8 research outputs found

    Brain lesion segmentation from diffusion weighted MRI based on adaptive thresholding and gray level co-occurrence matrix

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    This project presents brain lesion segmentation of diffusion-weighted magnetic resonance images (DWI) based on thresholding technique and gray level co-occurrence matrix (GLCM). The lesions are hyperintense lesion from tumour, acute infarction, haemorrhage and abscess, and hypointense lesion from chronic infarction and haemorrhage. Pre-processing is applied to the DWI for intensity normalization, background removal and intensity enhancement. Then, the lesions are segmented by using two different methods which are thresholding technique and GLCM. For the thresholding technique, image histogram is calculated at each region to find the maximum number of pixels for each intensity level. The optimal threshold is determined by comparing normal and lesion regions. Conversely, GLCM is computed to segment the lesions. Different peaks from the GLCM crosssection indicate the present of normal brain region, cerebral spinal fluid (CSF), hyperintense or hypointense lesions. Minimum and maximum threshold values are computed from the GLCM cross-section. Region and boundary information from the GLCM are introduced as the statistical features for segmentation of hyperintense and hypointense lesions. The proposed technique has been validated by using area overlap (AO), false positive rate (FPR), false negative rate (FNR), misclassified area (MA), mean absolute percentage error (MAPE) and pixels absolute error ratio (rerr). The results are demonstrated in three indexes MA, MAPE and rerr, where 0.3167, 0.1440 and 0.0205 for GLCM, while 0.3211, 0.1524 and 0.0377 for thresholding technique. Overall, GLCM provides better segmentation performance compared to thresholding technique

    Brain Lesion Segmentation from Diffusion-Weighted MRI Based on Adaptive Thresholding and Gray Level Co-Occurence Matrix

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    This project presents brain lesion segmentation of diffusion-weighted magnetic resonance images (DWI) based on thresholding technique and gray level co-occurrence matrix (GLCM). The lesions are hyperintense lesion from tumour, acute infarction, haemorrhage and abscess, and hypointense lesion from chronic infarction and haemorrhage. Pre-processing is applied to the DWI for intensity normalization, background removal and intensity enhancement. Then, the lesions are segmented by using two different methods which are thresholding technique and GLCM. For the thresholding technique, image histogram is calculated at each region to find the maximum number of pixels for each intensity level. The optimal threshold is determined by comparing normal and lesion regions. Conversely, GLCM is computed to segment the lesions. Different peaks from the GLCM crosssection indicate the present of normal brain region, cerebral spinal fluid (CSF), hyperintense or hypointense lesions. Minimum and maximum threshold values are computed from the GLCM cross-section. Region and boundary information from the GLCM are introduced as the statistical features for segmentation of hyperintense and hypointense lesions. The proposed technique has been validated by using area overlap (AO), false positive rate (FPR), false negative rate (FNR), misclassified area (MA), mean absolute percentage error (MAPE) and pixels absolute error ratio (r err) . The results are demonstrated in three indexes MA, MAPE and r err , where 0.3167, 0.1440 and 0.0205 for GLCM, while 0.3211, 0.1524 and 0.0377 for thresholding technique. Overall, GLCM provides better segmentation performance compared to thresholding techniqu

    Probabilistic partial volume modelling of biomedical tomographic image data

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    EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Visual attention models and arse representations for morphometrical image analysis

