Weighted Shape-based Averaging with Neighborhood Prior Model for Multiple Atlas Fusion-based Medical Image Segmentation

In medical imaging, merging automated segmentations obtained from multiple atlases has become a standard practice for improving the accuracy. In this letter, we propose two new fusion methods: "Global Weighted Shape-Based Averaging" (GWSBA) and "Local Weighted Shape-Based Averaging" (LWSBA). These methods extend the well known Shape-Based Averaging (SBA) by additionally incorporating the similarity information between the reference (i.e., atlas) images and the target image to be segmented. We also propose a new spatially-varying similarity-weighted neighborhood prior model, and an edge-preserving smoothness term that can be used with many of the existing fusion methods. We first present our new Markov Random Field (MRF) based fusion framework that models the above mentioned information. The proposed methods are evaluated in the context of segmentation of lymph nodes in the head and neck 3D CT images, and they resulted in more accurate segmentations compared to the existing SBA.

A fully-automatic caudate nucleus segmentation of brain MRI: application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder

Background Accurate automatic segmentation of the caudate nucleus in magnetic resonance images (MRI) of the brain is of great interest in the analysis of developmental disorders. Segmentation methods based on a single atlas or on multiple atlases have been shown to suitably localize caudate structure. However, the atlas prior information may not represent the structure of interest correctly. It may therefore be useful to introduce a more flexible technique for accurate segmentations. Method We present Cau-dateCut: a new fully-automatic method of segmenting the caudate nucleus in MRI. CaudateCut combines an atlas-based segmentation strategy with the Graph Cut energy-minimization framework. We adapt the Graph Cut model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus, by defining new energy function data and boundary potentials. In particular, we exploit information concerning the intensity and geometry, and we add supervised energies based on contextual brain structures. Furthermore, we reinforce boundary detection using a new multi-scale edgeness measure. Results We apply the novel CaudateCut method to the segmentation of the caudate nucleus to a new set of 39 pediatric attention-deficit/hyperactivity disorder (ADHD) patients and 40 control children, as well as to a public database of 18 subjects. We evaluate the quality of the segmentation using several volumetric and voxel by voxel measures. Our results show improved performance in terms of segmentation compared to state-of-the-art approaches, obtaining a mean overlap of 80.75%. Moreover, we present a quantitative volumetric analysis of caudate abnormalities in pediatric ADHD, the results of which show strong correlation with expert manual analysis. Conclusion CaudateCut generates segmentation results that are comparable to gold-standard segmentations and which are reliable in the analysis of differentiating neuroanatomical abnormalities between healthy controls and pediatric ADHD.

Segmentation of Head and Neck Lymph Node Regions for Radiotherapy Planning Using Active Contour-Based Atlas Registration

In this paper, we present the segmentation of the headand neck lymph node regions using a new active contourbased atlas registration model. We propose to segment thelymph node regions without directly including them in theatlas registration process; instead, they are segmentedusing the dense deformation field computed from theregistration of the atlas structures with distinctboundaries. This approach results in robust and accuratesegmentation of the lymph node regions even in thepresence of significant anatomical variations between theatlas-image and the patient's image to be segmented. Wealso present a quantitative evaluation of lymph noderegions segmentation using various statistical as well asgeometrical metrics: sensitivity, specificity, dicesimilarity coefficient and Hausdorff distance. Acomparison of the proposed method with two other state ofthe art methods is presented. The robustness of theproposed method to the atlas selection, in segmenting thelymph node regions, is also evaluated.

IFTrace: Video segmentation of deformable objects using the Image Foresting Transform

