The treatment plan for radiation therapy with protons is based on images from a computed tomography (CT) scanner. This is problematic since the photons in the x-ray beam from the CT scanner and the protons are affected differently by the tissue in the patient, which introduce an uncertainty in the track length of the protons. The hypothesis of this study is that a new generation of CT scanners (DECT), with the capacity to simultaneously scan the patient with two photon spectra of different mean energy, will improve the tissue characterisation and which in turn reduce the uncertainty in the track length of the protons. In this study, the accuracy and precision of a DECT-based method from the literature is compared to the conventional calibration method used today at the University clinics in Sweden to relate the attenuation of the photon beam to the slowing down of the protons. The methods are tested on CT images of a phantom, a plastic body containing tissue equivalent plastic inserts of known elemental composition. The results turned out to be inconclusive as there were large uncertainties in the measurements. The method has potential, as has been shown in the literature, but there are many questions that need to be answered before the method is ready to be implemented at the clinic. / En proton som färdas genom människokroppen deponerar endast en liten del av sin energi längs vägen innan den plötsligt deponerar allt i slutet på dess bana. Hur lång dess bana är beror på protonens ursprungliga energi och den atomära sammansättningen hos vävnaden den passerar igenom. Om sammansättningen är känd går det genom att justera den initiala energin bestämma banlängden. Denna egenskap gör protonen väldigt attraktiv för strålterpi, då det innbär möjligheten att behandla med hög precision samt bespara frisk vävnad onödig dos. Strålterapi med protoner planeras idag med bilder från en skiktröntgen (CT) som underlag. Ett problem med det är att röntgenstrålarna från CT-skannern påverkas annorlunda än protonerna av vävnaden, vilket introducerar en osäkerhet i protonernas banlängd. Hypotesen i denna studie är att en ny generation av CT-scanner (DECT), med möjlighet att simultant skanna patienten med två fotonspektran av olika medelenergi, på ett bättre sätt ska kunna bestämma den atomära sammansättningen för vävnaden och därmed reducera osäkerheten i protonernas banlängd. Noggrannhet och precision för en DECT-baserad metod från litteraturen jämförs med den SECT-baserade kalibreringsmetoden, som idag används på Universitetssjukhusen i Sverige för att relatera fotonstrålens dämpning i vävnaden till protonernas inbromsning. Metoderna testas på CT bilder av ett fantom, en plastkropp innehållandes olika cylindrar av vävnadsekvivalent plast med känd atomär sammansättning. Resultatet av den här studien är inte starkt nog för att bevisa hypotesen för studien. Det insamlade bildmaterialet innehåller höga brusnivåer jämfört med de som rapporteras i literaturen. Brusnivåer är så höga att det mesta av resultatet inte kan anses som statistiskt signifikant. Det är dessutom svårt att göra en direkt jämförelse av prestanda med befintlig teori för vävnadskaraktärisering, då bildmaterialet från de CT skanners som jämfördes är av olika typer. De resultat som publicerats i litteraturen visar att den DECT-baserade metoden har potential, men den här studien gör tydligt att det fortfarande finns frågor som måste besvaras innan metoden är redo att implementeras kliniskt.

