Classification and staging of chronic liver disease from multimodal data

Chronic liver disease (CLD) is most of the time an asymptomatic, progressive, and ultimately potentially fatal disease. In this study, an automatic hierarchical procedure to stage CLD using ultrasound images, laboratory tests, and clinical records are described. The first stage of the proposed method, called clinical based classifier (CBC), discriminates healthy from pathologic conditions. When nonhealthy conditions are detected, the method refines the results in three exclusive pathologies in a hierarchical basis: 1) chronic hepatitis; 2) compensated cirrhosis; and 3) decompensated cirrhosis. The features used as well as the classifiers (Bayes, Parzen, support vector machine, and k-nearest neighbor) are optimally selected for each stage. A large multimodal feature database was specifically built for this study containing 30 chronic hepatitis cases, 34 compensated cirrhosis cases, and 36 decompensated cirrhosis cases, all validated after histopathologic analysis by liver biopsy. The CBC classification scheme outperformed the nonhierachical one against all scheme, achieving an overall accuracy of 98.67% for the normal detector, 87.45% for the chronic hepatitis detector, and 95.71% for the cirrhosis detector.

An ultrasound based computer-aided diagnosis tool for steatosis detection

Liver steatosis is a common disease usually associated with social and genetic factors. Early detection and quantification is important since it can evolve to cirrhosis. In this paper, a new computer-aided diagnosis (CAD) system for steatosis classification, in a local and global basis, is presented. Bayes factor is computed from objective ultrasound textural features extracted from the liver parenchyma. The goal is to develop a CAD screening tool, to help in the steatosis detection. Results showed an accuracy of 93.33%, with a sensitivity of 94.59% and specificity of 92.11%, using the Bayes classifier. The proposed CAD system is a suitable graphical display for steatosis classification.

Urban Detection From Hyperspectral Images Using Dimension-Reduction Model and Fusion of Multiple Segmentations Based on Stuctural and Textural Features

Ce mémoire de maîtrise présente une nouvelle approche non supervisée pour détecter et segmenter les régions urbaines dans les images hyperspectrales. La méthode proposée n ́ecessite trois étapes. Tout d’abord, afin de réduire le coût calculatoire de notre algorithme, une image couleur du contenu spectral est estimée. A cette fin, une étape de réduction de dimensionalité non-linéaire, basée sur deux critères complémentaires mais contradictoires de bonne visualisation; à savoir la précision et le contraste, est réalisée pour l’affichage couleur de chaque image hyperspectrale. Ensuite, pour discriminer les régions urbaines des régions non urbaines, la seconde étape consiste à extraire quelques caractéristiques discriminantes (et complémentaires) sur cette image hyperspectrale couleur. A cette fin, nous avons extrait une série de paramètres discriminants pour décrire les caractéristiques d’une zone urbaine, principalement composée d’objets manufacturés de formes simples g ́eométriques et régulières. Nous avons utilisé des caractéristiques texturales basées sur les niveaux de gris, la magnitude du gradient ou des paramètres issus de la matrice de co-occurrence combinés avec des caractéristiques structurelles basées sur l’orientation locale du gradient de l’image et la détection locale de segments de droites. Afin de réduire encore la complexité de calcul de notre approche et éviter le problème de la ”malédiction de la dimensionnalité” quand on décide de regrouper des données de dimensions élevées, nous avons décidé de classifier individuellement, dans la dernière étape, chaque caractéristique texturale ou structurelle avec une simple procédure de K-moyennes et ensuite de combiner ces segmentations grossières, obtenues à faible coût, avec un modèle efficace de fusion de cartes de segmentations. Les expérimentations données dans ce rapport montrent que cette stratégie est efficace visuellement et se compare favorablement aux autres méthodes de détection et segmentation de zones urbaines à partir d’images hyperspectrales.

Multiscale object-based classification of satellite images merging multispectral information with panchromatic textural features

Once admitted the advantages of object-based classification compared to pixel-based classification; the need of simple and affordable methods to define and characterize objects to be classified, appears. This paper presents a new methodology for the identification and characterization of objects at different scales, through the integration of spectral information provided by the multispectral image, and textural information from the corresponding panchromatic image. In this way, it has defined a set of objects that yields a simplified representation of the information contained in the two source images. These objects can be characterized by different attributes that allow discriminating between different spectral&textural patterns. This methodology facilitates information processing, from a conceptual and computational point of view. Thus the vectors of attributes defined can be used directly as training pattern input for certain classifiers, as for example artificial neural networks. Growing Cell Structures have been used to classify the merged information.