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

ECG arrhythmia classification based on optimum-path forest

An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) signals, since the non-invasive nature and simplicity of the ECG exam. According to the application, ECG data analysis consists of steps such as preprocessing, segmentation, feature extraction and classification aiming to detect cardiac arrhythmias (i.e.; cardiac rhythm abnormalities). Aiming to made a fast and accurate cardiac arrhythmia signal classification process, we apply and analyze a recent and robust supervised graph-based pattern recognition technique, the optimum-path forest (OPF) classifier. To the best of our knowledge, it is the first time that OPF classifier is used to the ECG heartbeat signal classification task. We then compare the performance (in terms of training and testing time, accuracy, specificity, and sensitivity) of the OPF classifier to the ones of other three well-known expert system classifiers, i.e.; support vector machine (SVM), Bayesian and multilayer artificial neural network (MLP), using features extracted from six main approaches considered in literature for ECG arrhythmia analysis. In our experiments, we use the MIT-BIH Arrhythmia Database and the evaluation protocol recommended by The Association for the Advancement of Medical Instrumentation. A discussion on the obtained results shows that OPF classifier presents a robust performance, i.e.; there is no need for parameter setup, as well as a high accuracy at an extremely low computational cost. Moreover, in average, the OPF classifier yielded greater performance than the MLP and SVM classifiers in terms of classification time and accuracy, and to produce quite similar performance to the Bayesian classifier, showing to be a promising technique for ECG signal analysis. © 2012 Elsevier Ltd. All rights reserved.

Fully Automatic Segmentation of Hip CT Images

Automatic segmentation of the hip joint with pelvis and proximal femur surfaces from CT images is essential for orthopedic diagnosis and surgery. It remains challenging due to the narrowness of hip joint space, where the adjacent surfaces of acetabulum and femoral head are hardly distinguished from each other. This chapter presents a fully automatic method to segment pelvic and proximal femoral surfaces from hip CT images. A coarse-to-fine strategy was proposed to combine multi-atlas segmentation with graph-based surface detection. The multi-atlas segmentation step seeks to coarsely extract the entire hip joint region. It uses automatically detected anatomical landmarks to initialize and select the atlas and accelerate the segmentation. The graph based surface detection is to refine the coarsely segmented hip joint region. It aims at completely and efficiently separate the adjacent surfaces of the acetabulum and the femoral head while preserving the hip joint structure. The proposed strategy was evaluated on 30 hip CT images and provided an average accuracy of 0.55, 0.54, and 0.50 mm for segmenting the pelvis, the left and right proximal femurs, respectively.

Column-based databases: estudo exploratório no âmbito das bases de dados NoSQL

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Document-based databases: estudo comparativo no âmbito das bases de dados NoSql

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Magnetization transfer-based 3D visualization of foot peripheral nerves.

PURPOSE: To investigate magnetization transfer (MT) effects as a new source of contrast for imaging and tracking of peripheral foot nerves. MATERIALS AND METHODS: Two sets of 3D spoiled gradient-echo images acquired with and without a saturation pulse were used to generate MT ratio (MTR) maps of 260 μm in-plane resolution for eight volunteers at 3T. Scan parameters were adjusted to minimize signal loss due to T2 dephasing, and a dedicated coil was used to improve the inherently low signal-to-noise ratio of small voxels. Resulting MTR values in foot nerves were compared with those in surrounding muscle tissue. RESULTS: Average MTR values for muscle (45.5 ± 1.4%) and nerve (21.4 ± 3.1%) were significantly different (P < 0.0001). In general, the difference in MTR values was sufficiently large to allow for intensity-based segmentation and tracking of foot nerves in individual subjects. This procedure was termed MT-based 3D visualization. CONCLUSION: The MTR serves as a new source of contrast for imaging of peripheral foot nerves and provides a means for high spatial resolution tracking of these structures. The proposed methodology is directly applicable on standard clinical MR scanners and could be applied to systemic pathologies, such as diabetes.

A typology of internet users based on comparative affective states: evidence from eight countries

Purpose - The role of affective states in consumer behaviour is well established. However, no study to date has empirically examined online affective states as a basis for constructing typologies of internet users and for assessing the invariance of clusters across national cultures. Design/methodology/approach - Four focus groups with internet users were carried out to adapt a set of affective states identified from the literature to the online environment. An online survey was then designed to collect data from internet users in four Western and four East Asian countries. Findings - Based on a cluster analysis, six cross-national market segments are identified and labelled "Positive Online Affectivists", "Offline Affectivists", "On/Off-line Negative Affectivists", "Online Affectivists", "Indistinguishable Affectivists", and "Negative Offline Affectivists". The resulting clusters discriminate on the basis of national culture, gender, working status and perceptions towards online brands. Practical implications - Marketers may use this typology to segment internet users in order to predict their perceptions towards online brands. Also, a standardised approach to e-marketing is not recommended on the basis of affective state-based segmentation. Originality/value - This is the first study proposing affective state-based typologies of internet users using comparable samples from four Western and four East Asian countries.

