Texture analysis and Its applications in biomedical imaging: a survey

Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications. This survey’s emphasis is in collecting and categorising over five decades of active research on texture analysis.Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this survey’s final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.Manuscript received February 3, 2021; revised June 23, 2021; accepted September 21, 2021. Date of publication September 27, 2021; date of current version January 24, 2022. This work was supported in part by the Portuguese Foundation for Science and Technology (FCT) under Grants PTDC/EMD-EMD/28039/2017, UIDB/04950/2020, PestUID/NEU/04539/2019, and CENTRO-01-0145-FEDER-000016 and by FEDER-COMPETE under Grant POCI-01-0145-FEDER-028039. (Corresponding author: Rui Bernardes.)info:eu-repo/semantics/publishedVersio

Deep learning of brain asymmetry digital biomarkers to support early diagnosis of cognitive decline and dementia

Early identification of degenerative processes in the human brain is essential for proper care and treatment. This may involve different instrumental diagnostic methods, including the most popular computer tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. These technologies provide detailed information about the shape, size, and function of the human brain. Structural and functional cerebral changes can be detected by computational algorithms and used to diagnose dementia and its stages (amnestic early mild cognitive impairment - EMCI, Alzheimer’s Disease - AD). They can help monitor the progress of the disease. Transformation shifts in the degree of asymmetry between the left and right hemispheres illustrate the initialization or development of a pathological process in the brain. In this vein, this study proposes a new digital biomarker for the diagnosis of early dementia based on the detection of image asymmetries and crosssectional comparison of NC (normal cognitively), EMCI and AD subjects. Features of brain asymmetries extracted from MRI of the ADNI and OASIS databases are used to analyze structural brain changes and machine learning classification of the pathology. The experimental part of the study includes results of supervised machine learning algorithms and transfer learning architectures of convolutional neural networks for distinguishing between cognitively normal subjects and patients with early or progressive dementia. The proposed pipeline offers a low-cost imaging biomarker for the classification of dementia. It can be potentially helpful to other brain degenerative disorders accompanied by changes in brain asymmetries

Sparse feature learning for image analysis in segmentation, classification, and disease diagnosis.

The success of machine learning algorithms generally depends on intermediate data representation, called features that disentangle the hidden factors of variation in data. Moreover, machine learning models are required to be generalized, in order to reduce the specificity or bias toward the training dataset. Unsupervised feature learning is useful in taking advantage of large amount of unlabeled data, which is available to capture these variations. However, learned features are required to capture variational patterns in data space. In this dissertation, unsupervised feature learning with sparsity is investigated for sparse and local feature extraction with application to lung segmentation, interpretable deep models, and Alzheimer\u27s disease classification. Nonnegative Matrix Factorization, Autoencoder and 3D Convolutional Autoencoder are used as architectures or models for unsupervised feature learning. They are investigated along with nonnegativity, sparsity and part-based representation constraints for generalized and transferable feature extraction

