302 research outputs found

    Identification of tumor epithelium and stroma in tissue microarrays using texture analysis

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    <p>Abstract</p> <p>Background</p> <p>The aim of the study was to assess whether texture analysis is feasible for automated identification of epithelium and stroma in digitized tumor tissue microarrays (TMAs). Texture analysis based on local binary patterns (LBP) has previously been used successfully in applications such as face recognition and industrial machine vision. TMAs with tissue samples from 643 patients with colorectal cancer were digitized using a whole slide scanner and areas representing epithelium and stroma were annotated in the images. Well-defined images of epithelium (n = 41) and stroma (n = 39) were used for training a support vector machine (SVM) classifier with LBP texture features and a contrast measure C (LBP/C) as input. We optimized the classifier on a validation set (n = 576) and then assessed its performance on an independent test set of images (n = 720). Finally, the performance of the LBP/C classifier was evaluated against classifiers based on Haralick texture features and Gabor filtered images.</p> <p>Results</p> <p>The proposed approach using LPB/C texture features was able to correctly differentiate epithelium from stroma according to texture: the agreement between the classifier and the human observer was 97 per cent (kappa value = 0.934, <it>P </it>< 0.0001) and the accuracy (area under the ROC curve) of the LBP/C classifier was 0.995 (CI95% 0.991-0.998). The accuracy of the corresponding classifiers based on Haralick features and Gabor-filter images were 0.976 and 0.981 respectively.</p> <p>Conclusions</p> <p>The method illustrates the capability of automated segmentation of epithelial and stromal tissue in TMAs based on texture features and an SVM classifier. Applications include tissue specific assessment of gene and protein expression, as well as computerized analysis of the tumor microenvironment.</p> <p>Virtual slides</p> <p>The virtual slide(s) for this article can be found here: <url>http://www.diagnosticpathology.diagnomx.eu/vs/4123422336534537</url></p

    Texture Analysis of Late Gadolinium Enhanced Cardiac Magnetic Resonance Images for Characterizing Myocardial Fibrosis and Infarction