On the use of textural features for writer identification in old handwritten music scores

Writer identification consists in determining the writer of a piece of handwriting from a set of writers. In this paper we present a system for writer identification in old handwritten music scores which uses only music notation to determine the author. The steps of the proposed system are the following. First of all, the music sheet is preprocessed for obtaining a music score without the staff lines. Afterwards, four different methods for generating texture images from music symbols are applied. Every approach uses a different spatial variation when combining the music symbols to generate the textures. Finally, Gabor filters and Grey-scale Co-ocurrence matrices are used to obtain the features. The classification is performed using a k-NN classifier based on Euclidean distance. The proposed method has been tested on a database of old music scores from the 17th to 19th centuries, achieving encouraging identification rates.

Association between conventional risk factors and different ultrasound-based markers of atherosclerosis at carotid and femoral levels in a middle-aged population.

Ultrasound detection of sub-clinical atherosclerosis (ATS) may help identify individuals at high cardiovascular risk. Most studies evaluated intima-media thickness (IMT) at carotid level. We compared the relationships between main cardiovascular risk factors (CVRF) and five indicators of ATS (IMT, mean and maximal plaque thickness, mean and maximal plaque area) at both carotid and femoral levels. Ultrasound was performed on 496 participants aged 45-64 years randomly selected from the general population of the Republic of Seychelles. 73.4 % participants had ≥ 1 plaque (IMT thickening ≥ 1.2 mm) at carotid level and 67.5 % at femoral level. Variance (adjusted R2) contributed by age, sex and CVRF (smoking, LDL-cholesterol, HDL-cholesterol, blood pressure, diabetes) in predicting any of the ATS markers was larger at femoral than carotid level. At both carotid and femoral levels, the association between CVRF and ATS was stronger based on plaque-based markers than IMT. Our findings show that the associations between CVRF and ATS markers were stronger at femoral than carotid level, and with plaque-based markers rather than IMT. Pending comparison of these markers using harder cardiovascular endpoints, our findings suggest that markers based on plaque morphology assessed at femoral artery level might be useful cardiovascular risk predictors.

The Atherosclerosis Burden Score (ABS): a Convenient Ultrasound-Based Score of Peripheral Atherosclerosis for Coronary Artery Disease Prediction.

Ultrasonographic detection of subclinical atherosclerosis improves cardiovascular risk stratification, but uncertainty persists about the most discriminative method to apply. In this study, we found that the "atherosclerosis burden score (ABS)", a novel straightforward ultrasonographic score that sums the number of carotid and femoral arterial bifurcations with plaques, significantly outperformed common carotid intima-media thickness, carotid mean/maximal thickness, and carotid/femoral plaque scores for the detection of coronary artery disease (CAD) (receiver operating characteristic (ROC) curve area under the curve (AUC) = 0.79; P = 0.027 to <0.001 with the other five US endpoints) in 203 patients undergoing coronary angiography. ABS was also more correlated with CAD extension (R = 0.55; P < 0.001). Furthermore, in a second group of 1128 patients without cardiovascular disease, ABS was weakly correlated with the European Society of Cardiology chart risk categories (R (2) = 0.21), indicating that ABS provided information beyond usual cardiovascular risk factor-based risk stratification. Pending prospective studies on hard cardiovascular endpoints, ABS appears as a promising tool in primary prevention.

Road Sign Recognition based onInvariant Features using SupportVector Machine

Since last two decades researches have been working on developing systems that can assistsdrivers in the best way possible and make driving safe. Computer vision has played a crucialpart in design of these systems. With the introduction of vision techniques variousautonomous and robust real-time traffic automation systems have been designed such asTraffic monitoring, Traffic related parameter estimation and intelligent vehicles. Among theseautomatic detection and recognition of road signs has became an interesting research topic.The system can assist drivers about signs they don’t recognize before passing them.Aim of this research project is to present an Intelligent Road Sign Recognition System basedon state-of-the-art technique, the Support Vector Machine. The project is an extension to thework done at ITS research Platform at Dalarna University [25]. Focus of this research work ison the recognition of road signs under analysis. When classifying an image its location, sizeand orientation in the image plane are its irrelevant features and one way to get rid of thisambiguity is to extract those features which are invariant under the above mentionedtransformation. These invariant features are then used in Support Vector Machine forclassification. Support Vector Machine is a supervised learning machine that solves problemin higher dimension with the help of Kernel functions and is best know for classificationproblems.

