Segmentation of Optic Disc in Fundus Images using Convolutional Neural Networks for Detection of Glaucoma

The condition of the vascular network of human eye is an important diagnostic factor in ophthalmology. Its segmentation in fundus imaging is a difficult task due to various anatomical structures like blood vessel, optic cup, optic disc, macula and fovea. Blood vessel segmentation can assist in the detection of pathological changes which are possible indicators for arteriosclerosis, retinopathy, microaneurysms and macular degeneration. The segmentation of optic disc and optic cup from retinal images is used to calculate an important indicator, cup-to disc ratio( CDR) accurately to help the professionals in the detection of Glaucoma in fundus images.In this proposed work, an automated segmentation of anatomical structures in fundus images such as blood vessel and optic disc is done using Convolutional Neural Networks (CNN) . A Convolutional Neural Network is a composite of multiple elementary processing units, each featuring several weighted inputs and one output, performing convolution of input signals with weights and transforming the outcome with some form of nonlinearity. The units are arranged in rectangular layers (grids), and their locations in a layer correspond to pixels in an input image. The spatial arrangement of units is the primary characteristics that makes CNNs suitable for processing visual information; the other features are local connectivity, parameter sharing and pooling of hidden units. The advantage of CNN is that it can be trained repeatedly so more features can be found. An average accuracy of 95.64% is determined in the classification of blood vessel or not. Optic cup is also segmented from the optic disc by Fuzzy C Means Clustering (FCM). This proposed algorithm is tested on a sample of hospital images and CDR value is determined. The obtained values of CDR is compared with the given values of the sample images and hence the performance of proposed system in which Convolutional Neural Networks for segmentation is employed, is excellent in automated detection of healthy and Glaucoma images

Segmentation Of Retinal Blood Vessels Using A Novel Fuzzy Logic Algorithm

In this work, a rule-based method is presented for blood vessel segmentation in digital retinal images. This method can be used in computer analyses of retinal images, e.g., in automated screening for diabetic retinopathy. Diabetic retinopathy is the most common diabetic eye disease and a leading cause of blindness. Diagnosis of diabetic retinopathy at an early stage can be done through the segmentation of the blood vessels of retina. Many studies have been carried out in the last decade in order to obtain accurate blood vessel segmentation in retinal images including supervised and rule-based methods. This method uses eight feature vectors for each pixel. These features are means and medians of intensity values of pixel itself, first and second nearest neighbor at four directions. Features are used in fuzzy logic algorithm as crisp input. The final segmentation is obtained using a thresholding method. The method was tested on the publicly available database DRIVE and its results are compared with distinguished published methods. Our method achieved an average accuracy of 93.82% and an area under the receiver operating characteristic curve of 94.19% for DRIVE database. Our results demonstrated an average sensitivity of 72.28% and a specificity of 97.04%. The calculated sensitivity and specificity values for DRIVE database also state that the proposed segmentation method is effective and robust

