A study of wavelet-based noise reduction techniques in mammograms

Breast cancer is one of the most common cancers and claims over one thousand lives every day. Breast cancer turns fatal only when diagnosed in late stages, but can be cured when diagnosed in its early stages. Over the last two decades, Digital Mammography has served the diagnosis of breast cancer. It is a very powerful aid for early detection of breast cancer. However, the images produced by mammography typically contain a great amount noise from the inherent characteristics of the imaging system and the radiation involved. Shot noise or quantum noise is the most significant noise which emerges as a result of uneven distribution of incident photons on the receptor. The X-ray dose given to patients must be minimized because of the risk of exposure. This noise present in mammograms manifests itself more when the dose of X-ray radiation is less and therefore needs to be treated before enhancing the mammogram for contrast and clarity. Several approaches have been taken to reduce the amount of noise in mammograms. This thesis presents a study of the wavelet-based techniques employed for noise reduction in mammograms --Abstract, page iii

Modélisation pharmacocinétique en tomographie d'émission par positrons en utilisant la technique des ondelettes

Dans le cadre de ce travail de recherche, les objectifs étaient de mettre en oeuvre et de valider la technique des ondelettes dans la modélisation pharmacocinétique chez le rat par tomographie d'émission par positrons (TEP). En TEP, le métabolisme du glucose dans l'organe étudié est mesuré en injectant un analogue du glucose, le fluorodéoxyglucose ([indice supérieur 18]FDG). La quantité de radioactivité injectée est mesurée dans le plasma sanguin en fonction du temps et constitue la courbe d'entrée, tandis que la radioactivité mesurée dans les tissus à l'aide de la TEP constitue la réponse des tissus. Avec la courbe d'entrée et l'intensité de la radioactivité dans les tissus telle que mesurée par le tomographe, le métabolisme du glucose est calculé à l'aide d'un modèle mathématique compartimental. Ce calcul se fait habituellement sur des images reconstruites filtrées ou itérées. Cependant, ces images filtrées ont perdu la résolution spatiale ou contiennent encore du bruit dû à la faible dose de radioactivité injectée ou le temps restreint de la mesure. Dans ce travail, nous proposons la technique des ondelettes basée sur des algorithmes de compression et de filtrage qui s'avèrent performants et faciles à utiliser. De plus, à partir des images filtrées et compressées par les ondelettes, nous calculons le métabolisme du glucose pixel par pixel, afin de générer une image appelée l'image paramétrique qui permet une visualisation du métabolisme du glucose dans les différentes structures d'un organe. Nous avons appliqué la technique des ondelettes autant sur les images que sur les projections, c'est-à-dire directement sur les matrices de projections avant de reconstruire les images pour éviter le filtrage des mesures et les opérations de reconstruction. Les ondelettes ont l'avantage de réduire les matrices et de grouper les intensités des pixels, procurant une meilleure statistique, donc plus de précision, et par conséquent une meilleure qualité des images paramétriques. La technique des ondelettes a été introduite également pour la correction du volume partiel en imagerie TEP. L'effet du volume partiel survient lorsque la radioactivité des structures, dont la taille est inférieure à la résolution spatiale du tomographe, est sous-estimée. La méthode des ondelettes continues représente une alternative aux méthodes habituellement utilisées, basées sur les informations anatomiques qui proviennent de l'imagerie par résonance magnétique (IRM) ou de tomodensitométrie (TDM). L'approche des ondelettes continues consiste à caractériser les différentes structures par le couple échelle et position. En utilisant ces informations fournies par les ondelettes, toutes les intensités sous-estimées des petites structures sont rehaussées, ce qui permet d'améliorer la détection des lésions et des tumeurs en imagerie TEP. En conclusion, le travail de cette thèse démontre l'avantage de l'utilisation des ondelettes dans le calcul des paramètres physiologiques à partir des images et des sinogrammes TEP mesurés avec le [indice supérieur 18]FDG chez le rat. Enfin, les résultats obtenus sur les images avec les ondelettes ont montré moins de variation, moins de bruit tout en préservant la résolution spatiale. L'application de la transformée en ondelettes continues dans la correction de l'effet du volume partiel pour les images TEP en utilisant l'ondelette appropriée a montré le potentiel des ondelettes pour localiser les différentes structures permettant une bonne correction et une meilleure qualité d'image

NON-INVASIVE IMAGE ENHANCEMENT OF COLOUR RETINAL FUNDUS IMAGES FOR A COMPUTERISED DIABETIC RETINOPATHY MONITORING AND GRADING SYSTEM

