Diabetic retinopathy detection with texture features

Diabetic retinopathy is one of the leading causes of visual impairment and blindness in the world and the prevalence keeps increasing. It is a vascular disorder of the retina and a symptom of diabetes mellitus. The health of the retina is studied with non-invasive retinal imaging. However, the analysis of the retinal images is laborious, subjective and the number of images to be reviewed is increasing. In this master’s thesis, a computer-aided detection system for diabetic retinopathy, microaneurysms and small hemorrhages was designed and implemented. The purpose of this study was to find out, are texture features able to produce descriptive and efficient information for the retinal image classification and is the implemented system accurate. The process included image preprocessing, extraction of 21 texture features, feature selection and classification with a support vector machine. The retinal image datasets that were used for the testing were Messidor, DIARETDB1 and e-ophta. The texture features were not successful when classifying the retinal images into diabetic retinopathy or normal. The best average accuracy was 69 % with 72 % average sensitivity and 66 % average specificity. The texture features are not that descriptive as global features with a whole retinal image. Additionally, the varying size of the images and variation within a class affected the classification. The second experiment studied the classification of images into microaneurysm or normal by dividing the retinal images into blocks. The texture features were successful when the images were divided into small blocks of size 50*50. The best average accuracy was 96 % with 96 % average sensitivity and 96 % average specificity. The texture features are more descriptive in the local setting since then they can extract finer details. To ease the clinical workflow of ophthalmologists and other experts, the computer-aided detection system can lower the manual labor and make retinal image analysis more efficient, accurate and precise. To develop the systems further, an optic disc and image quality detectors are needed

RFFE – Random Forest Fuzzy Entropy for the classification of Diabetes Mellitus

Diabetes is a category of metabolic disease commonly known as a chronic illness. It causes the body to generate less insulin and raises blood sugar levels, leading to various issues and disrupting the functioning of organs, including the retinal, kidney and nerves. To prevent this, people with chronic illnesses require lifetime access to treatment. As a result, early diabetes detection is essential and might save many lives. Diagnosis of people at high risk of developing diabetes is utilized for preventing the disease in various aspects. This article presents a chronic illness prediction prototype based on a person's risk feature data to provide an early prediction for diabetes with Fuzzy Entropy random vectors that regulate the development of each tree in the Random Forest. The proposed prototype consists of data imputation, data sampling, feature selection, and various techniques to predict the disease, such as Fuzzy Entropy, Synthetic Minority Oversampling Technique (SMOTE), Convolutional Neural Network (CNN) with Stochastic Gradient Descent with Momentum (SGDM), Support Vector Machines (SVM), Classification and Regression Tree (CART), K-Nearest Neighbor (KNN), and Naïve Bayes (NB). This study uses the existing Pima Indian Diabetes (PID) dataset for diabetic disease prediction. The predictions' true/false positive/negative rate is investigated using the confusion matrix and the receiver operating characteristic area under the curve (ROCAUC). Findings on a PID dataset are compared with machine learning algorithms revealing that the proposed Random Forest Fuzzy Entropy (RFFE) is a valuable approach for diabetes prediction, with an accuracy of 98 percent

Développement et validation d’un système automatique de classification de la dégénérescence maculaire liée à l’âge