Retinal vessel segmentation using textons

Segmenting vessels from retinal images, like segmentation in many other medical image domains, is a challenging task, as there is no unified way that can be adopted to extract the vessels accurately. However, it is the most critical stage in automatic assessment of various forms of diseases (e.g. Glaucoma, Age-related macular degeneration, diabetic retinopathy and cardiovascular diseases etc.). Our research aims to investigate retinal image segmentation approaches based on textons as they provide a compact description of texture that can be learnt from a training set. This thesis presents a brief review of those diseases and also includes their current situations, future trends and techniques used for their automatic diagnosis in routine clinical applications. The importance of retinal vessel segmentation is particularly emphasized in such applications. An extensive review of previous work on retinal vessel segmentation and salient texture analysis methods is presented. Five automatic retinal vessel segmentation methods are proposed in this thesis. The first method focuses on addressing the problem of removing pathological anomalies (Drusen, exudates) for retinal vessel segmentation, which have been identified by other researchers as a problem and a common source of error. The results show that the modified method shows some improvement compared to a previously published method. The second novel supervised segmentation method employs textons. We propose a new filter bank (MR11) that includes bar detectors for vascular feature extraction and other kernels to detect edges and photometric variations in the image. The k-means clustering algorithm is adopted for texton generation based on the vessel and non-vessel elements which are identified by ground truth. The third improved supervised method is developed based on the second one, in which textons are generated by k-means clustering and texton maps representing vessels are derived by back projecting pixel clusters onto hand labelled ground truth. A further step is implemented to ensure that the best combinations of textons are represented in the map and subsequently used to identify vessels in the test set. The experimental results on two benchmark datasets show that our proposed method performs well compared to other published work and the results of human experts. A further test of our system on an independent set of optical fundus images verified its consistent performance. The statistical analysis on experimental results also reveals that it is possible to train unified textons for retinal vessel segmentation. In the fourth method a novel scheme using Gabor filter bank for vessel feature extraction is proposed. The ii method is inspired by the human visual system. Machine learning is used to optimize the Gabor filter parameters. The experimental results demonstrate that our method significantly enhances the true positive rate while maintaining a level of specificity that is comparable with other approaches. Finally, we proposed a new unsupervised texton based retinal vessel segmentation method using derivative of SIFT and multi-scale Gabor filers. The lack of sufficient quantities of hand labelled ground truth and the high level of variability in ground truth labels amongst experts provides the motivation for this approach. The evaluation results reveal that our unsupervised segmentation method is comparable with the best other supervised methods and other best state of the art methods

Segmentation and Characterization of Small Retinal Vessels in Fundus Images Using the Tensor Voting Approach