Doenças coronárias causam a morte de milhões de pessoas anualmente. Uma dessas disfunções é a aterosclerose coronariana, acúmulo de placas lipídicas, fibrosas, e calcificadas na parede das coronárias. Esse acúmulo pode causar tromboses, infarto do miocárdio, ou morte cardíaca súbita. Porém, essas lesões apresentam graus distintos de periculosidade e elasticidade. As predominantemente lipídicas são de alto risco e elasticidade, enquanto as calcificadas e as fibrosas são mais estáveis e menos elásticas. O Ultrassom Intravascular (IVUS) é uma das modalidades de referência em diagnósticos e acompanhamento de doenças coronárias. Contudo, a imagem de IVUS pura fornece apenas informações subjetivas sobre vasos e placas; assim, é importante a criação de métodos e técnicas que possam tornar objetiva a análise dessa informação. Devido a isso, e levando em conta a riqueza de informações espaciais e temporais presentes nas imagens de IVUS, esse trabalho apresenta métodos de segmentação, e extração de características de lesões, que possibilitam a quantização de informações espaciais, e a discriminação de placas de baixo e elevado-risco. Consequentemente, fornecendo subsídios para diagnósticos, e procedimentos terapêuticos mais adequados. O método de segmentação combina Wavelet, Otsu, e Morfologia Matemática, para delineamento da parede do vaso. A avaliação do método foi feita usando 1300 imagens de IVUS, resultando em 92, 72% e 91, 9% de verdadeiros positivos, e 10, 7% e 9, 1% de falsos positivos, para o lúmen e borda da média adventícia, respectivamente. Adicionalmente, foi criado um método simples, para caracterização de placas a partir de suas propriedades mecânicas. Esse procedimento se baseia em computar um índice, chamado taxa de variação da área da placa, em imagens adquiridas pré e pós deformação do vaso e placas. Phantoms foram usados para avaliação, os resultados conseguidos com o índice proposto e um amplamente usado foram comparados. Uma correlação chegando à 99%, uma forte concordância usando Análise de Bland Altman, e Histogramas muito similares entre os dois índices, mostraram que o método proposto equivale ao já estabelecido. / Coronary diseases are the cause of death of millions of people annually. One of these dysfunctions is the coronary atherosclerosis, which is the accumulation of lipidic, fibrous and calcified plaques in the coronary wall. This accumulation may cause thrombosis, myocardial infarction and sudden cardiac death. Nonetheless, the kind of plaques offers different levels of dangerousness and elasticity. The highly lipidic plaques are very elastic, offers high risk, while the calcified and fibrous are more stable and less elastic. The Intravascular Ultrasound (IVUS) is the reference medical imaging modality for diagnostic and treatment of coronary diseases. However, the conventional IVUS images provides only anatomical vessel and plaque information; therefore, it is very important the creation of methods and techniques that could make objective the analysis of this information. Due to that, and taking into account the spatial and time information of IVUS images, this work presents methods of segmentation, and feature extraction of lesions, which make possible the quantization of spatial information, and the discrimination of high, and low risk plaques. Consequently, subsidies for diagnoses and more appropriate therapeutic procedures are provided. The segmentation method combines Wavelet, Otsu, and Mathematical Morphology, for the vessel wall delineation. The method evaluation was performed using 1300 IVUS images, resulting in 92, 72% and 91, 9% of true positives, and 10, 7% and 9, 1% of false positives, for the lumen and media adventitia border, respectively. Additionally, a simple method, for plaque characterization using the regarding mechanical properties was created. The procedure relies on computing an index, ratio of plaque area variation, in acquired images pre and post deformation procedure of vessel wall and plaques. Phantoms were used for evaluation. The results obtained by the proposed index, and a widely used one was compared. A correlation up to 99%, a strong agreement with Bland Altman, and similar Histograms between the two indexes demonstrated the equivalence between them; however, the proposed index is much simpler.

A histopatologia é considerada um dos recursos diagnósticos mais importantes na prática médica e caracteriza-se pelo estudo das alterações estruturais e morfológicas das células e dos tecidos causadas por doenças. Atualmente, o principal método utilizado no diagnóstico histopatológico de imagens microscópicas, obtidas por meio de amostras em exames convencionais, é a avaliação visual do patologista, a qual se baseia na experiência do mesmo. O uso de técnicas de processamento computacional de imagens possibilita a identificação de elementos estruturais e a determinação de características inerentes, subsidiando o estudo da organização estrutural das células e de suas variações patológicas. A utilização de métodos computacionais no auxílio ao diagnóstico visa diminuir a subjetividade do processo de avaliação e classificação realizado pelo médico. Diferentes características dos tecidos podem ser mapeadas por meio de métricas específicas que poderão ser utilizadas em sistemas de reconhecimento de padrões. Dentro desta perspectiva, o objetivo geral deste trabalho inclui a proposta, a implementação e a avaliação de um método para a identificação e a análise de estruturas histológicas, a ser utilizado para a análise de lesões neoplásicas do colo do útero (NICs) a partir de amostras histopatológicas. Este trabalho foi desenvolvido em colaboração com uma equipe de patologistas, especialistas do domínio. As imagens microscópicas digitalizadas foram adquiridas a partir de lâminas previamente fixadas, contendo amostras de biópsias. Para segmentação