Using noise models to estimate rank parameters for rank order greyscale hit-or-miss transforms

The Hit-or-Miss Transform (HMT) is a morphological transform which can be used for template matching and other applications. Recent developments of the HMT include extensions of the transform for application to greyscale images as well as a variety of techniques aiming to improve its noise robustness. One popular technique for improving noise robustness is to use rank order filters in place of the traditional morphological operations of erosion and dilation. However, very few authors give consideration to developing generic techniques for estimating the rank parameters they introduce. Very recently, techniques which use ROC curves, or the SEs designed for object detection, have been presented for estimating optimal values for the rank parameter. This paper presents a new, simpler technique which uses noise models extracted from the image set under study to estimate the optimal rank parameter

Classification of De novo post-operative and persistent atrial fibrillation using multi-channel ECG recordings

Atrial fibrillation (AF) is the most sustained arrhythmia in the heart and also the most common complication developed after cardiac surgery. Due to its progressive nature, timely detection of AF is important. Currently, physicians use a surface electrocardiogram (ECG) for AF diagnosis. However, when the patient develops AF, its various development stages are not distinguishable for cardiologists based on visual inspection of the surface ECG signals. Therefore, severity detection of AF could start from differentiating between short-lasting AF and long-lasting AF. Here, de novo post-operative AF (POAF) is a good model for short-lasting AF while long-lasting AF can be represented by persistent AF. Therefore, we address in this paper a binary severity detection of AF for two specific types of AF. We focus on the differentiation of these two types as de novo POAF is the first time that a patient develops AF. Hence, comparing its development to a more severe stage of AF (e.g., persistent AF) could be beneficial in unveiling the electrical changes in the atrium. To the best of our knowledge, this is the first paper that aims to differentiate these different AF stages. We propose a method that consists of three sets of discriminative features based on fundamentally different aspects of the multi-channel ECG data, namely based on the analysis of RR intervals, a greyscale image representation of the vectorcardiogram, and the frequency domain representation of the ECG. Due to the nature of AF, these features are able to capture both morphological and rhythmic changes in the ECGs. Our classification system consists of a random forest classifier, after a feature selection stage using the ReliefF method. The detection efficiency is tested on 151 patients using 5-fold cross-validation. We achieved 89.07% accuracy in the classification of de novo POAF and persistent AF. The results show that the features are discriminative to reveal the severity of AF. Moreover, inspection of the most important features sheds light on the different characteristics of de novo post-operative and persistent AF.</p

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Contributions en morphologie mathématique pour l'analyse d'images multivariées