RÉSUMÉ La rétine permet de visualiser facilement une partie du réseau vasculaire humain. Elle offre ainsi un aperçu direct sur le développement et le résultat de certaines maladies liées au réseau vasculaire dans son entier. Chaque complication visible sur la rétine peut avoir un impact sur la capacité visuelle du patient. Les plus petits vaisseaux sanguins sont parmi les premières structures anatomiques affectées par la progression d’une maladie, être capable de les analyser est donc crucial. Les changements dans l’état, l’aspect, la morphologie, la fonctionnalité, ou même la croissance des petits vaisseaux indiquent la gravité des maladies. Le diabète est une maladie métabolique qui affecte des millions de personnes autour du monde. Cette maladie affecte le taux de glucose dans le sang et cause des changements pathologiques dans différents organes du corps humain. La rétinopathie diabétique décrit l’en- semble des conditions et conséquences du diabète au niveau de la rétine. Les petits vaisseaux jouent un rôle dans le déclenchement, le développement et les conséquences de la rétinopa- thie. Dans les dernières étapes de cette maladie, la croissance des nouveaux petits vaisseaux, appelée néovascularisation, présente un risque important de provoquer la cécité. Il est donc crucial de détecter tous les changements qui ont lieu dans les petits vaisseaux de la rétine dans le but de caractériser les vaisseaux sains et les vaisseaux anormaux. La caractérisation en elle-même peut faciliter la détection locale d’une rétinopathie spécifique. La segmentation automatique des structures anatomiques comme le réseau vasculaire est une étape cruciale. Ces informations peuvent être fournies à un médecin pour qu’elles soient considérées lors de son diagnostic. Dans les systèmes automatiques d’aide au diagnostic, le rôle des petits vaisseaux est significatif. Ne pas réussir à les détecter automatiquement peut conduire à une sur-segmentation du taux de faux positifs des lésions rouges dans les étapes ultérieures. Les efforts de recherche se sont concentrés jusqu’à présent sur la localisation précise des vaisseaux de taille moyenne. Les modèles existants ont beaucoup plus de difficultés à extraire les petits vaisseaux sanguins. Les modèles existants ne sont pas robustes à la grande variance d’apparence des vaisseaux ainsi qu’à l’interférence avec l’arrière-plan. Les modèles de la littérature existante supposent une forme générale qui n’est pas suffisante pour s’adapter à la largeur étroite et la courbure qui caractérisent les petits vaisseaux sanguins. De plus, le contraste avec l’arrière-plan dans les régions des petits vaisseaux est très faible. Les méthodes de segmentation ou de suivi produisent des résultats fragmentés ou discontinus. Par ailleurs, la segmentation des petits vaisseaux est généralement faite aux dépends de l’amplification du bruit. Les modèles déformables sont inadéquats pour segmenter les petits vaisseaux. Les forces utilisées ne sont pas assez flexibles pour compenser le faible contraste, la largeur, et vii la variance des vaisseaux. Enfin, les approches de type apprentissage machine nécessitent un entraînement avec une base de données étiquetée. Il est très difficile d’obtenir ces bases de données dans le cas des petits vaisseaux. Cette thèse étend les travaux de recherche antérieurs en fournissant une nouvelle mé- thode de segmentation des petits vaisseaux rétiniens. La détection de ligne à échelles multiples (MSLD) est une méthode récente qui démontre une bonne performance de segmentation dans les images de la rétine, tandis que le vote tensoriel est une méthode proposée pour reconnecter les pixels. Une approche combinant un algorithme de détection de ligne et de vote tensoriel est proposée. L’application des détecteurs de lignes a prouvé son efficacité à segmenter les vais- seaux de tailles moyennes. De plus, les approches d’organisation perceptuelle comme le vote tensoriel ont démontré une meilleure robustesse en combinant les informations voisines d’une manière hiérarchique. La méthode de vote tensoriel est plus proche de la perception humain que d’autres modèles standards. Comme démontré dans ce manuscrit, c’est un outil pour segmenter les petits vaisseaux plus puissant que les méthodes existantes. Cette combinaison spécifique nous permet de surmonter les défis de fragmentation éprouvés par les méthodes de type modèle déformable au niveau des petits vaisseaux. Nous proposons également d’utiliser un seuil adaptatif sur la réponse de l’algorithme de détection de ligne pour être plus robuste aux images non-uniformes. Nous illustrons également comment une combinaison des deux méthodes individuelles, à plusieurs échelles, est capable de reconnecter les vaisseaux sur des distances variables. Un algorithme de reconstruction des vaisseaux est également proposé. Cette dernière étape est nécessaire car l’information géométrique complète est requise pour pouvoir utiliser la segmentation dans un système d’aide au diagnostic. La segmentation a été validée sur une base de données d’images de fond d’oeil à haute résolution. Cette base contient des images manifestant une rétinopathie diabétique. La seg- mentation emploie des mesures de désaccord standards et aussi des mesures basées sur la perception. En considérant juste les petits vaisseaux dans les images de la base de données, l’amélioration dans le taux de sensibilité que notre méthode apporte par rapport à la méthode standard de détection multi-niveaux de lignes est de 6.47%. En utilisant les mesures basées sur la perception, l’amélioration est de 7.8%. Dans une seconde partie du manuscrit, nous proposons également une méthode pour caractériser les rétines saines ou anormales. Certaines images contiennent de la néovascula- risation. La caractérisation des vaisseaux en bonne santé ou anormale constitue une étape essentielle pour le développement d’un système d’aide au diagnostic. En plus des défis que posent les petits vaisseaux sains, les néovaisseaux démontrent eux un degré de complexité encore plus élevé. Ceux-ci forment en effet des réseaux de vaisseaux à la morphologie com- plexe et inhabituelle, souvent minces et à fortes courbures. Les travaux existants se limitent viii à l’utilisation de caractéristiques de premier ordre extraites des petits vaisseaux segmentés. Notre contribution est d’utiliser le vote tensoriel pour isoler les jonctions vasculaires et d’uti- liser ces jonctions comme points d’intérêts. Nous utilisons ensuite une statistique spatiale de second ordre calculée sur les jonctions pour caractériser les vaisseaux comme étant sains ou pathologiques. Notre méthode améliore la sensibilité de la caractérisation de 9.09% par rapport à une méthode de l’état de l’art. La méthode développée s’est révélée efficace pour la segmentation des vaisseaux réti- niens. Des tenseurs d’ordre supérieur ainsi que la mise en œuvre d’un vote par tenseur via un filtrage orientable pourraient être étudiés pour réduire davantage le temps d’exécution et résoudre les défis encore présents au niveau des jonctions vasculaires. De plus, la caractéri- sation pourrait être améliorée pour la détection de la rétinopathie proliférative en utilisant un apprentissage supervisé incluant des cas de rétinopathie diabétique non proliférative ou d’autres pathologies. Finalement, l’incorporation des méthodes proposées dans des systèmes d’aide au diagnostic pourrait favoriser le dépistage régulier pour une détection précoce des rétinopathies et d’autres pathologies oculaires dans le but de réduire la cessité au sein de la population.