dos núcleos celulares, foi implementado um pipeline de operadores morfológicos. Métodos de segmentação baseados em cor também foram testados e comparados à abordagem morfológica. Foi proposta e implementada uma abordagem baseada em camadas para representação do tecido, adotando-se a Triangulação de Delaunay (TD) como modelo de grafo de vizinhança. A TD apresenta algumas propriedades particulares que permitem a extração de métricas específicas. Foram utilizados algoritmos de agrupamento e morfologia de grafos, adotando-se critérios de semelhança e relações de adjacência entre os triângulos da rede, a fim de se obter a fronteira entre as camadas histológicas do tecido epitelial de forma automática. As seguintes métricas foram extraídas dos agrupamentos resultantes: grau médio, entropia e taxa de ocupação dos triângulos da rede. Finalmente, foi projetado um classificador estatístico levando-se em consideração os diferentes agrupamentos que poderiam ser obtidos a partir das imagens de treinamento. Valores de acurácia, sensitividade e especificidade foram utilizadas para avaliação dos resultados obtidos. Foi implementada validação cruzada em todos os experimentos realizados e foi utilizado um total de 116 imagens. Primeiro, foi avaliado a acurácia da metodologia proposta na determinação correta da presença de anomalia no tecido, para isto, todas as imagens que apresentavam NICs foram agrupadas em uma mesma classe. A maior taxa de acurácia obtida neste experimento foi de 88%. Em uma segunda etapa, foram realizadas avaliações entre as seguintes classes: Normal e NIC-I; NIC-I e NIC-II, e, NIC-II e NIC-III, obtendo-se taxas de acurácia máximas de 73%, 77% e 86%, respectivamente. Além disso, foi verificada também, a acurácia na discriminação entre os três tipos de NICs e regiões normais, obtendo-se acurácia de 64%. As taxas de ocupação relativas aos agrupamentos representativos das camadas basais e superficiais, foram os atributos que levaram às maiores taxas de acurácia. Os resultados obtidos permitem verificar a adequação do método proposto na representação e análise do processo de evolução das NICs no tecido epitelial do colo uterino. / Histopathology is considered one of the most important diagnostic tools in medical routine and is characterized by the study of structural and morphological changes of the cells in biological tissues caused by diseases. Currently, the visual assessment of the pathologist is the main method used in the histopathological diagnosis of microscopic images obtained from biopsy samples. This diagnosis is usually based on the experience of the pathologist. The use of computational techniques in the processing of these images allows the identification of structural elements and the determination of inherent characteristics, supporting the study of the structural organization of tissues and their pathological changes. Also, the use of computational methods to improve diagnosis aims to reduce the subjectivity of the evaluation made by the physician. Besides, different tissue characteristics can be mapped through specific metrics that can be used in pattern recognition systems. Within this perspective, the overall objective of this work includes the proposal, the implementation and the evaluation of a methodology for the identification and analysis of histological structures. This methodology includes the specification of a method for the analysis of cervical intraepithelial neoplasias (CINs) from histopathological samples. This work was developed in collaboration with a team of pathologists. Microscopic images were acquired from blades previously stained, containing samples of biopsy examinations. For the segmentation of cell nuclei, a pipeline of morphological operators were implemented. Segmentation techniques based on color were also tested and compared to the morphological approach. For the representation of the tissue architecture an approach based on the tissue layers was proposed and implemented adopting the Delaunay Triangulation (DT) as neighborhood graph. The DT has some special properties that allow the extraction of specific metrics. Clustering algorithms and graph morphology were used in order to automatically obtain the boundary between the histological layers of the epithelial tissue. For this purpose, similarity criteria and adjacency relations between the triangles of the network were explored. The following metrics were extracted from the resulting clusters: mean degree, entropy and the occupation rate of the clusters. Finally, a statistical classifier was designed taking into account the different combinations of clusters that could be obtained from the training process. Values of accuracy, sensitivity and specificity were used to evaluate the results. All the experiments were taken in a cross-validation process (5-fold) and a total of 116 images were used. First, it was evaluated the accuracy in determining the correct presence of abnormalities in the tissue. For this, all images presenting CINs were grouped in the same class. The highest accuracy rate obtained for this evaluation was 88%. In a second step, the discrimination between the following classes were analyzed: Normal/CIN 1; CIN 1/CIN 2, and, CIN 2/CIN 3, which represents the histological grading of the CINs. In a similar way, the highest accuracy rates obtained were 73%, 77% and 86%, respectively. In addition, it was also calculated the accuracy rate in discriminating between the four classes analyzed in this work: the three types of CINs and the normal region. In this last case, it was obtained a rate of 64%.The occupation rate for the basal and superficial layers were the attributes that led to the highest accuracy rates. The results obtained shows the adequacy of the proposed method in the representation and classification of the CINs evolution in the cervical epithelial tissue.