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    Le tiers de la population aux États-Unis est affectĂ© par des cardiomyopathies. Lorsque le muscle du coeur, le myocarde, est altĂ©rĂ© par la maladie, la santĂ© du patient est dĂ©tĂ©riorĂ©e et peut mĂȘme entrainer la mort. Les maladies ischĂ©miques sont le rĂ©sultat d’artĂšres coronariennes bloquĂ©es (stĂ©nose), limitant l’apport sanguin vers le myocarde. Les cardiomyopathies non-ischĂ©miques sont les maladies dues Ă  d’autres causes que des stĂ©noses. Les fibres de collagĂšne (fibrose) s’infiltrent dans le muscle cardiaque dans le but de maintenir la forme et les fonctions cardiaques lorsque la structure du myocarde est affectĂ©e par des cardiomyopathies. Ce principe, nĂ©cessaire au fonctionnement du coeur en prĂ©sence de maladies, devient mal adaptĂ© et mĂšne Ă  des altĂ©rations du myocarde aux consĂ©quences nĂ©gatives, par exemple l’augmentation de la rigiditĂ© du myocarde. Une partie du diagnostic clinique lors de cardiomyopathies consiste Ă  Ă©valuer la fibrose dans le coeur avec diffĂ©rentes modalitĂ©s d’imagerie. Les fibres de collagĂšne s’infiltrent et s’accumulent dans la zone extracellulaire du myocarde ou peuvent remplacer progressivement les cardiomyocytes compromises. L’infiltration de fibrose dans le myocarde peut possiblement ĂȘtre rĂ©versible, ce qui rend sa dĂ©tection particuliĂšrement importante pour le clinicien. DiffĂ©rents tests diagnostiques existent pour aider le clinicien Ă  Ă©tablir l’état du patient en prĂ©sence de cardiomyopathies. L’imagerie par rĂ©sonance magnĂ©tique (IRM) est une modalitĂ© d’imagerie qui offre une haute rĂ©solution pour la visualisation du myocarde. Parmi les sĂ©quences disponibles avec cette modalitĂ©, l’imagerie par rehaussement tardif (RT) augmente le contraste du signal existant entre les tissus sains et les tissues malades du myocarde. Il s’agit d’images en pondĂ©ration T1 avec administration d’agent de contraste qui se propage dans la matrice extracellulaire et rĂ©sulte en un rehaussement du signal Ă  cet endroit. Les images IRM RT permettent d’évaluer la prĂ©sence et l’étendue des dommages au myocarde. Le clinicien peut Ă©valuer la sĂ©vĂ©ritĂ© des cardiomyopathies et poser un pronostique Ă  l’aide de ces images. La dĂ©tection de fibrose diffuse dans ces images peut informer le clinicien sur l’état du patient et est un important marqueur de cardiomyopathies. Il est important d’établir l’occurrence de l’infarctus en prĂ©sence de maladies ischĂ©miques. En effet, l’approche interventionnelle varie selon que le clinicien fait face Ă  une ischĂ©mie aigue ou chronique. Lors du diagnostic, Il serait donc bĂ©nĂ©fique de diffĂ©rencier les infarctus du myocarde aigu de ceux chronique. Ceci s’est avĂ©rĂ© difficile Ă  l’aide des images IRM RT oĂč l’intensitĂ© du signal ou la taille des rĂ©gions sont similaires dans les deux types d’ischĂ©mie. Le but de la prĂ©sente thĂšse est donc d’appliquer les mĂ©thodes d’analyse de texture Ă  des images IRM RT afin de dĂ©tecter la prĂ©sence de fibrose diffuse dans le myocarde et de plus de dĂ©terminer l’ñge de l’infarctus du myocarde. La premiĂšre Ă©tude portait sur la dĂ©tection de fibrose diffuse dans le myocarde Ă  l’aide de l’analyse de texture appliquĂ©e Ă  des images IRM RT afin d’établir si un lien existe entre la variation du signal d’intensitĂ© et la structure sous-jacente du myocarde. La prĂ©sence de collagĂšne dans le myocarde augmente avec l’ñge et nous avons utilisĂ© un modĂšle animal de rats jeunes et ĂągĂ©s. Nous avons fait une Ă©tude ex-vivo afin d’obtenir des images IRM RT de haute rĂ©solution avec absence de mouvement et ainsi permettre une comparaison des images avec des coupes histologiques des coeurs imagĂ©s. Des images IRM RT ont Ă©tĂ© acquises sur vingt-quatre animaux. Les coupes histologiques ont Ă©tĂ© traitĂ©es avec la mĂ©thode utilisant un marqueur ‘picrosirius red’ qui donne une teinte rouge au collagĂšne. La quantification de la fibrose obtenue avec les images IRM RT a Ă©tĂ© comparĂ©e Ă  la quantification obtenue sur les coupes histologiques. Ces quantifications ont de plus Ă©tĂ© comparĂ©es Ă  l’analyse de texture appliquĂ©e aux images IRM RT. La mĂ©thode de texture a Ă©tĂ© appliquĂ©e en crĂ©ant des cartes de texture basĂ©es sur la valeur de Contraste, cette mesure Ă©tant obtenue par des calculs statistiques sur la matrice de cooccurrence. Les rĂ©gions montrant une plus grande complexitĂ© de signal d’intensitĂ© sur les images IRM RT ont Ă©tĂ© rehaussĂ©es avec les cartes de textures. Un calcul de rĂ©gression linĂ©aire a permis d’étudier le lien entre les diffĂ©rentes mĂ©thodes de quantification. Nous avons trouvĂ©s que la quantification de fibrose dans le myocarde Ă  l’aide de l’analyse de texture appliquĂ©e sur des images IRM RT concordait avec le niveau de collagĂšne identifiĂ© avec les images IRM et avec les coupes histologiques. De plus, nous avons trouvĂ©s que l’analyse de texture rehausse la prĂ©sence de fibrose diffuse dans le myocarde. La seconde Ă©tude a pour but de discriminer les infarctus aigus du myocarde de ceux qui sont chroniques sur des images IRM RT de patients souffrant de cardiomyopathies ischĂ©miques. Vingt-deux patients ont subi l’imagerie IRM (12 avec infarctus aigu du myocarde et 12 avec infarctus chronique). Une segmentation des images a permis d’isoler les diffĂ©rentes