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    Abstract. Medical diagnosis, treatment, follow-up and research activities are nowadays strongly supported on different types of diagnostic images, whose main goal is to provide an useful exchange of medical knowledge. This multi-modal information needs to be processed in order to extract information exploitable within the context of a particular medical task. In despite of the relevance of these complementary sources of medical knowledge, medical images are rarely further processed in actual clinical practice, so the specialists take decisions only based in the raw data. A new trend in the development of medical image processing and analysis tools follows the idea of biologically-inspired methods, which resemble the performance of the human vision system. Visual attention models and sparse representations are examples of this tendency. Based on this, the aim of this thesis was the development of a set of computational methods for automatic morph metrical analysis, combining the relevant region extraction power of visual attention models with the incorporation of a priori information capabilities of sparse representations. The combination of these biologically inspired tools with common machine learning techniques allowed the identification of visual patterns relevant for pathology discrimination, improving the accuracy and interpretability of morph metric measures and comparisons. After extensive validations with different image data sets, the computational methods proposed in this thesis seems to be promising tools for the definition of anatomical biomarkers, based on visual pattern analysis, and suitable for patient's diagnosis, prognosis and follow-up.Las actividades de diagnóstico, tratamiento, seguimiento e investigación en medicina están actualmente soportadas en diferentes clases de imágenes diagnósticas, cuyo objetivo principal es el de proveer un intercambio efectivo de conocimiento médico. Esta información multimodal necesita ser procesada con el objetivo de extraer información aprovechable en el contexto de una tarea médica particular. A pesar de la relevancia de estas fuentes complementarias de información clínica, las imágenes médicas son raramente procesadas en la práctica clínica actual, de forma que los especialistas sólo toman decisiones basados en los datos crudos. Una nueva tendencia en el desarrollo de herramientas de análisis y procesamiento de imágenes médicas persigue la idea de métodos biológicamente inspirados, que se asemejan al sistema de visión humana. Son ejemplos de esta tendencia los modelos de atención visual y las representaciones escasas (sparse representations). Con base en esto, el objetivo de esta tesis fue el desarrollo de un conjunto de métodos computacionales para soportar automáticamente los análisis morfo métricos, combinando el poder de extracción de regiones relevantes de los modelos de atención visual junto con la capacidad de incorporación de información a priori de las representaciones escasas. La combinación de estos métodos biológicamente inspirados con técnicas de aprendizaje de maquina facilito la identificación de patrones visuales relevantes para discriminar patologías cerebrales, mejorando la precisión e interpretabilidad de las medidas y comparaciones morfo métricas. Después de extensivas validaciones con diferentes conjuntos de imágenes, los métodos computacionales propuestos en esta tesis se perfilan como herramientas prometedoras para la definición de biomarcadores anatómicos, basados en el análisis visual de patrones, y convenientes para el diagnóstico, pronóstico y seguimiento del paciente.Doctorad

    Construction d'un modèle per-opératoire 3D du rachis pour la navigation en thoracoscopie.

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    RÉSUMÉ: Lors de discectomie par thoracoscopie, les outils de visualisation procurent peu d’information de profondeur et le champ de visualisation de la caméra miniature insérée dans le patient est relativement restreint. Aussi, le mouvement simultané de la caméra et des instruments chirurgicaux peut provoquer une désorientation. Ainsi, la courbe d’apprentissage pour l’utilisation de cette technologie est très abrupte et un nombre restreint de chirurgiens choisissent l’intervention minimalement invasive malgré les avantages qu’elle peut procurer aux patients. En effet la discectomie par thoracoscopie réduit les pertes sanguines, le traumatisme des tissus entourant le disque afin d’accéder à la zone d’intérêt et le temps d’hospitalisation. Les discectomies sont prescrites à certains patients scoliotiques afin de redonner de la flexibilité à la colonne avant l’instrumentation (pose de vis et tige pour corriger la déformation). La résection du disque intervertébral est faite partiellement et la quantité du disque réséqué dépend du degré de flexibilité que le chirurgien désire redonner au patient. En effectuant la discectomie par thoracoscopie, il est impossible pour le chirurgien de visualiser rapidement la quantité de disque restant en plus d’avoir les désavantages de désorientation et de petit champ de vision de la caméra miniature insérée dans le patient. Il est donc pertinent de tenter de réduire les problèmes de visualisation rencontrés lors des thoracoscopies en procurant au chirurgien la possibilité d’examiner en 3D les structures anatomiques du patient pendant la chirurgie sans ajouter de radiations supplémentaires au patient. Ce système d’assistance permettrait également d’accroître la sécurité du patient et la qualité de la chirurgie en donnant aux chirurgiens la possibilité de localiser en 3D la moelle épinière et en leur donnant également la possibilité de visualiser la quantité de disque restant. Ainsi, l’intérêt de fusionner les images vidéo avec un modèle pré-opératoire 3D est alors tout indiqué.---------- ABSTRACT: Visualization tools available while doing thoracoscopic diskectomy do not show depth information and the field of view of the miniaturized camera inserted into the patient is small. Also, simultaneous movement of the camera and surgical tools may result in disorientation. The learning curve for the use of this technology is very steep and numbers of surgeons choose not to use minimally invasive surgery despite important advantages for the patients. Indeed, thoracoscopic diskectomy reduce blood loss, trauma of surrounding soft tissues to access intervertebral disks and hospitalization time. Diskectomy are prescribed to specific scoliotic patients to gain flexibility of the spine before instrumentation surgery (fixation of screws and rod to correct the deformation). The intervertebral disk is partly resected depending on the level of flexibility the patient has to gain according to the surgeon. During thoracoscopic diskectomy, it is impossible for the surgeon to rapidly visualize the remaining disk tissue and this further increase the disadvantages for the surgeons. Hence, it is relevant to try to reduce visualization problems encountered during thoracoscopic diskectomy by providing to the surgeons a 3D view of the whole spine during the surgery, without adding supplementary radiation to the patient. The computer assisted surgery system would also increase the security of the patient by allowing the surgeons to localize rapidly in 3D the spinal canal as well as the remaining disk. The fusion of the video images with 3D spine of the patient is of great interest for the surgeons
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