A histopatologia é considerada um dos recursos diagnósticos mais importantes na prática médica e caracteriza-se pelo estudo das alterações estruturais e morfológicas das células e dos tecidos causadas por doenças. Atualmente, o principal método utilizado no diagnóstico histopatológico de imagens microscópicas, obtidas por meio de amostras em exames convencionais, é a avaliação visual do patologista, a qual se baseia na experiência do mesmo. O uso de técnicas de processamento computacional de imagens possibilita a identificação de elementos estruturais e a determinação de características inerentes, subsidiando o estudo da organização estrutural das células e de suas variações patológicas. A utilização de métodos computacionais no auxílio ao diagnóstico visa diminuir a subjetividade do processo de avaliação e classificação realizado pelo médico. Diferentes características dos tecidos podem ser mapeadas por meio de métricas específicas que poderão ser utilizadas em sistemas de reconhecimento de padrões. Dentro desta perspectiva, o objetivo geral deste trabalho inclui a proposta, a implementação e a avaliação de um método para a identificação e a análise de estruturas histológicas, a ser utilizado para a análise de lesões neoplásicas do colo do útero (NICs) a partir de amostras histopatológicas. Este trabalho foi desenvolvido em colaboração com uma equipe de patologistas, especialistas do domínio. As imagens microscópicas digitalizadas foram adquiridas a partir de lâminas previamente fixadas, contendo amostras de biópsias. Para segmentação dos núcleos celulares, foi implementado um pipeline de operadores morfológicos. Métodos de segmentação baseados em cor também foram testados e comparados à abordagem morfológica. Foi proposta e implementada uma abordagem baseada em camadas para representação do tecido, adotando-se a Triangulação de Delaunay (TD) como modelo de grafo de vizinhança. A TD apresenta algumas propriedades particulares que permitem a extração de métricas específicas. Foram utilizados algoritmos de agrupamento e morfologia de grafos, adotando-se critérios de semelhança e relações de adjacência entre os triângulos da rede, a fim de se obter a fronteira entre as camadas histológicas do tecido epitelial de forma automática. As seguintes métricas foram extraídas dos agrupamentos resultantes: grau médio, entropia e taxa de ocupação dos triângulos da rede. Finalmente, foi projetado um classificador estatístico levando-se em consideração os diferentes agrupamentos que poderiam ser obtidos a partir das imagens de treinamento. Valores de acurácia, sensitividade e especificidade foram utilizadas para avaliação dos resultados obtidos. Foi implementada validação cruzada em todos os experimentos realizados e foi utilizado um total de 116 imagens. Primeiro, foi avaliado a acurácia da metodologia proposta na determinação correta da presença de anomalia no tecido, para isto, todas as imagens que apresentavam NICs foram agrupadas em uma mesma classe. A maior taxa de acurácia obtida neste experimento foi de 88%. Em uma segunda etapa, foram realizadas avaliações entre as seguintes classes: Normal e NIC-I; NIC-I e NIC-II, e, NIC-II e NIC-III, obtendo-se taxas de acurácia máximas de 73%, 77% e 86%, respectivamente. Além disso, foi verificada também, a acurácia na discriminação entre os três tipos de NICs e regiões normais, obtendo-se acurácia de 64%. As taxas de ocupação relativas aos agrupamentos representativos das camadas basais e superficiais, foram os atributos que levaram às maiores taxas de acurácia. Os resultados obtidos permitem verificar a adequação do método proposto na representação e análise do processo de evolução das NICs no tecido epitelial do colo uterino. / Histopathology is considered one of the most important diagnostic tools in medical routine and is characterized by the study of structural and morphological changes of the cells in biological tissues caused by diseases. Currently, the visual assessment of the pathologist is the main method used in the histopathological diagnosis of microscopic images obtained from biopsy samples. This diagnosis is usually based on the experience of the pathologist. The use of computational techniques in the processing of these images allows the identification of structural elements and the determination of inherent characteristics, supporting the study of the structural organization of tissues and their pathological changes. Also, the use of computational methods to improve diagnosis aims to reduce the subjectivity of the evaluation made by the physician. Besides, different tissue characteristics can be mapped through specific metrics that can be used in pattern recognition systems. Within this perspective, the overall objective of this work includes the proposal, the implementation and the evaluation of a methodology for the identification and analysis of histological structures. This methodology includes the specification of a method for the analysis of cervical intraepithelial neoplasias (CINs) from histopathological samples. This work was developed in collaboration with a team of pathologists. Microscopic images were acquired from blades previously stained, containing samples of biopsy examinations. For the segmentation of cell nuclei, a pipeline of morphological