zones du myocarde, soit la zone d’infarctus, la zone grise au rebord de l’infarctus et la zone du myocarde sain, dans les deux groupes de patients. L’analyse de texture s’est faite dans ces rĂ©gions en comparant les valeurs obtenues dans les deux groupes. Nous avons obtenu plus de valeurs de texture discriminantes dans la zone grise, en comparaison avec la rĂ©gion du myocarde sain, oĂč aucune valeur de texture n’était significativement diffĂ©rente, et Ă  la zone d’infarctus, oĂč seule la valeur de texture statistique Moyenne Ă©tait diffĂ©rentes dans les deux groupes. La zone grise a dĂ©jĂ  fait l’objet d’études ayant Ă©tablis cette rĂ©gion comme composĂ©e de cardiomyocytes sains entremĂȘlĂ©s avec des fibres de collagĂšne. Notre Ă©tude montre que cette rĂ©gion peut exhiber des diffĂ©rences structurelles entre les infarctus aigus du myocarde et ceux qui sont chroniques et que l’analyse de texture a rĂ©ussi Ă  les dĂ©tecter. L’étude de la prĂ©sence de collagĂšne dans le myocarde est importante pour le clinicien afin qu’il puisse faire un diagnostic adĂ©quat du patient et pour qu’il puisse faire un choix de traitement appropriĂ©. Nous avons montrĂ©s que l’analyse de texture sur des images IRM RT de patients peut diffĂ©rencier et mĂȘme permettre la classification des ischĂ©mies aigues des ischĂ©mies chroniques, ce qui n’était pas possible avec uniquement ce type d’images. Nous avons de plus dĂ©montrĂ©s que l’analyse de texture d’images IRM RT permettait d’évaluer le contenu de fibrose diffuse dans un modĂšle animal de haute rĂ©solution avec validation histologique. Une telle relation entre les rĂ©sultats d’analyse de texture d’images IRM RT et la structure sous-jacente du myocarde n’avait pas Ă©tĂ© Ă©tudiĂ©e dans la littĂ©rature. Notre mĂ©thode pourra ĂȘtre amĂ©liorĂ©e en effectuant d’autres calculs statistiques sur la matrice de cooccurrence, en testant d’autres mĂ©thodes d’analyse de texture et en appliquant notre mĂ©thode Ă  de nouvelles sĂ©quences d’acquisition IRM, tel les images en pondĂ©ration T1. D’autres amĂ©liorations possibles pourraient porter sur une Ă©valuation de matrice de cooccurrence avec voisinage circulaire suivant la forme du myocarde sur les tranches d’images IRM RT. Plusieurs matrice de cooccurrence pourraient aussi ĂȘtre Ă©valuĂ©es en fonction de la position dans l’espace du voisinage afin d’intĂ©grer une composante directionnelle dans les calculs de texture. D’autres Ă©tudes sont nĂ©cessaires afin d’établir si une analyse de texture des images IRM RT pourrait diffĂ©rencier le stade de la fibrose pour un mĂȘme patient lors d’une Ă©tude de suivi. De mĂȘme, d’autres Ă©tudes sont nĂ©cessaires afin de valider l’utilisation de texture sur des scanners IRM diffĂ©rents. Établir l’ñge de l’infarctus du myocarde permettra de planifier les interventions thĂ©rapeutiques et d’évaluer le pronostique pour le patient.----------ABSTRACT A third of the United States population is affected by cardiomyopathies. Impairment of the heart muscle, the myocardium, puts the patient’s health at risk and could ultimately lead to death. Ischemic cardiomyopathies result from lack of blood (ischemia) reaching the myocardium from blocked coronary arteries. Non-ischemic cardiomyopathies are diseases from other etiology than ischemia. Often collagen fibers infiltrate the heart (fibrosis), as a means to maintain its shape and function in the presence of disease that affects the myocardial cellular structure. This necessary phenomenon ultimately becomes maladaptive and results in the heart’s impairment. Part of the heart’s involvement in disease can be assessed through the analysis of myocardial fibrosis. Cardiomyopathy diagnosis involves the investigation of the presence of myocardial fibrosis, either infiltrative, defined as the increased presence of collagen protein in the extracellular space, or replacement fibrosis, when collagen fibers progressively replace diseased cardiomyocytes. The infiltrative fibrosis is believed to be reversible in some instances and consequently, myocardial fibrosis analysis has decisional impact on the interventional procedure that would benefit the health of the patient. The heart contracts and relaxes as it pumps blood to the rest of the body, an action directly impaired by myocardial damage. Any myocardial involvement should be assessed by the clinician to identify the severity of the myocardial damage, establish a prognosis and plan therapeutic intervention. Different diagnostic tests are required to image the myocardium and help the clinician in the diagnostic process. Cardiac magnetic resonance (CMR) imaging has emerged as a high resolution imaging modality that offers precise structural analysis of the heart. Among the different imaging sequences available with CMR, late gadolinium enhancement (LGE) shows the myocardium and enhances any impairments that may exist with the use of a contrast agent. It is a T1-weighted image with extracellular contrast agent (CA) administration. Increased signal intensity in the infarct scar is created from the CA dynamics. LGE CMR imaging offers information on the scar size and its location. The clinician can estimate the severity of the disease and establish prognosis with LGE CMR images. In ischemic cardiomyopathy, it is important to establish the occurrence of the infarction and know the