Information Filtering for Ultrasound-based Real-time Registration

This paper presents methods based on Information Filters for solving matching problems with emphasis on real-time, or effectively real-time applications. Both applications discussed in this work deal with ultrasound-based rigid registration in computer-assisted orthopedic surgery. In the first application, the usual workflow of rigid registration is reformulated such that registration algorithms would iterate while the surgeon is acquiring ultrasound images of the anatomy to be operated. Using this effectively real-time approach to registration, the surgeon would then receive feedback in order to better gauge the quality of the final registration outcome. The second application considered in this paper circumvents the need to attach physical markers to bones for anatomical referencing. Experiments using anatomical objects immersed in water are performed in order to evaluate and compare the different methods presented herein, using both 2D as well as real-time 3D ultrasound.

Serial in vivo intravascular ultrasound-based echogenicity changes of everolimus-eluting bioresorbable vascular scaffold during the first 12 months after implantation insights from the ABSORB B trial

This study sought to investigate quantitative and homogeneity differential echogenicity changes of the ABSORB scaffold (1.1) during the first year after implantation.

Association between conventional risk factors and different ultrasound-based markers of atherosclerosis at carotid and femoral levels in a middle-aged population

Ultrasound detection of sub-clinical atherosclerosis (ATS) may help identify individuals at high cardiovascular risk. Most studies evaluated intima-media thickness (IMT) at carotid level. We compared the relationships between main cardiovascular risk factors (CVRF) and five indicators of ATS (IMT, mean and maximal plaque thickness, mean and maximal plaque area) at both carotid and femoral levels. Ultrasound was performed on 496 participants aged 45-64 years randomly selected from the general population of the Republic of Seychelles. 73.4 % participants had ≥ 1 plaque (IMT thickening ≥ 1.2 mm) at carotid level and 67.5 % at femoral level. Variance (adjusted R2) contributed by age, sex and CVRF (smoking, LDL-cholesterol, HDL-cholesterol, blood pressure, diabetes) in predicting any of the ATS markers was larger at femoral than carotid level. At both carotid and femoral levels, the association between CVRF and ATS was stronger based on plaque-based markers than IMT. Our findings show that the associations between CVRF and ATS markers were stronger at femoral than carotid level, and with plaque-based markers rather than IMT. Pending comparison of these markers using harder cardiovascular endpoints, our findings suggest that markers based on plaque morphology assessed at femoral artery level might be useful cardiovascular risk predictors.

Applicability and dosimetric impact of ultrasound-based preplanning in high-dose-rate brachytherapy of prostate cancer

BACKGROUND AND PURPOSE: Analyses of permanent brachytherapy seed implants of the prostate have demonstrated that the use of a preplan may lead to a considerable decrease of dosimetric implant quality. The authors aimed to determine whether the same drawbacks of preplanning also apply to high-dose-rate (HDR) brachytherapy. PATIENTS AND METHODS: 15 patients who underwent two separate HDR brachytherapy implants in addition to external-beam radiation therapy for advanced prostate cancer were analyzed. A pretherapeutic transrectal ultrasound was performed in all patients to generate a preplan for the first brachytherapy implant. For the second brachytherapy, a subset of patients were treated by preplans based on the ultrasound from the first brachytherapy implant. Preplans were compared with the respective postplans assessing the following parameters: coverage index, minimum target dose, homogeneity index, and dose exposure of organs at risk. The prostate geometries (volume, width, height, length) were compared as well. RESULTS: At the first brachytherapy, the matching between the preplan and actual implant geometry was sufficient in 47% of the patients, and the preplan could be applied. The dosimetric implant quality decreased considerably: the mean coverage differed by -0.11, the mean minimum target dose by -0.15, the mean homogeneity index by -0.09. The exposure of organs at risk was not substantially altered. At the second brachytherapy, all patients could be treated by the preplan; the differences between the implant quality parameters were less pronounced. The changes of prostate geometry between preplans and postplans were considerable, the differences in volume ranging from -8.0 to 13.8 cm(3) and in dimensions (width, height, length) from -1.1 to 1.0 cm. CONCLUSION: Preplanning in HDR brachytherapy of the prostate is associated with a substantial decrease of dosimetric implant quality, when the preplan is based on a pretherapeutic ultrasound. The implant quality is less impaired in subsequent implants of fractionated brachytherapy.