operators were implemented. Segmentation techniques based on color were also tested and compared to the morphological approach. For the representation of the tissue architecture an approach based on the tissue layers was proposed and implemented adopting the Delaunay Triangulation (DT) as neighborhood graph. The DT has some special properties that allow the extraction of specific metrics. Clustering algorithms and graph morphology were used in order to automatically obtain the boundary between the histological layers of the epithelial tissue. For this purpose, similarity criteria and adjacency relations between the triangles of the network were explored. The following metrics were extracted from the resulting clusters: mean degree, entropy and the occupation rate of the clusters. Finally, a statistical classifier was designed taking into account the different combinations of clusters that could be obtained from the training process. Values of accuracy, sensitivity and specificity were used to evaluate the results. All the experiments were taken in a cross-validation process (5-fold) and a total of 116 images were used. First, it was evaluated the accuracy in determining the correct presence of abnormalities in the tissue. For this, all images presenting CINs were grouped in the same class. The highest accuracy rate obtained for this evaluation was 88%. In a second step, the discrimination between the following classes were analyzed: Normal/CIN 1; CIN 1/CIN 2, and, CIN 2/CIN 3, which represents the histological grading of the CINs. In a similar way, the highest accuracy rates obtained were 73%, 77% and 86%, respectively. In addition, it was also calculated the accuracy rate in discriminating between the four classes analyzed in this work: the three types of CINs and the normal region. In this last case, it was obtained a rate of 64%.The occupation rate for the basal and superficial layers were the attributes that led to the highest accuracy rates. The results obtained shows the adequacy of the proposed method in the representation and classification of the CINs evolution in the cervical epithelial tissue.

Doenças coronárias causam a morte de milhões de pessoas anualmente. Uma dessas disfunções é a aterosclerose coronariana, acúmulo de placas lipídicas, fibrosas, e calcificadas na parede das coronárias. Esse acúmulo pode causar tromboses, infarto do miocárdio, ou morte cardíaca súbita. Porém, essas lesões apresentam graus distintos de periculosidade e elasticidade. As predominantemente lipídicas são de alto risco e elasticidade, enquanto as calcificadas e as fibrosas são mais estáveis e menos elásticas. O Ultrassom Intravascular (IVUS) é uma das modalidades de referência em diagnósticos e acompanhamento de doenças coronárias. Contudo, a imagem de IVUS pura fornece apenas informações subjetivas sobre vasos e placas; assim, é importante a criação de métodos e técnicas que possam tornar objetiva a análise dessa informação. Devido a isso, e levando em conta a riqueza de informações espaciais e temporais presentes nas imagens de IVUS, esse trabalho apresenta métodos de segmentação, e extração de características de lesões, que possibilitam a quantização de informações espaciais, e a discriminação de placas de baixo e elevado-risco. Consequentemente, fornecendo subsídios para diagnósticos, e procedimentos terapêuticos mais adequados. O método de segmentação combina Wavelet, Otsu, e Morfologia Matemática, para delineamento da parede do vaso. A avaliação do método foi feita usando 1300 imagens de IVUS, resultando em 92, 72% e 91, 9% de verdadeiros positivos, e 10, 7% e 9, 1% de falsos positivos, para o lúmen e borda da média adventícia, respectivamente. Adicionalmente, foi criado um método simples, para caracterização de placas a partir de suas propriedades mecânicas. Esse procedimento se baseia em computar um índice, chamado taxa de variação da área da placa, em imagens adquiridas pré e pós deformação do vaso e placas. Phantoms foram usados para avaliação, os resultados conseguidos com o índice proposto e um amplamente usado foram comparados. Uma correlação chegando à 99%, uma forte concordância usando Análise de Bland Altman, e Histogramas muito similares entre os dois índices, mostraram que o método proposto equivale ao já estabelecido. / Coronary diseases are the cause of death of millions of people annually. One of these dysfunctions is the coronary atherosclerosis, which is the accumulation of lipidic, fibrous and calcified plaques in the coronary wall. This accumulation may cause thrombosis, myocardial infarction and sudden cardiac death. Nonetheless, the kind of plaques offers different levels of dangerousness and elasticity. The highly lipidic plaques are very elastic, offers high risk, while the calcified and fibrous are more stable and less elastic. The Intravascular Ultrasound (IVUS) is the reference medical imaging modality for diagnostic and treatment of coronary diseases. However, the conventional IVUS images provides only anatomical vessel and plaque information; therefore, it is very important the creation of methods and techniques that could make objective the analysis of this information. Due to that, and taking into account the spatial and time information of IVUS images, this work presents methods