Diabetic Retinopathy (DR) is a sight threatening complication due to diabetes mellitus affecting the retina. The pathologies of DR can be monitored by analysing colour fundus images. However, the low and varied contrast between retinal vessels and the background in colour fundus images remains an impediment to visual analysis in particular in analysing tiny retinal vessels and capillary networks. To circumvent this problem, fundus fluorescein angiography (FF A) that improves the image contrast is used. Unfortunately, it is an invasive procedure (injection of contrast dyes) that leads to other physiological problems and in the worst case may cause death. The objective of this research is to develop a non-invasive digital Image enhancement scheme that can overcome the problem of the varied and low contrast colour fundus images in order that the contrast produced is comparable to the invasive fluorescein method, and without introducing noise or artefacts. The developed image enhancement algorithm (called RETICA) is incorporated into a newly developed computerised DR system (called RETINO) that is capable to monitor and grade DR severity using colour fundus images. RETINO grades DR severity into five stages, namely No DR, Mild Non Proliferative DR (NPDR), Moderate NPDR, Severe NPDR and Proliferative DR (PDR) by enhancing the quality of digital colour fundus image using RETICA in the macular region and analysing the enlargement of the foveal avascular zone (F AZ), a region devoid of retinal vessels in the macular region. The importance of this research is to improve image quality in order to increase the accuracy, sensitivity and specificity of DR diagnosis, and to enable DR grading through either direct observation or computer assisted diagnosis system

NON-INVASIVE IMAGE ENHANCEMENT OF COLOUR RETINAL FUNDUS IMAGES FOR A COMPUTERISED DIABETIC RETINOPATHY MONITORING AND GRADING SYSTEM

Diabetic Retinopathy (DR) is a sight threatening complication due to diabetes mellitus affecting the retina. The pathologies of DR can be monitored by analysing colour fundus images. However, the low and varied contrast between retinal vessels and the background in colour fundus images remains an impediment to visual analysis in particular in analysing tiny retinal vessels and capillary networks. To circumvent this problem, fundus fluorescein angiography (FF A) that improves the image contrast is used. Unfortunately, it is an invasive procedure (injection of contrast dyes) that leads to other physiological problems and in the worst case may cause death. The objective of this research is to develop a non-invasive digital Image enhancement scheme that can overcome the problem of the varied and low contrast colour fundus images in order that the contrast produced is comparable to the invasive fluorescein method, and without introducing noise or artefacts. The developed image enhancement algorithm (called RETICA) is incorporated into a newly developed computerised DR system (called RETINO) that is capable to monitor and grade DR severity using colour fundus images. RETINO grades DR severity into five stages, namely No DR, Mild Non Proliferative DR (NPDR), Moderate NPDR, Severe NPDR and Proliferative DR (PDR) by enhancing the quality of digital colour fundus image using RETICA in the macular region and analysing the enlargement of the foveal avascular zone (F AZ), a region devoid of retinal vessels in the macular region. The importance of this research is to improve image quality in order to increase the accuracy, sensitivity and specificity of DR diagnosis, and to enable DR grading through either direct observation or computer assisted diagnosis system

Digital Image Processing via Combination of Low-Level and High-Level Approaches.

With the growth of computer power, Digital Image Processing plays a more and more important role in the modern world, including the field of industry, medical, communications, spaceflight technology etc. There is no clear definition how to divide the digital image processing, but normally, digital image processing includes three main steps: low-level, mid-level and highlevel processing. Low-level processing involves primitive operations, such as: image preprocessing to reduce the noise, contrast enhancement, and image sharpening. Mid-level processing on images involves tasks such as segmentation (partitioning an image into regions or objects), description of those objects to reduce them to a form suitable for computer processing, and classification (recognition) of individual objects. Finally, higher-level processing involves "making sense" of an ensemble of recognised objects, as in image analysis. Based on the theory just described in the last paragraph, this thesis is organised in three parts: Colour Edge and Face Detection; Hand motion detection; Hand Gesture Detection and Medical Image Processing. II In Colour Edge Detection, two new images G-image and R-image are built through colour space transform, after that, the two edges extracted from G-image and R-image respectively are combined to obtain the final new edge. In Face Detection, a skin model is built first, then the boundary condition of this skin model can be extracted to cover almost all of the skin pixels. After skin detection, the knowledge about size, size ratio, locations of ears and mouth is used to recognise the face in the skin regions. In Hand Motion Detection, frame differe is compared with an automatically chosen threshold in order to identify the moving object. For some special situations, with slow or smooth object motion, the background modelling and frame differencing are combined in order to improve the performance. In Hand Gesture Recognition, 3 features of every testing image are input to Gaussian Mixture Model (GMM), and then the Expectation Maximization algorithm (EM)is used to compare the GMM from testing images and GMM from training images in order to classify the results. In Medical Image Processing (mammograms), the Artificial Neural Network (ANN) and clustering rule are applied to choose the feature. Two classifier, ANN and Support Vector Machine (SVM), have been applied to classify the results, in this processing, the balance learning theory and optimized decision has been developed are applied to improve the performance