----------ABSTRACT As an easily accessible site for the direct observation of the circulation system, human retina can offer a unique insight into diseases development or outcome. Retinal vessels are repre- sentative of the general condition of the whole systematic circulation, and thus can act as a "window" to the status of the vascular network in the whole body. Each complication on the retina can have an adverse impact on the patient’s sight. In this direction, small vessels’ relevance is very high as they are among the first anatomical structures that get affected as diseases progress. Moreover, changes in the small vessels’ state, appearance, morphology, functionality, or even growth indicate the severity of the diseases. This thesis will focus on the retinal lesions due to diabetes, a serious metabolic disease affecting millions of people around the world. This disorder disturbs the natural blood glucose levels causing various pathophysiological changes in different systems across the human body. Diabetic retinopathy is the medical term that describes the condition when the fundus and the retinal vessels are affected by diabetes. As in other diseases, small vessels play a crucial role in the onset, the development, and the outcome of the retinopathy. More importantly, at the latest stage, new small vessels, or neovascularizations, growth constitutes a factor of significant risk for blindness. Therefore, there is a need to detect all the changes that occur in the small retinal vessels with the aim of characterizing the vessels to healthy or abnormal. The characterization, in turn, can facilitate the detection of a specific retinopathy locally, like the sight-threatening proliferative diabetic retinopathy. Segmentation techniques can automatically isolate important anatomical structures like the vessels, and provide this information to the physician to assist him in the final decision. In comprehensive systems for the automatization of DR detection, small vessels role is significant as missing them early in a CAD pipeline might lead to an increase in the false positive rate of red lesions in subsequent steps. So far, the efforts have been concentrated mostly on the accurate localization of the medium range vessels. In contrast, the existing models are weak in case of the small vessels. The required generalization to adapt an existing model does not allow the approaches to be flexible, yet robust to compensate for the increased variability in the appearance as well as the interference with the background. So far, the current template models (matched filtering, line detection, and morphological processing) assume a general shape for the vessels that is not enough to approximate the narrow, curved, characteristics of the small vessels. Additionally, due to the weak contrast in the small vessel regions, the current segmentation and the tracking methods produce fragmented or discontinued results. Alternatively, the small vessel segmentation can be accomplished at the expense of x background noise magnification, in the case of using thresholding or the image derivatives methods. Furthermore, the proposed deformable models are not able to propagate a contour to the full extent of the vasculature in order to enclose all the small vessels. The deformable model external forces are ineffective to compensate for the low contrast, the low width, the high variability in the small vessel appearance, as well as the discontinuities. Internal forces, also, are not able to impose a global shape constraint to the contour that could be able to approximate the variability in the appearance of the vasculature in different categories of vessels. Finally, machine learning approaches require the training of a classifier on a labelled set. Those sets are difficult to be obtained, especially in the case of the smallest vessels. In the case of the unsupervised methods, the user has to predefine the number of clusters and perform an effective initialization of the cluster centers in order to converge to the global minimum. This dissertation expanded the previous research work and provides a new segmentation method for the smallest retinal vessels. Multi-scale line detection (MSLD) is a recent method that demonstrates good segmentation performance in the retinal images, while tensor voting is a method first proposed for reconnecting pixels. For the first time, we combined the line detection with the tensor voting framework. The application of the line detectors has been proved an effective way to segment medium-sized vessels. Additionally, perceptual organization approaches like tensor voting, demonstrate increased robustness by combining information coming from the neighborhood in a hierarchical way. Tensor voting is closer than standard models to the way human perception functions. As we show, it is a more powerful tool to segment small vessels than the existing methods. This specific combination allows us to overcome the apparent fragmentation challenge of the template methods at the smallest vessels. Moreover, we thresholded the line detection response adaptively to compensate for non-uniform images. We also combined the two individual methods in a multi-scale scheme in order to reconnect vessels at variable distances. Finally, we reconstructed the vessels from their extracted centerlines based on pixel painting as complete geometric information is required to be able to utilize the segmentation in a CAD system. The segmentation was validated on a high-resolution fundus image database that in- cludes diabetic retinopathy images of varying stages, using standard discrepancy as well as perceptual-based measures. When only the smallest vessels are considered, the improve- ments in the sensitivity rate for the database against the standard multi-scale line detection method is 6.47%. For the perceptual-based measure, the improvement is 7.8% against the basic method. The second objective of the thesis was to implement a method for the characterization of isolated retinal areas into healthy or abnormal cases. Some of the original images, from which xi these patches are extracted, contain neovascularizations. Investigation of image features for the vessels characterization to healthy or abnormal constitutes an essential step in the direction of developing CAD system for the automatization of DR screening. Given that the amount of data will significantly increase under CAD systems, the focus on this category of vessels can facilitate the referral of sight-threatening cases to early treatment. In addition to the challenges that small healthy vessels pose, neovessels demonstrate an even higher degree of complexity as they form networks of convolved, twisted, looped thin vessels. The existing work is limited to the use of first-order characteristics extracted from the small segmented vessels that limits the study of patterns. Our contribution is in using the tensor voting framework to isolate the retinal vascular junctions and in turn using those junctions as points of interests. Second, we exploited second-order statistics computed on the junction spatial distribution to characterize the vessels as healthy or neovascularizations. In fact, the second-order spatial statistics extracted from the junction distribution are combined with widely used features to improve the characterization sensitivity by 9.09% over the state of art. The developed method proved effective for the segmentation of the retinal vessels. Higher order tensors along with the implementation of tensor voting via steerable filtering could be employed to further reduce the execution time, and resolve the challenges at vascular junctions. Moreover, the characterization could be advanced to the detection of prolifera- tive retinopathy by extending the supervised learning to include non-proliferative diabetic retinopathy cases or other pathologies. Ultimately, the incorporation of the methods into CAD systems could facilitate screening for the effective reduction of the vision-threatening diabetic retinopathy rates, or the early detection of other than ocular pathologies