RÉSUMÉ La dégénérescence maculaire liée à l’âge (DMLA) est une des principales causes de déficience visuelle menant à une cécité irréversible chez les personnes âgées dans les pays industrialisés. Cette maladie regroupe une variété d’anomalies touchant la macula, se présentant sous diverses formes. Un des moyens les plus couramment utilisés pour rapidement examiner la rétine est la photographie de fond d’œil. À partir de ces images, il est déjà possible de détecter et de poser un diagnostic sur l’avancée de la maladie. Une classification recommandée pour évaluer la DMLA est la classification simplifiée de l’AREDS qui consiste à diviser la maladie en quatre catégories : non-DMLA, précoce, modérée, et avancée. Cette classification aide à déterminer le traitement spécifique le plus optimal. Elle se base sur des critères quantitatifs mais également qualitatifs, ce qui peut entrainer des variabilités inter- et intra-expert. Avec le vieillissement de la population et le dépistage systématique, le nombre de cas de DMLA à être examinés et le nombre d’images à être analysées est en augmentation rendant ainsi le travail long et laborieux pour les cliniciens. C’est pour cela, que des méthodes automatiques de détection et de classification de la DMLA ont été proposées, afin de rendre le processus rapide et reproductible. Cependant, il n’existe aucune méthode permettant une classification du degré de sévérité de la DMLA qui soit robuste à la qualité de l’image. Ce dernier point est important lorsqu’on travaille dans un contexte de télémédecine. Dans ce projet, nous proposons de développer et valider un système automatique de classification de la DMLA qui soit robuste à la qualité de l’image. Pour ce faire, nous avons d’abord établi une base de données constituée de 159 images, représentant les quatre catégories de l’AREDS et divers niveaux de qualité d’images. L’étiquetage de ces images a été réalisé par un expert en ophtalmologie et nous a servi de référence. Ensuite, une étude sur l’extraction de caractéristiques nous a permis de relever celles qui étaient pertinentes et de configurer les paramètres pour notre application. Nous en avons conclu que les caractéristiques de texture, de couleur et de contexte visuel semblaient les plus intéressantes. Nous avons effectué par après une étape de sélection afin de réduire la dimensionnalité de l’espace des caractéristiques. Cette étape nous a également permis d’évaluer l’importance des différentes caractéristiques lorsqu’elles étaient combinées ensemble.----------ABSTRACT Age-related macular degeneration (AMD) is the leading cause of visual deficiency and legal blindness in the elderly population in industrialized countries. This disease is a group of heterogeneous disorders affecting the macula. For eye examination, a common used modality is the fundus photography because it is fast and non-invasive procedure which may establish a diagnostic on the stage of the disease. A recommended classification for AMD is the simplified classification of AREDS which divides the disease into four categories: non-AMD, early, moderate and advanced. This classification is helpful to determine the optimal and specific treatment. It is based on quantitative criteria but also on qualitative ones, introducing inter- and intra-expert variability. Moreover, with the aging population and systematic screening, more cases of AMD must be examined and more images must be analyzed, rendering this task long and laborious for clinicians. To address this problem, automatic methods for AMD classification were then proposed for a fast and reproducible process. However, there is no method performing AMD severity classification which is robust to image quality. This last part is especially important in a context of telemedicine where the acquisition conditions are various. The aim of this project is to develop and validate an automatic system for AMD classification which is robust to image quality. To do so, we worked with a database of 159 images, representing the different categories at various levels of image quality. The labelling of these images is realized by one expert and served as a reference. A study on feature extraction is carried out to determine relevant features and to set the parameters for this application. We conclude that features based on texture, color and visual context are the most interesting. After, a selection is applied to reduce the dimensionality of features space. This step allows us to evaluate the feature relevance when all the features are combined. It is shown that the local binary patterns applied on the green channel are the most the discriminant features for AMD classification. Finally, different systems for AMD classification were modeled and tested to assess how the proposed method classifies the fundus images into the different categories. The results demonstrated robustness to image quality and also that our method outperforms the methods proposed in the literature. Errors were noted on images presenting diabetic retinopathy, visible choroidal vessels or too much degradation caused by artefacts. In this project, we propose the first AMD severities classification robust to image quality

Optimizing Banana Type Identification: An Support Vector Machine Classification-Based Approach for Cavendish, Mas, and Tanduk Varieties

This research focuses on addressing the need for improved efficiency in the agricultural sector, particularly in banana processing in Indonesia, where the demand for bananas is consistently high. To improve the efficiency of banana processing, the research proposes the development of a machine learning based solution for automatic banana type selection. This solution uses image data of three banana types (Cavendish, Mas, and Tanduked) captured by a microscopic camera. The images are subjected to feature extraction, and a Support Vector Machine (SVM) algorithm is used to train the model. The results are implemented in a graphical user interface (GUI). The experimental results show promising results, with an accuracy of 86.67%, a precision of 87.78%, and an error rate of 13.33%, achieved with SVM parameters of C = 1000 and a linear kernel. This automated approach provides a practical and sustainable solution to the labor-intensive manual banana variety selection process, thus increasing the efficiency of the banana processing industry

Data mining based learning algorithms for semi-supervised object identification and tracking

Sensor exploitation (SE) is the crucial step in surveillance applications such as airport security and search and rescue operations. It allows localization and identification of movement in urban settings and can significantly boost knowledge gathering, interpretation and action. Data mining techniques offer the promise of precise and accurate knowledge acquisition techniques in high-dimensional data domains (and diminishing the “curse of dimensionality” prevalent in such datasets), coupled by algorithmic design in feature extraction, discriminative ranking, feature fusion and supervised learning (classification). Consequently, data mining techniques and algorithms can be used to refine and process captured data and to detect, recognize, classify, and track objects with predictable high degrees of specificity and sensitivity. Automatic object detection and tracking algorithms face several obstacles, such as large and incomplete datasets, ill-defined regions of interest (ROIs), variable scalability, lack of compactness, angular regions, partial occlusions, environmental variables, and unknown potential object classes, which work against their ability to achieve accurate real-time results. Methods must produce fast and accurate results by streamlining image processing, data compression and reduction, feature extraction, classification, and tracking algorithms. Data mining techniques can sufficiently address these challenges by implementing efficient and accurate dimensionality reduction with feature extraction to refine incomplete (ill-partitioning) data-space and addressing challenges related to object classification, intra-class variability, and inter-class dependencies. A series of methods have been developed to combat many of the challenges for the purpose of creating a sensor exploitation and tracking framework for real time image sensor inputs. The framework has been broken down into a series of sub-routines, which work in both series and parallel to accomplish tasks such as image pre-processing, data reduction, segmentation, object detection, tracking, and classification. These methods can be implemented either independently or together to form a synergistic solution to object detection and tracking. The main contributions to the SE field include novel feature extraction methods for highly discriminative object detection, classification, and tracking. Also, a new supervised classification scheme is presented for detecting objects in urban environments. This scheme incorporates both novel features and non-maximal suppression to reduce false alarms, which can be abundant in cluttered environments such as cities. Lastly, a performance evaluation of Graphical Processing Unit (GPU) implementations of the subtask algorithms is presented, which provides insight into speed-up gains throughout the SE framework to improve design for real time applications. The overall framework provides a comprehensive SE system, which can be tailored for integration into a layered sensing scheme to provide the war fighter with automated assistance and support. As more sensor technology and integration continues to advance, this SE framework can provide faster and more accurate decision support for both intelligence and civilian applications