This thesis contributes to the field of mathematical morphology and illustrates how multivariate statistics and machine learning techniques can be exploited to design vector ordering and to include results of morphological operators in the pipeline of multivariate image analysis. In particular, we make use of supervised learning, random projections, tensor representations and conditional transformations to design new kinds of multivariate ordering, and morphological filters for color and multi/hyperspectral images. Our key contributions include the following points:• Exploration and analysis of supervised ordering based on kernel methods.• Proposition of an unsupervised ordering based on statistical depth function computed by random projections. We begin by exploring the properties that an image requires to ensure that the ordering and the associated morphological operators can be interpreted in a similar way than in the case of grey scale images. This will lead us to the notion of background/foreground decomposition. Additionally, invariance properties are analyzed and theoretical convergence is showed.• Analysis of supervised ordering in morphological template matching problems, which corresponds to the extension of hit-or-miss operator to multivariate image by using supervised ordering.• Discussion of various strategies for morphological image decomposition, specifically, the additive morphological decomposition is introduced as an alternative for the analysis of remote sensing multivariate images, in particular for the task of dimensionality reduction and supervised classification of hyperspectral remote sensing images.• Proposition of an unified framework based on morphological operators for contrast enhancement and salt- and-pepper denoising.• Introduces a new framework of multivariate Boolean models using a complete lattice formulation. This theoretical contribution is useful for characterizing and simulation of multivariate textures.Cette thèse contribue au domaine de la morphologie mathématique et illustre comment la statistique multivariée et les techniques d'apprentissage numérique peuvent être exploitées pour concevoir un ordre dans l'espace des vecteurs et pour inclure les résultats d'opérateurs morphologiques au processus d'analyse d'images multivariées. En particulier, nous utilisons l'apprentissage supervisé, les projections aléatoires, les représentations tensorielles et les transformations conditionnelles pour concevoir de nouveaux types d'ordres multivariés et de nouveaux filtres morphologiques pour les images multi/hyperspectrales. Nos contributions clés incluent les points suivants :• Exploration et analyse d'ordre supervisé, basé sur les méthodes à noyaux.• Proposition d'un ordre nonsupervisé, basé sur la fonction de profondeur statistique calculée par projections aléatoires. Nous commençons par explorer les propriétés nécessaires à une image pour assurer que l'ordre ainsi que les opérateurs morphologiques associés, puissent être interprétés de manière similaire au cas d'images en niveaux de gris. Cela nous amènera à la notion de décomposition en arrière plan. De plus, les propriétés d'invariance sont analysées et la convergence théorique est démontrée.• Analyse de l'ordre supervisé dans les problèmes de correspondance morphologique de patrons, qui correspond à l'extension de l'opérateur tout-ou-rien aux images multivariées grâce à l‘utilisation de l'ordre supervisé.• Discussion sur différentes stratégies pour la décomposition morphologique d'images. Notamment, la décomposition morphologique additive est introduite comme alternative pour l'analyse d'images de télédétection, en particulier pour les tâches de réduction de dimension et de classification supervisée d'images hyperspectrales de télédétection.• Proposition d'un cadre unifié basé sur des opérateurs morphologiques, pour l'amélioration de contraste et pour le filtrage du bruit poivre-et-sel.• Introduction d'un nouveau cadre de modèles Booléens multivariés en utilisant une formulation en treillis complets. Cette contribution théorique est utile pour la caractérisation et la simulation de textures multivariées

Investigating the effects of a combined spatial and spectral dimensionality reduction approach for aerial hyperspectral target detection applications

Target detection and classification is an important application of hyperspectral imaging in remote sensing. A wide range of algorithms for target detection in hyperspectral images have been developed in the last few decades. Given the nature of hyperspectral images, they exhibit large quantities of redundant information and are therefore compressible. Dimensionality reduction is an effective means of both compressing and denoising data. Although spectral dimensionality reduction is prevalent in hyperspectral target detection applications, the spatial redundancy of a scene is rarely exploited. By applying simple spatial masking techniques as a preprocessing step to disregard pixels of definite disinterest, the subsequent spectral dimensionality reduction process is simpler, less costly and more informative. This paper proposes a processing pipeline to compress hyperspectral images both spatially and spectrally before applying target detection algorithms to the resultant scene. The combination of several different spectral dimensionality reduction methods and target detection algorithms, within the proposed pipeline, are evaluated. We find that the Adaptive Cosine Estimator produces an improved F1 score and Matthews Correlation Coefficient when compared to unprocessed data. We also show that by using the proposed pipeline the data can be compressed by over 90% and target detection performance is maintained