age of the infarct to plan surgical intervention. Differentiation of acute from chronic MI is therefore important in the diagnostic process. In LGE CMR the level of signal intensity or the size of infarction are both similar in acute or in chronic MI. It has therefore been challenging to distinguish acute MI from chronic MI scars with LGE CMR images alone. The aim of this thesis was to investigate texture analysis of LGE CMR images to determine if acute MI could be distinguished from chronic MI and to detect increased presence of diffuse myocardial fibrosis in the myocardium. The first study was performed to investigate if texture analysis of LGE CMR images could detect variations in the presence of diffuse myocardial fibrosis and if the underlying myocardial structure could be related to the texture measures. Collagen content increased with aging and we used an animal model of young versus old rat. An ex-vivo animal model was necessary to allow for higher image resolution in LGE CMR images and to perform validation of our texture measures with histology images. Twenty four animals were scanned for LGE CMR images and texture analysis was applied to the heart images. Histology slices were stained with picrosirius red and collagen fibers were isolated based on their color content. LGE CMR quantification was compared to histological slices of the heart stained with the picrosirius red method. Texture analysis of LGE CMR images was also compared to the original LGE CMR image quantification and to histology. Texture analysis was done by creating contrast texture maps extracted from Haralick’s gray level co-occurrence matrix (GLCM). Regions of complex signal intensity combination were enhanced in LGE CMR images and in contrast texture maps. Regression analysis was performed to assess the level of agreement between the different analysis methods. We found that LGE CMR images could assess the different levels of collagen content in the different aged animal model, and that moreover texture analysis enhanced those differences. The location of enhancement from texture analysis images corresponded to location of increased collagen content in the old compared to the young rat hearts. Histological validation was shown for texture analysis applied to LGE CMR images to assess myocardial fibrosis. Our second study aimed at discriminating acute versus chronic MI from LGE CMR patient images alone through the use of texture analysis. Twenty two patients who had LGE CMR images were included in our study (12 acute and 12 chronic MI). Regional segmentation was performed and texture features were compared in those regions between both groups of patient. Texture analysis resulted in significantly different values between the two groups. More specifically the peri-infarct zone had the most number of discriminative features compared to the remote myocardium which had none and to the infarct core where only the mean features was significantly different. The border zone has been shown to be composed of healthy cardiomyocytes intermingled with the scar’s collagen fibers. Our study indicates this region might exhibit structural differences in the myocardium in acute from chronic MI patients that texture analysis of LGE CMR images can detect. Characterization of myocardial collagen content is important while clinicians analyze the state of the patient since it influences the course of action required to treat cardiomyopathies. LGE CMR images have been thoroughly used and validated to characterize focal myocardial scar, however it was limited in characterizing the age of infarction or quantifying diffuse collagen content. We have shown texture analysis of LGE CMR images alone can differentiate and even classify, acute from chronic MI patients, which was not previously possible. Characterization of myocardial infarction according to age will prove important in planning therapeutic interventions in clinical practice. Moreover, we have established texture analysis as a means to characterize the myocardium and detect variation in fibrosis content from high resolution LGE CMR images with histology validation. To our knowledge, such a relation between texture analysis of LGE CMR images and the underlying myocardial structure had not been done previously. Improvements could be done to our method, as we can increase the number of texture features that were analyzed from the GLCM, include other texture analysis methods such as the run-length matrix, and apply our method to other CMR imaging sequences such as T1 mapping. Adapting the GLCM to the heart could also be investigated, such as considering circular GLCM computation to consider the round shape of the myocardium in the short axis LGE CMR image slices. Directional GLCM could also be computed individually and analyzed for any myocardial or collagen fiber orientation indication. Further analysis is also required to establish if texture analysis could differentiate the age of MI in the same individual through a follow-up study. The measures of texture analysis from LGE CMR images obtained through different CMR scanners remains to be investigated as well. Knowing the age of infarct and evaluating the presence of diffuse myocardial fibrosis will help the clinician plan therapeutic interventions and establish a prognosis for the patient