Computer Vision for Tissue Characterization and Outcome Prediction in Cancer

The aim of this dissertation was to investigate the use of computer vision for tissue characterization and patient outcome prediction in cancer. This work focused on analysis of digitized tissue specimens, which were stained only for basic morphology (i.e. hematoxylin and eosin). The applicability of texture analysis and convolutional neural networks was evaluated for detection of biologically and clinically relevant features. Moreover, novel approaches to guide ground-truth annotation and outcome-supervised learning for prediction of patient survival directly from the tumor tissue images without expert guidance was investigated. We first studied quantification of tumor viability through segmentation of necrotic and viable tissue compartments. We developed a regional texture analysis method, which was trained and tested on whole sections of mouse xenograft models of human lung cancer. Our experiments showed that the proposed segmentation was able to discriminate between viable and non-viable tissue regions with high accuracy when compared to human expert assessment. We next investigated the feasibility of pre-trained convolutional neural networks in analysis of breast cancer tissue, aiming to quantify tumor-infiltrating lymphocytes in the specimens. Interestingly, our results showed that pre-trained convolutional neural networks can be adapted for analysis of histological image data, outperforming texture analysis. The results also indicated that the computerized assessment was on par with pathologist assessments. Moreover, the study presented an image annotation technique guided by specific antibody staining for improved ground-truth labeling. Direct outcome prediction in breast cancer was then studied using a nationwide patient cohort. A computerized pipeline, which incorporated orderless feature aggregation and convolutional image descriptors for outcome-supervised classification, resulted in a risk grouping that was predictive of both disease-specific and overall survival. Surprisingly, further analysis suggested that the computerized risk prediction was also an independent prognostic factor that provided information complementary to the standard clinicopathological factors. This doctoral thesis demonstrated how computer-vision methods can be powerful tools in analysis of cancer tissue samples, highlighting strategies for supervised characterization of tissue entities and an approach for identification of novel prognostic morphological features.Kudosnäytteiden mikroskooppisten piirteiden visuaalinen tarkastelu on yksi tärkeimmistä määrityksistä syöpäpotilaiden diagnosoinnissa ja hoidon suunnittelussa. Edistyneet kuvantamisteknologiat ovat mahdollistaneet histologisten kasvainkudosnäytteiden digitalisoinnin tarkalla resoluutiolla. Näytteiden digitalisoinnin seurauksena niiden analysointiin voidaan soveltaa edistyneitä koneoppimiseen perustuvia konenäön menetelmiä. Tämä väitöskirja tutkii konenäön menetelmien soveltamista syöpäkudosnäytteiden laskennalliseen analyysiin. Työssä tutkitaan yksittäisten histologisten entiteettien, kuten nekroottisen kudoksen ja immuunisolujen automaattista kvantifiointia. Lisäksi työssä esitellään menetelmä potilaan selviytymisen ennustamiseen pelkkään kudosmorfologiaan perustuen

Image classification : a study in age-related macular degeneration screening

This thesis presents research work conducted in the field of image mining. More specifically, the work is directed at the employment of image classification techniques to classify images where features of interest are very difficult to distinguish. In this context, three distinct approaches to image classification are proposed. The first is founded on a time series based image representation, whereby each image is defined in terms of histograms that in turn are presented as "time series" curves. A Case Based Reasoning (CBR) mechanism, coupled with a Time Series Analysis (TSA) technique, is then applied to classify new "unseen" images. The second proposed approach uses statistical parameters that are extracted from the images either directly or indirectly. These parameters are then represented in a tabular form from which a classifier can be built on. The third is founded on a tree based representation, whereby a hierarchical decomposition technique is proposed. The images are successively decomposed into smaller segments until each segment describes a uniform set of features. The resulting tree structures allow for the application of weighted frequent sub-graph mining to identify feature vectors representing each image. A standard classifier generator is then applied to this feature vector representation to produce the desired classifier. The presented evaluation, applied to all three approaches, is directed at the classification of retinal colour fundus images; the aim is to screen for an eye condition known as Age-related Macular Degeneration (AMD). Of all the approaches considered in this thesis, the tree based representation coupled with weighted frequent sub-graph mining produced the best performance. The evaluation also indicated that a sound foundation has been established for future potential AMD screening programmes

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise