(SEMI)-AUTOMATED ANALYSIS OF MELANOCYTIC LESIONS

Melanoma is a very aggressive form of skin cancer whose incidence has constantly grown in the last 50 years. To increase the survival rate, an early diagnosis followed by a prompt excision is crucial and requires an accurate and periodic analysis of the patient's melanocytic lesions. We have developed an hardware and software solution named Mole Mapper to assist the dermatologists during the diagnostic process. The goal is to increase the accuracy of the diagnosis, accelerating the entire process at the same time. This is achieved through an automated analysis of the dermatoscopic images which computes and highlights the proper information to the dermatologist. In this thesis we present the 3 main algorithms that have been implemented into the Mole Mapper: A robust segmentation of the melanocytic lesion, which is the starting point for any other image processing algorithm and which allows the extraction of useful information about the lesion's shape and size. It outperforms the speed and quality of other state-of-the-art methods, with a precision that meets a Senior Dermatologist's standard and an execution time that allows for real-time video processing; A virtual shaving algorithm, which increases the precision and robustness of the other computer vision algorithms and provides the dermatologist with a hair-free image to be used during the evaluation process. It matches the quality of state-of-the-art methods but requires only a fraction of the computational time, allowing for computation on a mobile device in a time-frame compatible with an interactive GUI; A registration algorithm through which to study the evolution of the lesion over time, highlighting any unexpected anomalies and variations. Since a standard approach to this problem has not yet been proposed, we define the scope and constraints of the problem; we analyze the results and issues of standard registration techniques; and finally, we propose an algorithm with a speed compatible with Mole Mapper's constraints and with an accuracy comparable to the registration performed by a human operator

Paripari: Connectivity optimization

PariPari è una piattaforma P2P multifunzionale ed estensibile. Questa tesi descrive come PariConnectivity, il modulo di PariPari che gestisce le comunicazioni di rete, sia stato completamente riprogettato e ottimizzato: lo sviluppo di un nuovo insieme di API, fondate sulle librerie Java NIO, ha fornito un nuovo livello di astrazione per l'accesso alla rete in PariPari, arricchito da un sistema efficiente di I/O asincrono. Su questa base è stato poi possibile realizzare nuovi servizi quali limitazione di banda, NAT Traversal, Multicast e Anonimato. PariPari is a multi-functional and extensible P2P platform. This thesis illustrates how PariConnectivity --the module of PariPari which provides access to network resources -- has been reengineered and optimized. Through the development of a new set of APIs, built on the Java NIO libraries, we have created a new network abstraction layer, providing easiness-to-use and efficiency thanks to the introduction of advanced features as, for instance, the asynchronous I/O. On this basis, we have introduced and enhanced some crucial services like bandwidth limitation, NAT Traversal, Multicast and Anonymit

(SEMI)-AUTOMATED ANALYSIS OF MELANOCYTIC LESIONS

Melanoma is a very aggressive form of skin cancer whose incidence has constantly grown in the last 50 years. To increase the survival rate, an early diagnosis followed by a prompt excision is crucial and requires an accurate and periodic analysis of the patient's melanocytic lesions. We have developed an hardware and software solution named Mole Mapper to assist the dermatologists during the diagnostic process. The goal is to increase the accuracy of the diagnosis, accelerating the entire process at the same time. This is achieved through an automated analysis of the dermatoscopic images which computes and highlights the proper information to the dermatologist. In this thesis we present the 3 main algorithms that have been implemented into the Mole Mapper: A robust segmentation of the melanocytic lesion, which is the starting point for any other image processing algorithm and which allows the extraction of useful information about the lesion's shape and size. It outperforms the speed and quality of other state-of-the-art methods, with a precision that meets a Senior Dermatologist's standard and an execution time that allows for real-time video processing; A virtual shaving algorithm, which increases the precision and robustness of the other computer vision algorithms and provides the dermatologist with a hair-free image to be used during the evaluation process. It matches the quality of state-of-the-art methods but requires only a fraction of the computational time, allowing for computation on a mobile device in a time-frame compatible with an interactive GUI; A registration algorithm through which to study the evolution of the lesion over time, highlighting any unexpected anomalies and variations. Since a standard approach to this problem has not yet been proposed, we define the scope and constraints of the problem; we analyze the results and issues of standard registration techniques; and finally, we propose an algorithm with a speed compatible with Mole Mapper's constraints and with an accuracy comparable to the registration performed by a human operator.Il Melanoma è una forma molto aggressiva di cancro alla pelle la cui incidenza è costantemente aumentata negli ultimi 50 anni. Una diagnosi precoce unita ad una rapida asportazione risulta indispensabile per migliorare il tasso di sopravvivenza e richiede una analisi periodica ed accurata della lesioni melanocitiche del paziente. Abbiamo sviluppato una soluzione hardware e software chiamata Mole Mapper per assistere i deramtologi durante l'intero processo di diagnosi. L'obiettivo è permettere un incremento dell'accuratezza della diagnosi velocizzando al contempo l'intero processo. Tali caratteristiche si sono ottenute grazie ad un'analisi automatica delle immagini dermatoscopiche che individua ed evidenza al dermatologo le informazioni più significative. In questa tesi presentiamo 3 principali algoritmi che sono stati implementati in Mole Mapper: Una robusta segmentazione di lesioni melanocitiche, che risulta il punto di partenza di ogni altro algoritmo di elaborazioni di immagini e permette l'estrazione di informazioni utili riguardanti la forma e la dimensione delle lesioni. Tale algoritmo supera in accuratezza e velocità lo stato dell'arte attuale, con una precisione paragonabile ad un dermatologo esperto ed un tempo di esecuzione compatibile con l'elaborazione video realtime; Un algoritmo di depilazione digitale, che garantisce miglior precisione e robustezza agli altri algoritmi di elaborazione di immagini a fornisce al dermatologo un immagine priva di peli da impiegare nel processo di valutazione. La nostra proposta supera l'accuratezza dello stato dell'arte richiedendo solo una frazione del tempo di esecuzione, tanto da poter essere integrata su dispositivi mobili all'interno di una GUI interattiva. Un algoritmo di registrazione, per studiare l'evoluzione delle lesioni nel tempo evidenziando ogni possibile anomalia o variazione. Data la mancanza di un approccio standard al problema, abbiamo caratteriizzato gli obbiettivi ed i vincoli a cui sottostare proponendo quindi un approccio con un tempo di esecuzione compatibile con le necessità del Mole Mapper ed un accuratezza paragonabile a quella di un operatore umano

Simpler, faster, more accurate melanocytic lesion segmentation through MEDS.

We present a new technique for melanocytic lesion segmentation, Mimicking Expert Dermatologists' Segmentations (MEDS), and extensive tests of its accuracy, speed, and robustness. MEDS combines a thresholding scheme reproducing the cognitive process of dermatologists with a number of optimizations that may be of independent interest. MEDS is simple, with a single parameter tuning its \u201ctightness\u201d. It is extremely fast, segmenting medium-resolution images in a fraction of a second even with the modest computational resources of a cell phone-an improvement of an order of magnitude or more over state-of-the-art techniques. And it is extremely accurate: very experienced dermatologists disagree with its segmentations less than they disagree with the segmentations of state-of-the-art techniques, and in fact less than they disagree with the segmentations of dermatologists of moderate experience

Simple, Fast, Accurate Melanocytic Lesion Segmentation in 1D Colour Space

We present a novel technique for melanocytic lesion segmentation, based on mono-dimensional Principal Component Analysis (PCA) in colour space. Our technique is simple and extremely fast, segmenting high-resolution images in a fraction of a second even with the modest computational resources available on a cell phone \u2013 an improvement of an order of magnitude or more over state-of-the-art techniques. Our technique is also extremely accurate: very experienced dermatologists disagree with its segmentations less than they disagree with the segmentations of all state-of-the-art techniques we tested, and in fact less than they disagree with the segmentations of dermatologists of moderate experience