Representation learning for histopathology image analysis

Abstract. Nowadays, automatic methods for image representation and analysis have been successfully applied in several medical imaging problems leading to the emergence of novel research areas like digital pathology and bioimage informatics. The main challenge of these methods is to deal with the high visual variability of biological structures present in the images, which increases the semantic gap between their visual appearance and their high level meaning. Particularly, the visual variability in histopathology images is also related to the noise added by acquisition stages such as magnification, sectioning and staining, among others. Many efforts have focused on the careful selection of the image representations to capture such variability. This approach requires expert knowledge as well as hand-engineered design to build good feature detectors that represent the relevant visual information. Current approaches in classical computer vision tasks have replaced such design by the inclusion of the image representation as a new learning stage called representation learning. This paradigm has outperformed the state-of-the-art results in many pattern recognition tasks like speech recognition, object detection, and image scene classification. The aim of this research was to explore and define a learning-based histopathology image representation strategy with interpretative capabilities. The main contribution was a novel approach to learn the image representation for cancer detection. The proposed approach learns the representation directly from a Basal-cell carcinoma image collection in an unsupervised way and was extended to extract more complex features from low-level representations. Additionally, this research proposed the digital staining module, a complementary interpretability stage to support diagnosis through a visual identification of discriminant and semantic features. Experimental results showed a performance of 92% in F-Score, improving the state-of-the-art representation by 7%. This research concluded that representation learning improves the feature detectors generalization as well as the performance for the basal cell carcinoma detection task. As additional contributions, a bag of features image representation was extended and evaluated for Alzheimer detection, obtaining 95% in terms of equal error classification rate. Also, a novel perspective to learn morphometric measures in cervical cells based on bag of features was presented and evaluated obtaining promising results to predict nuclei and cytoplasm areas.Los métodos automáticos para la representación y análisis de imágenes se han aplicado con éxito en varios problemas de imagen médica que conducen a la aparición de nuevas áreas de investigación como la patología digital. El principal desafío de estos métodos es hacer frente a la alta variabilidad visual de las estructuras biológicas presentes en las imágenes, lo que aumenta el vacío semántico entre su apariencia visual y su significado de alto nivel. Particularmente, la variabilidad visual en imágenes de histopatología también está relacionada con el ruido añadido por etapas de adquisición tales como magnificación, corte y tinción entre otros. Muchos esfuerzos se han centrado en la selección de la representacion de las imágenes para capturar dicha variabilidad. Este enfoque requiere el conocimiento de expertos y el diseño de ingeniería para construir buenos detectores de características que representen la información visual relevante. Los enfoques actuales en tareas de visión por computador han reemplazado ese diseño por la inclusión de la representación en la etapa de aprendizaje. Este paradigma ha superado los resultados del estado del arte en muchas de las tareas de reconocimiento de patrones tales como el reconocimiento de voz, la detección de objetos y la clasificación de imágenes. El objetivo de esta investigación es explorar y definir una estrategia basada en el aprendizaje de la representación para imágenes histopatológicas con capacidades interpretativas. La contribución principal de este trabajo es un enfoque novedoso para aprender la representación de la imagen para la detección de cáncer. El enfoque propuesto aprende la representación directamente de una colección de imágenes de carcinoma basocelular en forma no supervisada que permite extraer características más complejas a partir de las representaciones de bajo nivel. También se propone el módulo de tinción digital, una nueva etapa de interpretabilidad para apoyar el diagnóstico a través de una identificación visual de las funciones discriminantes y semánticas. Los resultados experimentales mostraron un rendimiento del 92% en términos de F-Score, mejorando la representación del estado del arte en un 7%. Esta investigación concluye que el aprendizaje de la representación mejora la generalización de los detectores de características así como el desempeño en la detección de carcinoma basocelular. Como contribuciones adicionales, una representación de bolsa de caracteristicas (BdC) fue ampliado y evaluado para la detección de la enfermedad de Alzheimer, obteniendo un 95% en términos de EER. Además, una nueva perspectiva para aprender medidas morfométricas en las células del cuello uterino basado en BdC fue presentada y evaluada obteniendo resultados prometedores para predecir las areás del nucleo y el citoplasma.Maestrí

Apathy Classification by Exploiting Task Relatedness

International audienceApathy is characterized by symptoms such as reduced emotional response, lack of motivation, and limited social interaction. Current methods for apathy diagnosis require the pa-tient's presence in a clinic and time consuming clinical interviews, which are costly and inconvenient for both patients and clinical staff, hindering among others large-scale diagnostics. In this work we propose a multi-task learning (MTL) framework for apathy classification based on facial analysis, entailing both emotion and facial movements. In addition, it leverages information from other auxiliary tasks (i.e., clinical scores), which might be closely or distantly related to the main task of apathy classification. Our proposed MTL approach (termed MTL+) improves apathy classification by jointly learning model weights and the relatedness of the auxiliary tasks to the main task in an iterative manner. Our results on 90 video sequences acquired from 45 subjects obtained an apathy classification accuracy of up to 80%, using the concatenated emotion and motion features. Our results further demonstrate the improved performance of MTL+ over MTL

Characterizing the State of Apathy with Facial Expression and Motion Analysis

International audienceReduced emotional response, lack of motivation, and limited social interaction comprise the major symptoms of apathy. Current methods for apathy diagnosis require the patient's presence in a clinic, and time consuming clinical interviews and questionnaires involving medical personnel, which are costly and logistically inconvenient for patients and clinical staff, hindering among other large scale diagnostics. In this paper we introduce a novel machine learning framework to classify apathetic and non-apathetic patients based on analysis of facial dynamics, entailing both emotion and facial movement. Our approach caters to the challenging setting of current apathy assessment interviews, which include short video clips with wide face pose variations, very low-intensity expressions, and insignificant inter-class variations. We test our algorithm on a dataset consisting of 90 video sequences acquired from 45 subjects and obtained an accuracy of 84% in apathy classification. Based on extensive experiments, we show that the fusion of emotion and facial local motion produces the best feature set for apathy classification. In addition, we train regression models to predict the clinical scores related to the mental state examination (MMSE) and the neuropsychiatric apathy inventory (NPI) using the motion and emotion features. Our results suggest that the performance can be further improved by appending the predicted clinical scores to the video-based feature representation

Classification of Alzheimer's Disease and Mild Cognitive Impairment Using Longitudinal FDG-PET Images