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

A retinal vasculature tracking system guided by a deep architecture

Many diseases such as diabetic retinopathy (DR) and cardiovascular diseases show their early signs on retinal vasculature. Analysing the vasculature in fundus images may provide a tool for ophthalmologists to diagnose eye-related diseases and to monitor their progression. These analyses may also facilitate the discovery of new relations between changes on retinal vasculature and the existence or progression of related diseases or to validate present relations. In this thesis, a data driven method, namely a Translational Deep Belief Net (a TDBN), is adapted to vasculature segmentation. The segmentation performance of the TDBN on low resolution images was found to be comparable to that of the best-performing methods. Later, this network is used for the implementation of super-resolution for the segmentation of high resolution images. This approach provided an acceleration during segmentation, which relates to down-sampling ratio of an input fundus image. Finally, the TDBN is extended for the generation of probability maps for the existence of vessel parts, namely vessel interior, centreline, boundary and crossing/bifurcation patterns in centrelines. These probability maps are used to guide a probabilistic vasculature tracking system. Although segmentation can provide vasculature existence in a fundus image, it does not give quantifiable measures for vasculature. The latter has more practical value in medical clinics. In the second half of the thesis, a retinal vasculature tracking system is presented. This system uses Particle Filters to describe vessel morphology and topology. Apart from previous studies, the guidance for tracking is provided with the combination of probability maps generated by the TDBN. The experiments on a publicly available dataset, REVIEW, showed that the consistency of vessel widths predicted by the proposed method was better than that obtained from observers. Moreover, very noisy and low contrast vessel boundaries, which were hardly identifiable to the naked eye, were accurately estimated by the proposed tracking system. Also, bifurcation/crossing locations during the course of tracking were detected almost completely. Considering these promising initial results, future work involves analysing the performance of the tracking system on automatic detection of complete vessel networks in fundus images.Open Acces