    Analysis of contrast-enhanced medical images.

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    Early detection of human organ diseases is of great importance for the accurate diagnosis and institution of appropriate therapies. This can potentially prevent progression to end-stage disease by detecting precursors that evaluate organ functionality. In addition, it also assists the clinicians for therapy evaluation, tracking diseases progression, and surgery operations. Advances in functional and contrast-enhanced (CE) medical images enabled accurate noninvasive evaluation of organ functionality due to their ability to provide superior anatomical and functional information about the tissue-of-interest. The main objective of this dissertation is to develop a computer-aided diagnostic (CAD) system for analyzing complex data from CE magnetic resonance imaging (MRI). The developed CAD system has been tested in three case studies: (i) early detection of acute renal transplant rejection, (ii) evaluation of myocardial perfusion in patients with ischemic heart disease after heart attack; and (iii), early detection of prostate cancer. However, developing a noninvasive CAD system for the analysis of CE medical images is subject to multiple challenges, including, but are not limited to, image noise and inhomogeneity, nonlinear signal intensity changes of the images over the time course of data acquisition, appearances and shape changes (deformations) of the organ-of-interest during data acquisition, determination of the best features (indexes) that describe the perfusion of a contrast agent (CA) into the tissue. To address these challenges, this dissertation focuses on building new mathematical models and learning techniques that facilitate accurate analysis of CAs perfusion in living organs and include: (i) accurate mathematical models for the segmentation of the object-of-interest, which integrate object shape and appearance features in terms of pixel/voxel-wise image intensities and their spatial interactions; (ii) motion correction techniques that combine both global and local models, which exploit geometric features, rather than image intensities to avoid problems associated with nonlinear intensity variations of the CE images; (iii) fusion of multiple features using the genetic algorithm. The proposed techniques have been integrated into CAD systems that have been tested in, but not limited to, three clinical studies. First, a noninvasive CAD system is proposed for the early and accurate diagnosis of acute renal transplant rejection using dynamic contrast-enhanced MRI (DCE-MRI). Acute rejection–the immunological response of the human immune system to a foreign kidney–is the most sever cause of renal dysfunction among other diagnostic possibilities, including acute tubular necrosis and immune drug toxicity. In the U.S., approximately 17,736 renal transplants are performed annually, and given the limited number of donors, transplanted kidney salvage is an important medical concern. Thus far, biopsy remains the gold standard for the assessment of renal transplant dysfunction, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The diagnostic results of the proposed CAD system, based on the analysis of 50 independent in-vivo cases were 96% with a 95% confidence interval. These results clearly demonstrate the promise of the proposed image-based diagnostic CAD system as a supplement to the current technologies, such as nuclear imaging and ultrasonography, to determine the type of kidney dysfunction. Second, a comprehensive CAD system is developed for the characterization of myocardial perfusion and clinical status in heart failure and novel myoregeneration therapy using cardiac first-pass MRI (FP-MRI). Heart failure is considered the most important cause of morbidity and mortality in cardiovascular disease, which affects approximately 6 million U.S. patients annually. Ischemic heart disease is considered the most common underlying cause of heart failure. Therefore, the detection of the heart failure in its earliest forms is essential to prevent its relentless progression to premature death. While current medical studies focus on detecting pathological tissue and assessing contractile function of the diseased heart, this dissertation address the key issue of the effects of the myoregeneration therapy on the associated blood nutrient supply. Quantitative and qualitative assessment in a cohort of 24 perfusion data sets demonstrated the ability of the proposed framework to reveal regional perfusion improvements with therapy, and transmural perfusion differences across the myocardial wall; thus, it can aid in follow-up on treatment for patients undergoing the myoregeneration therapy. Finally, an image-based CAD system for early detection of prostate cancer using DCE-MRI is introduced. Prostate cancer is the most frequently diagnosed malignancy among men and remains the second leading cause of cancer-related death in the USA with more than 238,000 new cases and a mortality rate of about 30,000 in 2013. Therefore, early diagnosis of prostate cancer can improve the effectiveness of treatment and increase the patient’s chance of survival. Currently, needle biopsy is the gold standard for the diagnosis of prostate cancer. However, it is an invasive procedure with high costs and potential morbidity rates. Additionally, it has a higher possibility of producing false positive diagnosis due to relatively small needle biopsy samples. Application of the proposed CAD yield promising results in a cohort of 30 patients that would, in the near future, represent a supplement of the current technologies to determine prostate cancer type. The developed techniques have been compared to the state-of-the-art methods and demonstrated higher accuracy as shown in this dissertation. The proposed models (higher-order spatial interaction models, shape models, motion correction models, and perfusion analysis models) can be used in many of today’s CAD applications for early detection of a variety of diseases and medical conditions, and are expected to notably amplify the accuracy of CAD decisions based on the automated analysis of CE images