of segmentation, and feature extraction of lesions, which make possible the quantization of spatial information, and the discrimination of high, and low risk plaques. Consequently, subsidies for diagnoses and more appropriate therapeutic procedures are provided. The segmentation method combines Wavelet, Otsu, and Mathematical Morphology, for the vessel wall delineation. The method evaluation was performed using 1300 IVUS images, resulting in 92, 72% and 91, 9% of true positives, and 10, 7% and 9, 1% of false positives, for the lumen and media adventitia border, respectively. Additionally, a simple method, for plaque characterization using the regarding mechanical properties was created. The procedure relies on computing an index, ratio of plaque area variation, in acquired images pre and post deformation procedure of vessel wall and plaques. Phantoms were used for evaluation. The results obtained by the proposed index, and a widely used one was compared. A correlation up to 99%, a strong agreement with Bland Altman, and similar Histograms between the two indexes demonstrated the equivalence between them; however, the proposed index is much simpler.

Medical imaging technologies such as MRI, CT, PET, etc. enable the use of higher resolution 3D digital image data for research and clinical treatment. The new technologies provide improved spatial resolution at the cost of increased data processing time. Manual identification of anatomical landmarks is still a common practice in many neuroimaging and other medical imaging applications but it is labor-intensive, subjective, and suffers from intra-/inter- rater inconsistency. This work explored one way of estimating a landmark constellation automatically, consistently, and efficiently. The proposed method demonstrated a successful application on how to effectively utilize image processing in tackling clinical challenges. It is shown that the cooperation of spatial localization using linear model prediction with evolutionary principal components and local search estimation using statistical shape models is capable of effectively extracting important landmark detection information from both morphometric relationships of landmarks and consistent intensity distribution of images. It is accurate (compared to 1.6 mm root mean squared errors of manual labeling of brain landmarks), consistent, reliable in predicting many salient midbrain point landmarks such as ac, pc, MPJ, etc. in a longitudinal, multisubject environment, and throughout large datasets with different modalities and image information such as orientation, spacing, and origin. The framework of linear model estimation method using evolutionary principal components and the idea of local search using statistical shape models are generalized to the detection task for arbitrary number of landmarks in other organs, creatures, or even any other physical objects in the world as long as the landmarks present intensity consistency and satisfy regularity in spatial organization.

Virtual surgery planning systems have demonstrated great potential to help surgeons achieve a better functional and aesthetic outcome for the patient, and at the same time reduce time in the operating room resulting in considerable cost savings. However, the two-dimensional tools employed in these systems today, such as a mouse and a conventional graphical display, are difficult to use for interaction with three-dimensional anatomical images. Therefore surgeons often outsource virtual planning which increases cost and lead time to surgery. Haptics relates to the sense of touch and haptic technology encompasses algorithms, software, and hardware designed to engage the sense of touch. To demonstrate how haptic technology in combination with stereo visualization can make cranio-maxillofacial surgery planning more efficient and easier to use, we describe our haptics-assisted surgery planning (HASP) system. HASP supports in-house virtual planning of reconstructions in complex trauma cases, and reconstructions with a fibula osteocutaneous free flap including bone, vessels, and soft-tissue in oncology cases. An integrated stable six degrees-of-freedom haptic attraction force model, snap-to-fit, supports semi-automatic alignment of virtual bone fragments in trauma cases. HASP has potential beyond this thesis as a teaching tool and also as a development platform for future research. In addition to HASP, we describe a surgical bone saw simulator with a novel hybrid haptic interface that combines kinesthetic and vibrotactile feedback to display both low frequency contact forces and realistic high frequency vibrations when a virtual saw blade comes in contact with a virtual bone model.  We also show that visuo-haptic co-location shortens the completion time, but does not improve the accuracy, in interaction tasks performed on two different visuo-haptic displays: one based on a holographic optical element and one based on a half-transparent mirror.  Finally, we describe two prototype hand-worn haptic interfaces that potentially may expand the interaction capabilities of the HASP system. In particular we evaluate two different types of piezo-electric motors, one walking quasi-static motor and one traveling-wave ultrasonic motor for actuating the interfaces.

