a method for semi-local Hurst estimation by incorporating a Markov random field model to constrain a wavelet-based pointwise Hurst estimator. This results in an estimator which is able to exploit the spatial regularities of a piecewise parametric varying Hurst parameter. The pointwise estimates are jointly inferred along with the parametric form of the underlying Hurst function which characterises how the Hurst parameter varies deterministically over the spatial support of the data. Unlike recent Hurst regularisation methods, the proposed approach is flexible in that arbitrary parametric forms can be considered and is extensible in as much as the associated gradient descent algorithm can accommodate a broad class of distributional assumptions without any significant modifications. The potential benefits of the approach are illustrated with simulations of various first-order polynomial forms. Scattering Hidden Markov Tree We here combine the rich, over-complete signal representation afforded by the scattering transform together with a probabilistic graphical model which captures hierarchical dependencies between coefficients at different layers. The wavelet scattering network result in a high-dimensional representation which is translation invariant and stable to deformations whilst preserving informative content. Such properties are achieved by cascading wavelet transform convolutions with non-linear modulus and averaging operators. The network structure and its distributions are described using a Hidden Markov Tree. This yields a generative model for high dimensional inference and offers a means to perform various inference tasks such as prediction. Our proposed scattering convolutional hidden Markov tree displays promising results on classification tasks of complex images in the challenging case where the number of training examples is extremely small. We also use variational methods on the aforementioned model and leverage the objective sAB variational objective defined earlier to improve the quality of the approximation

Computational Modeling for Abnormal Brain Tissue Segmentation, Brain Tumor Tracking, and Grading

This dissertation proposes novel texture feature-based computational models for quantitative analysis of abnormal tissues in two neurological disorders: brain tumor and stroke. Brain tumors are the cells with uncontrolled growth in the brain tissues and one of the major causes of death due to cancer. On the other hand, brain strokes occur due to the sudden interruption of the blood supply which damages the normal brain tissues and frequently causes death or persistent disability. Clinical management of these brain tumors and stroke lesions critically depends on robust quantitative analysis using different imaging modalities including Magnetic Resonance (MR) and Digital Pathology (DP) images. Due to uncontrolled growth and infiltration into the surrounding tissues, the tumor regions appear with a significant texture variation in the static MRI volume and also in the longitudinal imaging study. Consequently, this study developed computational models using novel texture features to segment abnormal brain tissues (tumor, and stroke lesions), tracking the change of tumor volume in longitudinal images, and tumor grading in MR images. Manual delineation and analysis of these abnormal tissues in large scale is tedious, error-prone, and often suffers from inter-observer variability. Therefore, efficient computational models for robust segmentation of different abnormal tissues is required to support the diagnosis and analysis processes. In this study, brain tissues are characterized with novel computational modeling of multi-fractal texture features for multi-class brain tumor tissue segmentation (BTS) and extend the method for ischemic stroke lesions in MRI. The robustness of the proposed segmentation methods is evaluated using a huge amount of private and public domain clinical data that offers competitive performance when compared with that of the state-of-the-art methods. Further, I analyze the dynamic texture behavior of tumor volume in longitudinal imaging and develop post-processing frame-work using three-dimensional (3D) texture features. These post-processing methods are shown to reduce the false positives in the BTS results and improve the overall segmentation result in longitudinal imaging. Furthermore, using this improved segmentation results the change of tumor volume has been quantified in three types such as stable, progress, and shrinkage as observed by the volumetric changes of different tumor tissues in longitudinal images. This study also investigates a novel non-invasive glioma grading, for the first time in literature, that uses structural MRI only. Such non-invasive glioma grading may be useful before an invasive biopsy is recommended. This study further developed an automatic glioma grading scheme using the invasive cell nuclei morphology in DP images for cross-validation with the same patients. In summary, the texture-based computational models proposed in this study are expected to facilitate the clinical management of patients with the brain tumors and strokes by automating large scale imaging data analysis, reducing human error, inter-observer variability, and producing repeatable brain tumor quantitation and grading