Retinal imaging tool for assessment of the parapapillary atrophy and the optic disc

Ophthalmic diseases such as glaucoma are associated with progressive changes in the structure of the optic disc (OD) and parapapillary atrophy (PPA). These structural changes may therefore have relevance to other systemic diseases. The size and location of OD and PPA can be used as registration landmarks for monitoring changes in features of the fundus of the eye. Retinal vessel evaluation, for example, can be used as a biomarker for the effects of multiple systemic diseases, or co-morbidities. This thesis presents the first computer-aided measuring tool that detects and quantifies the progression of PPA automatically on a 2D retinal fundus image in the presence of image noise. An automated segmentation system is described that can detect features of the optic nerve. Three novel approaches are explored that extract the PPA and OD region approximately from a 2D fundus image. The OD region is segmented using (i) a combination of active contour and morphological operations, (ii) a modified Chan-Vese algorithm and (iii) a combination of edge detection and ellipse fitting methods. The PPA region is identified from the presence of bright pixels in the temporal zone of the OD, and segmented using a sequence of techniques, including a modified Chan-Vese approach, thresholding, scanning filter and multi-seed region growing methods. The work demonstrates for the first time how the OD and PPA regions can be identified and quantified from 2D fundus images using a standard fundus camera

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

Psr1p interacts with SUN/sad1p and EB1/mal3p to establish the bipolar spindle

Regular Abstracts - Sunday Poster Presentations: no. 382During mitosis, interpolar microtubules from two spindle pole bodies (SPBs) interdigitate to create an antiparallel microtubule array for accommodating numerous regulatory proteins. Among these proteins, the kinesin-5 cut7p/Eg5 is the key player responsible for sliding apart antiparallel microtubules and thus helps in establishing the bipolar spindle. At the onset of mitosis, two SPBs are adjacent to one another with most microtubules running nearly parallel toward the nuclear envelope, creating an unfavorable microtubule configuration for the kinesin-5 kinesins. Therefore, how the cell organizes the antiparallel microtubule array in the first place at mitotic onset remains enigmatic. Here, we show that a novel protein psrp1p localizes to the SPB and plays a key role in organizing the antiparallel microtubule array. The absence of psr1+ leads to a transient monopolar spindle and massive chromosome loss. Further functional characterization demonstrates that psr1p is recruited to the SPB through interaction with the conserved SUN protein sad1p and that psr1p physically interacts with the conserved microtubule plus tip protein mal3p/EB1. These results suggest a model that psr1p serves as a linking protein between sad1p/SUN and mal3p/EB1 to allow microtubule plus ends to be coupled to the SPBs for organization of an antiparallel microtubule array. Thus, we conclude that psr1p is involved in organizing the antiparallel microtubule array in the first place at mitosis onset by interaction with SUN/sad1p and EB1/mal3p, thereby establishing the bipolar spindle.postprin

Computer simulation of a neurological model of learning

A number of problems in psychology and neurology are discussed to orient the reader to a theory of neural integration. The importance is stressed of the comprehensive temporal and spatial integration of sensory, motor and motivational aspects of brain function. It is argued that an extended neural template theory could provide such an integration. Contemporary solutions to the problem of neural integration are discussed. The available knowledge concerning the structure of neural tissue leads to the description of a theory of neural integration which might provide such neural templates. Integrating Neurons are suggested to be organised in columns or pools. Sub-sets of Neurons are formed as a result of excitation and can preferentially exchange excitation. These sub-sets or Linked Constellations would act as spatial templates to be matched with subsequent states of excitation. Inhibition acts to restrict spike emission to the most highly activated sub-sets. An initial computer simulation represented a simple learning or classical conditioning situation. In a variety of test computer runs the performance confirmed the main predictions of the theoretical model. The model was then extended to include representation of instrumental, consummatory, motivational and other aspects of behaviour. The intention of these further simulations was not to demonstrate the predictions of prior formulations but rather to use the computer to develop simulations progressively able to represent behaviour. Difficulties were encountered which were remedied by incorporating rhythmic mechanisms. A number of different versions of the model were explored. It was shown that the models could be trained to produced a different response to discriminative cues, when those cues had previously signalled different contingencies of obtaining the opportunity to perform consummatory behaviour. A published experiment on the Spiral Illusion is reported, which confirmed predictions suggested by the model.<p