Η διαχείριση της πληροφορίας που προέρχεται από εικόνες ιστοπαθολογίας μικροσκοπίου (βιοψίες) αποτελεί διεργασία υψηλής πολυπλοκότητας που αξιοποιείται για την εξαγωγή διαγνωστικών και προγνωστικών συμπερασμάτων από τον ιστοπαθολόγο. Η πολυπλοκότητα αυτή πηγάζει από τον τεράστιο όγκο βιολογικών οντοτήτων που περιέχονται στο δείγμα βιοψίας αλλά και στις μεταξύ τους πολυσύνθετες αλληλεπιδράσεις. Οι πιο σύγχρονες μέθοδοι τεχνητής νοημοσύνης προτείνουν εναλλακτικές προσεγγίσεις για την επίλυση των προβλημάτων υψηλής πολυπλοκότητας αυτού του τύπου. Ανάμεσα όμως στην είσοδο (δεδομένα) και έξοδο (αποτέλεσμα) των ‘έξυπνων’ υπολογιστικών συστημάτων, κρύβεται η μεθοδολογία και στρατηγική επεξεργασίας και ανάλυσης της διαθέσιμης πληροφορίας. Κατά το στάδιο αυτό οι παράμετροι ελέγχου διαχωρίζονται και συσχετίζονται μεταξύ τους ΄τυφλά’ (π.χ. με νευρωνικά δίκτυα, ασαφή λογική) σύμφωνα με συγκεκριμένα μαθηματικά κριτήρια (π.χ. πιθανοκρατικά, ελάχιστων τετραγώνων κ.α.) χωρίς όμως να λαμβάνουν υπόψη την ‘ευρετική’ (heuristic) του ειδικού με αποτέλεσμα να παρουσιάζουν πεπερασμένη ακρίβεια, μεγάλο χρόνο υλοποίησης, αδυναμία γενίκευσης. Έτσι, η απόδοση των συστημάτων αυτών εξαρτάται από το μέγεθος και ποιότητα (θορυβώδη, ελλιπή δεδομένα κ.α.) των δεδομένων, το πλήθος των συνδυασμών των ποσοτικών χαρακτηριστικών που περιγράφουν τα δεδομένα, τον καθορισμό των πλούσιων σε πληροφορία χαρακτηριστικών, την σημαντικότητα των επιμέρους χαρακτηριστικών και των μαθηματικών κριτηρίων ταξινόμησης. Για παράδειγμα πολλά χαρακτηριστικά περιγράφουν καλύτερα την υπό μελέτη διεργασία αλλά η εξαγωγή των πλούσιων σε προγνωστική πληροφορία χαρακτηριστικών απαιτεί πολλούς συνδυασμούς και μεγάλη υπολογιστική ισχύ. Επίσης πολλά χαρακτηριστικά σημαίνει εξειδίκευση του συστήματος στα δεδομένα εκπαίδευσης και αδυναμία εφαρμογής σε άγνωστα δεδομένα. Η παρούσα διατριβή διαπραγματεύεται τον σχεδιασμό, ανάπτυξη και υλοποίηση νέων μεθόδων επεξεργασίας και ανάλυσης ιατρικών εικόνων, επικεντρώνοντας ειδικότερα στην εφαρμογή των μεθόδων αυτών σε υπολογιστικό σύστημα μικροσκοπίας για την διάγνωση όγκων εγκεφάλου τύπου αστροκυττώματος. / Even though histological diagnosis is fundamentally important for patient's management, the potential of diagnostic errors in astrocytomas grading still remains substantially high, ranging from 25% to 40% in routine conditions. Diagnostic errors originate mainly from the lack of experience of experts; rare cancers low prevalence and their biological complexity hinder the establishment of concrete criteria able to predict tumours' behaviour, and, thus, to administrate proper treatments. The latter might explain the fact that a/ although promising treatments have been proposed, death rates have not been yet reduced and b/ the cost of rare cancers management still remains one of the highest healthcare economic burdens in Europe and worldwide. The aim of this thesis was to design, develop and implement new computerized methods to improve manual and computer-assisted malignancy grading of astrocytomas. Scientific objectives comprised: a/ develop a reliable and accurate segmentation algorithm for nuclei detection in routinely stained with H&E histopathological images of astrocytomas, b/ investigate and quantify modifications in nuclei morphology and texture with respect to the degree of tumour abnormality of astrocytic tumours, c/ evaluate whether quantitative analysis of cell nuclei by computer-assisted image analysis could assist the routinely performed malignancy grading of astrocytomas using conventional means, d/ investigate potential modifications in chromatin distribution, which might be used to improve the diagnostic evaluation of cases that histopathologists have difficulty in reaching definite diagnosis (i.e. 'intermediate' grade tumours), e/ support more reliable separation of high grade tumours into clinically meaningful subgroups of patients with grade III and grade IV tumours. For realizing the above objectives, a computer-assisted microscopy system was designed, built and implemented. The system was developed using novel methodologies that integrated state-of-art pattern recognition algorithms for microscopy image segmentation and classification. In addition, new classification techniques have been introduced. The usefulness of the proposed methods has been validated experimentally.