RÉSUMÉ La maladie d’Alzheimer (MA) est la principale cause de maladies dégénératives et se caractérise par un début insidieux, une perte de mémoire précoce, des déficits verbaux et visuo-spatiaux (associés à la destruction des lobes temporal et pariétal), un développement progressif et une absence de signes neurologiques tôt dans l’apparition de la maladie. Aucun traitement n’est disponible en ce moment pour guérir la MA. Les traitements actuels peuvent souvent ralentir de façon significative la progression de la maladie. La capacité de diagnostiquer la MA à son stade initial a un impact majeur sur l’intervention clinique et la planification thérapeutique, réduisant ainsi les coûts associés aux soins de longue durée. La distinction entre les différents stades de la démence est essentielle afin de ralentir la progression de la MA. La différenciation entre les patients ayant la MA, une déficience cognitive légère précoce (DCLP), une déficience cognitive légère tardive (DCLT) ou un état cognitif normal (CN) est un domaine de recherche qui a suscité beaucoup d’intérêt durant la dernière décennie. Les images obtenues par tomographie par émission de positrons (TEP) font partie des meilleures méthodes accessibles pour faciliter la distinction entre ces différentes classes. Du point de vue de la neuro-imagerie, les images TEP par fluorodésoxyglucose (FDG) pour le métabolisme cérébral du glucose et pour les plaques amyloïdes (AV45) sont considérées comme des biomarqueurs ayant une puissance diagnostique élevée. Cependant, seules quelques approches ont étudié l’efficacité de considérer uniquement les zones actives localisées par la TEP à des fins de classification. La question de recherche principale de ce travail est de démontrer la capacité des images TEP à classer les résultats de façon précise et de comparer les résultats de deux méthodes d’imagerie TEP (FDG et AV45). Afin de déterminer la meilleure façon de classer les sujets dans les catégories MA, DCLP, DCLT ou CN en utilisant exclusivement les images TEP, nous proposons une procédure qui utilise les caractéristiques apprises à partir d’images TEP identifiées sémantiquement. Les machines à vecteurs de support (MVS) sont déjà utilisées pour faire de nombreuses classifications et font partie des techniques les plus utilisées pour la classification basée sur la neuro-imagerie, comme pour la MA. Les MVS linéaires et la fonction de base radiale (FBR)-MVS sont deux noyaux populaires utilisés dans notre classification. L’analyse en composante principale (ACP) est utilisée pour diminuer la taille des données suivie par les MVS linéaires qui sont une autre méthode de classification. Les forêts d’arbres décisionnels (FAD) sont aussi exécutées pour rendre les résultats obtenus par MVS comparables. L’objectif général de ce travail est de concevoir un ensemble d’outils déjà existants pour classer la MA et les différents stades de DCL. Suivant les étapes de normalisation et de prétraitement, une méthode d’enregistrement TEP-IRM ultimodale et déformable est proposée afin de fusionner l’atlas du MNI au scan TEP de chaque patient et de développer une méthode simple de segmentation basée sur l’atlas du cerveau dans le but de générer un volume étiqueté avec 10 régions d’intérêt communes. La procédure a deux approches : la première utilise l’intensité des voxels des régions d’intérêt, et la seconde, l’intensité des voxels du cerveau en entier. La méthode a été testée sur 660 sujets provenant de la base de données de l’(Alzheimer’s Disease Neuroimaging Initiative) et a été comparée à une approche qui incluait le cerveau en entier. La précision de la classification entre la MA et les CN a été mesurée à 91,7% et à 91,2% en utilisant la FBR et les FAD, respectivement, sur des données combinant les caractéristiques multirégionales des FDG-TEP des examens transversal et de suivi. Une amélioration considérable a été notée pour la précision de classification entre les DCLP et DCLT avec un taux de 72,5%. La précision de classification entre la MA et les CN en utilisant AV45-TEP avec les données combinées a été mesurée à 90,8% et à 87,9% pour la FBR et les FAD, respectivement. Cette procédure démontre le potentiel des caractéristiques multirégionales de la TEP pour améliorer l’évaluation cognitive. Les résultats observés confirment qu’il est possible de se fier uniquement aux images TEP sans ajout d’autres bio-marqueurs pour obtenir une précision de classification élevée.----------ABSTRACT Alzheimer’s disease (AD) is the most general cause of degenerative dementia, characterized by insidious onset early memory loss, language and visuospatial deficits (associated with the destruction of the temporal and parietal lobes), a progressive course, and lack of early neurological signs early in the course of disease. There is currently no absolute cure for AD but some treatments can slow down the progression of the disease in early stages of AD. The ability to diagnose AD at an early stage has a great impact on the clinical intervention and treatment planning, and hence reduces costs associated with long-term care. In addition, discrimination of different stages of dementia is crucial to slow down the progression of AD. Distinguishing patients with AD, early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and normal controls (NC) is an extremely active research area, which has garnered significant attention in the past decade. Positron emission tomography (PET) images are one of the best accessible ways to discriminate between different classes. From a neuroimaging point of view, PET images of fluorodeoxyglucose (FDG) for cerebral glucose metabolism and amyloid plaque images (AV45) are considered a highly powerful diagnostic biomarker, but few approaches have investigated the efficacy of focusing on localized PETactive areas for classification purposes. The main research question of this work is to show the ability of using PET images to achieve accurate classification results and to compare the results of two imaging methods of PET (FDG and AV45). To find the best scenario to classify our subjects into AD, EMCI, LMCI, and NC using PET images exclusively, we proposed a pipeline using learned features from semantically labelled PET images to perform group classification using four classifiers. Support vector machines (SVMs) are already applied in a wide variety of classifications, and it is one of the most popular techniques in classification based on neuroimaging like AD. Linear SVMs and radial basis function (RBF) SVMs are two common kernels used in our classification. Principal component analysis (PCA) is used to reduce the dimension of our data followed by linear SVMs, which is another method of classification. Random forest (RF) is also applied to make our SVM results comparable. The general objective of this work is to design a set of existing tools for classifying AD and different stages of MCI. Following normalization and pre-processing steps, a multi-modal PET-MRI registration method is proposed to fuse the Montreal Neurological Institute (MNI) atlas to PET images of each patient which is registered to its corresponding MRI scan, developing a simple method of segmentation based on a brain atlas generated from a fully labelled volume with 10 common regions of interest (ROIs). This pipeline can be used in two ways: (1) using voxel intensities from specific regions of interest (multi-region approach), and (2) using voxel intensities from the entire brain (whole brain approach). The method was tested on 660 subjects from the Alzheimer’s Disease Neuroimaging Initiative database and compared to a whole-brain approach. The classification accuracy of AD vs NC was measured at 91.7 % and 91.2 % when using RBF-SVM and RF, respectively, on combining both multi-region features from FDG-PET on cross-sectional and follow-up exams. A considerable improvement compare to the similar works in the EMCI vs LMCI classification accuracy was achieved at 72.5 %. The classification accuracy of AD versus NC using AV45-PET on the combined data was measured at 90.8 % and 87.9 % using RBF-SVM and RF, respectively. The pipeline demonstrates the potential of exploiting longitudinal multi-region PET features to improve cognitive assessment. We can achieve high accuracy using only PET images. This suggests that PET images are a rich source of discriminative information for this task. We note that other methods rely on the combination of multiple sources