Deep learning analysis of eye fundus images to support medical diagnosis

Machine learning techniques have been successfully applied to support medical decision making of cancer, heart diseases and degenerative diseases of the brain. In particular, deep learning methods have been used for early detection of abnormalities in the eye that could improve the diagnosis of different ocular diseases, especially in developing countries, where there are major limitations to access to specialized medical treatment. However, the early detection of clinical signs such as blood vessel, optic disc alterations, exudates, hemorrhages, drusen, and microaneurysms presents three main challenges: the ocular images can be affected by noise artifact, the features of the clinical signs depend specifically on the acquisition source, and the combination of local signs and grading disease label is not an easy task. This research approaches the problem of combining local signs and global labels of different acquisition sources of medical information as a valuable tool to support medical decision making in ocular diseases. Different models for different eye diseases were developed. Four models were developed using eye fundus images: for DME, it was designed a two-stages model that uses a shallow model to predict an exudate binary mask. Then, the binary mask is stacked with the raw fundus image into a 4-channel array as an input of a deep convolutional neural network for diabetic macular edema diagnosis; for glaucoma, it was developed three deep learning models. First, it was defined a deep learning model based on three-stages that contains an initial stage for automatically segment two binary masks containing optic disc and physiological cup segmentation, followed by an automatic morphometric features extraction stage from previous segmentations, and a final classification stage that supports the glaucoma diagnosis with intermediate medical information. Two late-data-fusion methods that fused morphometric features from cartesian and polar segmentation of the optic disc and physiological cup with features extracted from raw eye fundus images. On the other hand, two models were defined using optical coherence tomography. First, a customized convolutional neural network termed as OCT-NET to extract features from OCT volumes to classify DME, DR-DME and AMD conditions. In addition, this model generates images with highlighted local information about the clinical signs, and it estimates the number of slides inside a volume with local abnormalities. Finally, a 3D-Deep learning model that uses OCT volumes as an input to estimate the retinal thickness map useful to grade AMD. The methods were systematically evaluated using ten free public datasets. The methods were compared and validated against other state-of-the-art algorithms and the results were also qualitatively evaluated by ophthalmology experts from Fundación Oftalmológica Nacional. In addition, the proposed methods were tested as a diagnosis support tool of diabetic macular edema, glaucoma, diabetic retinopathy and age-related macular degeneration using two different ocular imaging representations. Thus, we consider that this research could be potentially a big step in building telemedicine tools that could support medical personnel for detecting ocular diseases using eye fundus images and optical coherence tomography.Las técnicas de aprendizaje automático se han aplicado con éxito para apoyar la toma de decisiones médicas sobre el cáncer, las enfermedades cardíacas y las enfermedades degenerativas del cerebro. En particular, se han utilizado métodos de aprendizaje profundo para la detección temprana de anormalidades en el ojo que podrían mejorar el diagnóstico de diferentes enfermedades oculares, especialmente en países en desarrollo, donde existen grandes limitaciones para acceder a tratamiento médico especializado. Sin embargo, la detección temprana de signos clínicos como vasos sanguíneos, alteraciones del disco óptico, exudados, hemorragias, drusas y microaneurismas presenta tres desafíos principales: las imágenes oculares pueden verse afectadas por artefactos de ruido, las características de los signos clínicos dependen específicamente de fuente de adquisición, y la combinación de signos locales y clasificación de la enfermedad no es una tarea fácil. Esta investigación aborda el problema de combinar signos locales y etiquetas globales de diferentes fuentes de adquisición de información médica como una herramienta valiosa para apoyar la toma de decisiones médicas en enfermedades oculares. Se desarrollaron diferentes modelos para diferentes enfermedades oculares. Se desarrollaron cuatro modelos utilizando imágenes de fondo de ojo: para DME, se diseñó un modelo de dos etapas que utiliza un modelo superficial para predecir una máscara binaria de exudados. Luego, la máscara binaria se apila con la imagen de fondo de ojo original en una matriz de 4 canales como entrada de una red neuronal convolucional profunda para el diagnóstico de edema macular diabético; para el glaucoma, se desarrollaron tres modelos de aprendizaje profundo. Primero, se definió un modelo de aprendizaje profundo basado en tres etapas que contiene una etapa inicial para segmentar automáticamente dos máscaras binarias que contienen disco óptico y segmentación fisiológica de la copa, seguido de una etapa de extracción de características morfométricas automáticas de segmentaciones anteriores y una etapa de clasificación final que respalda el diagnóstico de glaucoma con información médica intermedia. Dos métodos de fusión de datos tardíos que fusionaron características morfométricas de la segmentación cartesiana y polar del disco óptico y la copa fisiológica con características extraídas de imágenes de fondo de ojo crudo. Por otro lado, se definieron dos modelos mediante tomografía de coherencia óptica. Primero, una red neuronal convolucional personalizada denominada OCT-NET para extraer características de los volúmenes OCT para clasificar las condiciones DME, DR-DME y AMD. Además, este modelo genera imágenes con información local resaltada sobre los signos clínicos, y estima el número de diapositivas dentro de un volumen con anomalías locales. Finalmente, un modelo de aprendizaje 3D-Deep que utiliza volúmenes OCT como entrada para estimar el mapa de espesor retiniano útil para calificar AMD. Los métodos se evaluaron sistemáticamente utilizando diez conjuntos de datos públicos gratuitos. Los métodos se compararon y validaron con otros algoritmos de vanguardia y los resultados también fueron evaluados cualitativamente por expertos en oftalmología de la Fundación Oftalmológica Nacional. Además, los métodos propuestos se probaron como una herramienta de diagnóstico de edema macular diabético, glaucoma, retinopatía diabética y degeneración macular relacionada con la edad utilizando dos representaciones de imágenes oculares diferentes. Por lo tanto, consideramos que esta investigación podría ser potencialmente un gran paso en la construcción de herramientas de telemedicina que podrían ayudar al personal médico a detectar enfermedades oculares utilizando imágenes de fondo de ojo y tomografía de coherencia óptica.Doctorad