This thesis explores two possible ways of detecting soft plaque present in the coronary arteries, using CTA imagery. The coronary arteries are vessels that supply oxidized blood to the cardiac muscle and are thus important for the proper functioning of heart. Cholesterol or reactive oxygen species from cigarette smoke and other toxins may get adhered to the walls of coronary arteries and trigger chronic inflammation that leads to formation of the soft plaque. When the soft plaque grows bigger in volume, it occludes the blood flow to the cardiac muscle and finally results in ischemic heart attack. Moreover, smaller plaque can easily rupture due to the blood flow in arteries and can result in complications such as stroke. Hence there is a need to detect the soft plaque using non-invasive or minimally invasive techniques. In CTA imagery, the cardiac muscle appears as a dark gray color, while the blood appears as dull white color and the the calcified plaque appears as bright white. The soft plaque has an intensity which falls between the intensity level of the blood and cardiac muscle, making it difficult to directly segment the soft plaque using standard segmentation methods. Soft plaque in its advanced stages forms a concavity in the blood lumen. A watershed based segmentation method was used to detect the presence of this concavity which in turn identifies the location of the soft plaque. For segmenting the soft plaque at its earlier stages, a novel segmentation technique was used. In this technique the surface is evolved based on a region-based energy calculated in the local neighborhood around each point on the evolving surface. This method seems to be superior to the watershed based segmentation method in detecting smaller plaque deposits.

The purpose of this project was to explore the performance of methods for estimating stopping power ratio (SPR) from Hounsfield units (HU) using dual energy CT scans, rather than the standard single energy CT scans, with the aim of finding a method which could outperform the current single energy stoichiometric method. Such a method could reduce the margin currently added to the target volume during treatment which is defined as 3.5 % of the range to the target volume + 1 mm . Three such methods, by Taasti, Zhu, and, Lalonde and Bouchard, were chosen and implemented in MATLAB. A phantom containing 10 tissue-like inserts was scanned and used as a basis for the SPR estimation. To investigate the variation of the SPR from day-to-day the phantom was scanned once a day for 12 days. The resulting SPR of all methods, including the stoichiometric method, were compared with theoretical SPR values which were calculated using known elemental weight fractions of the inserts and mean excitation energies from the National Institute of Standards and Technology (NIST). It was found that the best performing method was the Taasti method which had, at best, an average percentage difference from the theoretical values of only 2.5 %. The Zhu method had, at best, 4.8 % and Lalonde-Bouchard 15.6% including bone tissue or 6.3 % excluding bone. The best average percentage difference of the stoichiometric method was 3.1 %. As the Taasti method was the best performing method and shows much promise, future work should focus on further improving its performance by testing more scanning protocols and kernels to find the ones yielding the best performance. This should then be supplemented with testing different pairs of energies for the dual energy scans. The fact that the Zhu and Lalonde-Bouchard method performed poorly could indicate problems with the implementation of those methods in this project. Investigating and solving those problems is also an important goal for future projects. Lastly the Lalonde-Bouchard method should be tested with more than two energy spectra.