    A Geneaology of Correspondence Analysis: Part 2 - The Variants

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    In 2012, a comprehensive historical and genealogical discussion of correspondence analysis was published in Australian and New Zealand Journal of Statistics. That genealogy consisted of more than 270 key books and articles and focused on an historical development of the correspondence analysis,a statistical tool which provides the analyst with a visual inspection of the association between two or more categorical variables. In this new genealogy, we provide a brief overview of over 30 variants of correspondence analysis that now exist outside of the traditional approaches used to analysethe association between two or more categorical variables. It comprises of a bibliography of a more than 300 books and articles that were not included in the 2012 bibliography and highlights the growth in the development ofcorrespondence analysis across all areas of research

    Scandinavian Workshop on Imaging Food Quality 2011:Ystad, May 27, 2011 - Proceedings

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    Computerized cancer malignancy grading of fine needle aspirates

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    According to the World Health Organization, breast cancer is a leading cause of death among middle-aged women. Precise diagnosis and correct treatment significantly reduces the high number of deaths caused by breast cancer. Being successful in the treatment strictly relies on the diagnosis. Specifically, the accuracy of the diagnosis and the stage at which a cancer was diagnosed. Precise and early diagnosis has a major impact on the survival rate, which indicates how many patients will live after the treatment. For many years researchers in medical and computer science fields have been working together to find the approach for precise diagnosis. For this thesis, precise diagnosis means finding a cancer at as early a stage as possible by developing new computer aided diagnostic tools. These tools differ depending on the type of cancer and the type of the examination that is used for diagnosis. This work concentrates on cytological images of breast cancer that are produced during fine needle aspiration biopsy examination. This kind of examination allows pathologists to estimate the malignancy of the cancer with very high accuracy. Malignancy estimation is very important when assessing a patients survival rate and the type of treatment. To achieve precise malignancy estimation, a classification framework is presented. This framework is able to classify breast cancer malignancy into two malignancy classes and is based on features calculated according to the Bloom-Richardson grading scheme. This scheme is commonly used by pathologists when grading breast cancer tissue. In Bloom-Richardson scheme two types of features are assessed depending on the magnification. Low magnification images are used for examining the dispersion of the cells in the image while the high magnification images are used for precise analysis of the cells' nuclear features. In this thesis, different types of segmentation algorithms were compared to estimate the algorithm that allows for relatively fast and accurate nuclear segmentation. Based on that segmentation a set of 34 features was extracted for further malignancy classification. For classification purposes 6 different classifiers were compared. From all of the tests a set of the best preforming features were chosen. The presented system is able to classify images of fine needle aspiration biopsy slides with high accurac