Adaptive Feature Engineering Modeling for Ultrasound Image Classification for Decision Support

Ultrasonography is considered a relatively safe option for the diagnosis of benign and malignant cancer lesions due to the low-energy sound waves used. However, the visual interpretation of the ultrasound images is time-consuming and usually has high false alerts due to speckle noise. Improved methods of collection image-based data have been proposed to reduce noise in the images; however, this has proved not to solve the problem due to the complex nature of images and the exponential growth of biomedical datasets. Secondly, the target class in real-world biomedical datasets, that is the focus of interest of a biopsy, is usually significantly underrepresented compared to the non-target class. This makes it difficult to train standard classification models like Support Vector Machine (SVM), Decision Trees, and Nearest Neighbor techniques on biomedical datasets because they assume an equal class distribution or an equal misclassification cost. Resampling techniques by either oversampling the minority class or under-sampling the majority class have been proposed to mitigate the class imbalance problem but with minimal success. We propose a method of resolving the class imbalance problem with the design of a novel data-adaptive feature engineering model for extracting, selecting, and transforming textural features into a feature space that is inherently relevant to the application domain. We hypothesize that by maximizing the variance and preserving as much variability in well-engineered features prior to applying a classifier model will boost the differentiation of the thyroid nodules (benign or malignant) through effective model building. Our proposed a hybrid approach of applying Regression and Rule-Based techniques to build our Feature Engineering and a Bayesian Classifier respectively. In the Feature Engineering model, we transformed images pixel intensity values into a high dimensional structured dataset and fitting a regression analysis model to estimate relevant kernel parameters to be applied to the proposed filter method. We adopted an Elastic Net Regularization path to control the maximum log-likelihood estimation of the Regression model. Finally, we applied a Bayesian network inference to estimate a subset for the textural features with a significant conditional dependency in the classification of the thyroid lesion. This is performed to establish the conditional influence on the textural feature to the random factors generated through our feature engineering model and to evaluate the success criterion of our approach. The proposed approach was tested and evaluated on a public dataset obtained from thyroid cancer ultrasound diagnostic data. The analyses of the results showed that the classification performance had a significant improvement overall for accuracy and area under the curve when then proposed feature engineering model was applied to the data. We show that a high performance of 96.00% accuracy with a sensitivity and specificity of 99.64%) and 90.23% respectively was achieved for a filter size of 13 × 13