Dynamical models and machine learning for supervised segmentation

This thesis is concerned with the problem of how to outline regions of interest in medical images, when the boundaries are weak or ambiguous and the region shapes are irregular. The focus on machine learning and interactivity leads to a common theme of the need to balance conflicting requirements. First, any machine learning method must strike a balance between how much it can learn and how well it generalises. Second, interactive methods must balance minimal user demand with maximal user control. To address the problem of weak boundaries,methods of supervised texture classification are investigated that do not use explicit texture features. These methods enable prior knowledge about the image to benefit any segmentation framework. A chosen dynamic contour model, based on probabilistic boundary tracking, combines these image priors with efficient modes of interaction. We show the benefits of the texture classifiers over intensity and gradient-based image models, in both classification and boundary extraction. To address the problem of irregular region shape, we devise a new type of statistical shape model (SSM) that does not use explicit boundary features or assume high-level similarity between region shapes. First, the models are used for shape discrimination, to constrain any segmentation framework by way of regularisation. Second, the SSMs are used for shape generation, allowing probabilistic segmentation frameworks to draw shapes from a prior distribution. The generative models also include novel methods to constrain shape generation according to information from both the image and user interactions. The shape models are first evaluated in terms of discrimination capability, and shown to out-perform other shape descriptors. Experiments also show that the shape models can benefit a standard type of segmentation algorithm by providing shape regularisers. We finally show how to exploit the shape models in supervised segmentation frameworks, and evaluate their benefits in user trials

Automatic Multilevel Feature Abstraction in Adaptable Machine Vision Systems

Vision is a complex task which can be accomplished with apparent ease by biological systems, but for which the design of artificial systems is difficult. Although machine vision systems can be successfully designed for a specific task, under certain conditions, they are likely to fail if circumstances change. This was the motivation for the research into ways in which systems can be self-designing and adaptable to new visual tasks. The research was conducted in three vital areas of concern for machine vision systems. The first area is finding a suitable architecture for forming an appropriate representation for the current task. The research investigated the application of Hypernetworks theory to building a multilevel, generally-applicable representation, through repeated application of a fundamental 'self-similarity' principle, that parts of objects assembled under a particular relation at one level, form whole objects at the next. Results show that this is potentially a powerful approach for autonomously generating an adaptable system-architecture suitable for multiple visual tasks. The second area is the autonomous extraction of suitable low-level features, which the research investigated through random generation of minimally-constrained pixel-configurations and algorithmic generation of homogeneous and heterogeneous polygons. The results suggest that, despite the simplicity of the features making them vulnerable to image transformations, these are promising approaches worth developing further. The third area is automatic feature selection. The research explored management of 'dimensionality' and of 'combinatorial explosion', as well as how to locate relevant features at multiple representation levels, in the context of 'emergence' of structure. Results indicate that this approach can find useful 'intermediate-level' constructs through analysis of the connectivity of the simplices representing objects at higher levels. The research concludes that the proposed novel approaches to tackling the above issues, in particular the application of hypernetworks to the formation of multilevel representations and the resulting emergence of higher-level structure, is fruitful

Computer aided classification of histopathological damage in images of haematoxylin and eosin stained human skin

EngD ThesisExcised human skin can be used as a model to assess the potency, immunogenicity and contact sensitivity of potential therapeutics or cosmetics via the assessment of histological damage. The current method of assessing the damage uses traditional manual histological assessment, which is inherently subjective, time consuming and prone to intra-observer variability. Computer aided analysis has the potential to address issues surrounding traditional histological techniques through the application of quantitative analysis. This thesis describes the development of a computer aided process to assess the immune-mediated structural breakdown of human skin tissue. Research presented includes assessment and optimisation of image acquisition methodologies, development of an image processing and segmentation algorithm, identification and extraction of a novel set of descriptive image features and the evaluation of a selected subset of these features in a classification model. A new segmentation method is presented to identify epidermis tissue from skin with varying degrees of histopathological damage. Combining enhanced colour information with general image intensity information, the fully automated methodology segments the epidermis with a mean specificity of 97.7%, a mean sensitivity of 89.4% and a mean accuracy of 96.5% and segments effectively for different severities of tissue damage. A set of 140 feature measurements containing information about the tissue changes associated with different grades of histopathological skin damage were identified and a wrapper algorithm employed to select a subset of the extracted features, evaluating feature subsets based their prediction error for an independent test set in a Naïve Bayes Classifier. The final classification algorithm classified a 169 image set with an accuracy of 94.1%, of these images 20 were an unseen validation set for which the accuracy was 85.0%. The final classification method has a comparable accuracy to the existing manual method, improved repeatability and reproducibility and does not require an experienced histopathologist