    Multimodal image analysis of the human brain

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    Gedurende de laatste decennia heeft de snelle ontwikkeling van multi-modale en niet-invasieve hersenbeeldvorming technologieën een revolutie teweeg gebracht in de mogelijkheid om de structuur en functionaliteit van de hersens te bestuderen. Er is grote vooruitgang geboekt in het beoordelen van hersenschade door gebruik te maken van Magnetic Reconance Imaging (MRI), terwijl Elektroencefalografie (EEG) beschouwd wordt als de gouden standaard voor diagnose van neurologische afwijkingen. In deze thesis focussen we op de ontwikkeling van nieuwe technieken voor multi-modale beeldanalyse van het menselijke brein, waaronder MRI segmentatie en EEG bronlokalisatie. Hierdoor voegen we theorie en praktijk samen waarbij we focussen op twee medische applicaties: (1) automatische 3D MRI segmentatie van de volwassen hersens en (2) multi-modale EEG-MRI data analyse van de hersens van een pasgeborene met perinatale hersenschade. We besteden veel aandacht aan de verbetering en ontwikkeling van nieuwe methoden voor accurate en ruisrobuuste beeldsegmentatie, dewelke daarna succesvol gebruikt worden voor de segmentatie van hersens in MRI van zowel volwassen als pasgeborenen. Daarenboven ontwikkelden we een geïntegreerd multi-modaal methode voor de EEG bronlokalisatie in de hersenen van een pasgeborene. Deze lokalisatie wordt gebruikt voor de vergelijkende studie tussen een EEG aanval bij pasgeborenen en acute perinatale hersenletsels zichtbaar in MRI

    Dynamic Thermal Imaging for Intraoperative Monitoring of Neuronal Activity and Cortical Perfusion

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    Neurosurgery is a demanding medical discipline that requires a complex interplay of several neuroimaging techniques. This allows structural as well as functional information to be recovered and then visualized to the surgeon. In the case of tumor resections this approach allows more fine-grained differentiation of healthy and pathological tissue which positively influences the postoperative outcome as well as the patient's quality of life. In this work, we will discuss several approaches to establish thermal imaging as a novel neuroimaging technique to primarily visualize neural activity and perfusion state in case of ischaemic stroke. Both applications require novel methods for data-preprocessing, visualization, pattern recognition as well as regression analysis of intraoperative thermal imaging. Online multimodal integration of preoperative and intraoperative data is accomplished by a 2D-3D image registration and image fusion framework with an average accuracy of 2.46 mm. In navigated surgeries, the proposed framework generally provides all necessary tools to project intraoperative 2D imaging data onto preoperative 3D volumetric datasets like 3D MR or CT imaging. Additionally, a fast machine learning framework for the recognition of cortical NaCl rinsings will be discussed throughout this thesis. Hereby, the standardized quantification of tissue perfusion by means of an approximated heating model can be achieved. Classifying the parameters of these models yields a map of connected areas, for which we have shown that these areas correlate with the demarcation caused by an ischaemic stroke segmented in postoperative CT datasets. Finally, a semiparametric regression model has been developed for intraoperative neural activity monitoring of the somatosensory cortex by somatosensory evoked potentials. These results were correlated with neural activity of optical imaging. We found that thermal imaging yields comparable results, yet doesn't share the limitations of optical imaging. In this thesis we would like to emphasize that thermal imaging depicts a novel and valid tool for both intraoperative functional and structural neuroimaging
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