AUTOMATIC SEGMENTATION OF ANATOMICAL STRUCTURES USING DEFORMABLE MODELS AND BIO-INSPIRED/SOFT COMPUTING

This PhD dissertation is focused on the development of algorithms for the automatic segmentation of anatomical structures in biomedical images, usually the hippocampus in histological images from the mouse brain. Such algorithms are based on computer vision techniques and artificial intelligence methods. More precisely, on the one hand, we take advantage of deformable models to segment the anatomical structure under consideration, using prior knowledge from different sources, and to embed the segmentation into an optimization framework. On the other hand, metaheuristics and classifiers can be used to perform the optimization of the target function defined by the shape model (as well as to automatically tune the system parameters), and to refine the results obtained by the segmentation process, respectively. Three new different methods, with their corresponding advantages and disadvantages, are described and tested. A broad theoretical discussion, together with an extensive introduction to the state of the art, has also been included to provide an overview necessary for understanding the developed methods

Automatic segmentation of anatomical structures using deformable models and bio-inspired/soft computing

Advisor: Stefano Cagnoni. Date and location of PhD thesis defense: 10 March 2014, University of ParmaThis PhD dissertation is focused on the development of algorithms for the automatic segmentation of anatomical structures in biomedical images, usually the hippocampus in histological images from the mouse brain. Such algorithms are based on computer vision techniques and artificial intelligence methods. More precisely, on the one hand, we take advantage of deformable models to segment the anatomical structure under consideration, using prior knowledge from different sources, and to embed the segmentation into an optimization framework. On the other hand, metaheuristics and classifiers can be used to perform the optimization of the target function defined by the shape model (as well as to automatically tune the system parameters), and to refine the results obtained by the segmentation process, respectively. Three new different methods, with their corresponding advantages and disadvantages, are described and tested. A broad theoretical discussion, together with an extensive introduction to the state of the art, has also been included to provide an overview necessary for understanding the developed methods

Meta-optimization of Bio-inspired Techniques for Object Recognition

Il riconoscimento di oggetti consiste nel trovare automaticamente un oggetto all'interno di un'immagine o in una sequenza video. Questo compito è molto importante in molti campi quali diagnosi mediche, assistenza di guida avanzata, visione artificiale, sorveglianza, realtà aumentata. Tuttavia, questo compito può essere molto impegnativo a causa di artefatti (dovuti al sistema di acquisizione, all'ambiente o ad altri effetti ottici quali prospettiva, variazioni di illuminazione, etc.) che possono influenzare l'aspetto anche di oggetti facili da identificare e ben definiti . Una possibile tecnica per il riconoscimento di oggetti consiste nell'utilizzare approcci basati su modello: in questo scenario viene creato un modello che rappresenta le proprietà dell'oggetto da individuare; poi, vengono generate possibili ipotesi sul posizionamento dell'oggetto, e il modello viene trasformato di conseguenza, fino a trovare la migliore corrispondenza con l'aspetto reale dell'oggetto. Per generare queste ipotesi in maniera intelligente, è necessario un buon algoritmo di ottimizzazione. Gli algoritmi di tipo bio-ispirati sono metodi di ottimizzazione che si basano su proprietà osservate in natura (quali cooperazione, evoluzione, socialità). La loro efficacia è stata dimostrata in molte attività di ottimizzazione, soprattutto in problemi di difficile soluzione, multi-modali e multi-dimensionali quali, per l'appunto, il riconoscimento di oggetti. Anche se queste euristiche sono generalmente efficaci, esse dipendono da molti parametri che influenzano profondamente le loro prestazioni; pertanto, è spesso richiesto uno sforzo significativo per capire come farle esprimere al massimo delle loro potenzialità. Questa tesi descrive un metodo per (i) individuare automaticamente buoni parametri per tecniche bio-ispirate, sia per un problema specifico che più di uno alla volta, e (ii) acquisire maggior conoscenza sul ruolo di un parametro in questi algoritmi. Inoltre, viene mostrato come le tecniche bio-ispirate possono essere applicate con successo in diversi ambiti nel riconoscimento di oggetti, e come è possibile migliorare ulteriormente le loro prestazioni mediante il tuning automatico dei loro parametri.Object recognition is the task of automatically finding a given object in an image or in a video sequence. This task is very important in many fields such as medical diagnosis, advanced driving assistance, image understanding, surveillance, virtual reality. Nevertheless, this task can be very challenging because of artefacts (related with the acquisition system, the environment or other optical effects like perspective, illumination changes, etc.) which may affect the aspect even of easy-to-identify and well-defined objects. A possible way to achieve object recognition is using model-based approaches: in this scenario a model (also called template) representing the properties of the target object is created; then, hypotheses on the position of the object are generated, and the model is transformed accordingly, until the best match with the actual appearance of the object is found. To generate these hypotheses intelligently, a good optimization algorithm is required. Bio-inspired techniques are optimization methods whose foundations rely on properties observed in nature (such as cooperation, evolution, emergence). Their effectiveness has been proved in many optimization tasks, especially in multi-modal, multi-dimensional hard problems like object recognition. Although these heuristics are generally effective, they depend on many parameters that strongly affect their performances; therefore, a significant effort must be spent to understand how to let them express their full potentialities. This thesis describes a method to (i) automatically find good parameters for bio-inspired techniques, both for a specific problem and for more than one at the same time, and (ii) acquire more knowledge of a parameter's role in such algorithms. Then, it shows how bio-inspired techniques can be successfully applied to different object recognition tasks, and how it is possible to further improve their performances by means of automatic parameter tuning