A Graph Model for DynamicWaveband Switching in WDM Mesh Networks

We investigate the problem of waveband switching (WBS) in a wavelength-division multiplexing (WDM) mesh network with dynamic traffic requests. To solve the WBS problem in a homogeneous dynamic WBS network, where every node is a multi-granular optical cross-connect (MG-OXC), we construct an auxiliary graph. Based on the auxiliary graph, we develop two heuristic on-line WBS algorithms with different grouping policies, namely the wavelength-first WBS algorithm based on the auxiliary graph (WFAUG) and the waveband-first WBS algorithm based on the auxiliary graph (BFAUG). Our results show that the WFAUG algorithm outperforms the BFAUG algorithm.

Über das Vermessen tubulärer Strukturen in der Computertomographie

Die chronisch obstruktive Lungenerkrankung (engl. chronic obstructive pulmonary disease, COPD) ist ein Überbegriff für Erkrankungen, die zu Husten, Auswurf und Dyspnoe (Atemnot) in Ruhe oder Belastung führen - zu diesen werden die chronische Bronchitis und das Lungenemphysem gezählt. Das Fortschreiten der COPD ist eng verknüpft mit der Zunahme des Volumens der Wände kleiner Luftwege (Bronchien). Die hochauflösende Computertomographie (CT) gilt bei der Untersuchung der Morphologie der Lunge als Goldstandard (beste und zuverlässigste Methode in der Diagnostik). Möchte man Bronchien, eine in Annäherung tubuläre Struktur, in CT-Bildern vermessen, so stellt die geringe Größe der Bronchien im Vergleich zum Auflösungsvermögen eines klinischen Computertomographen ein großes Problem dar. In dieser Arbeit wird gezeigt wie aus konventionellen Röntgenaufnahmen CT-Bilder berechnet werden, wo die mathematischen und physikalischen Fehlerquellen im Bildentstehungsprozess liegen und wie man ein CT-System mittels Interpretation als lineares verschiebungsinvariantes System (engl. linear shift invariant systems, LSI System) mathematisch greifbar macht. Basierend auf der linearen Systemtheorie werden Möglichkeiten zur Beschreibung des Auflösungsvermögens bildgebender Verfahren hergeleitet. Es wird gezeigt wie man den Tracheobronchialbaum aus einem CT-Datensatz stabil segmentiert und mittels eines topologieerhaltenden 3-dimensionalen Skelettierungsalgorithmus in eine Skelettdarstellung und anschließend in einen kreisfreien Graphen überführt. Basierend auf der linearen System Theorie wird eine neue, vielversprechende, integral-basierte Methodik (IBM) zum Vermessen kleiner Strukturen in CT-Bildern vorgestellt. Zum Validieren der IBM-Resultate wurden verschiedene Messungen an einem Phantom, bestehend aus 10 unterschiedlichen Silikon Schläuchen, durchgeführt. Mit Hilfe der Skelett- und Graphendarstellung ist ein Vermessen des kompletten segmentierten Tracheobronchialbaums im 3-dimensionalen Raum möglich. Für 8 zweifach gescannte Schweine konnte eine gute Reproduzierbarkeit der IBM-Resultate nachgewiesen werden. In einer weiteren, mit IBM durchgeführten Studie konnte gezeigt werden, dass die durchschnittliche prozentuale Bronchialwandstärke in CT-Datensätzen von 16 Rauchern signifikant höher ist, als in Datensätzen von 15 Nichtrauchern. IBM läßt sich möglicherweise auch für Wanddickenbestimmungen bei Problemstellungen aus anderen Arbeitsgebieten benutzen - kann zumindest als Ideengeber dienen. Ein Artikel mit der Beschreibung der entwickelten Methodik und der damit erzielten Studienergebnisse wurde zur Publikation im Journal IEEE Transactions on Medical Imaging angenommen.

Applied Graph Theory in Computer Vision and Pattern Recognition

This book will serve as a foundation for a variety of useful applications of graph theory to computer vision, pattern recognition, and related areas. It covers a representative set of novel graph-theoretic methods for complex computer vision and pattern recognition tasks. The first part of the book presents the application of graph theory to low-level processing of digital images such as a new method for partitioning a given image into a hierarchy of homogeneous areas using graph pyramids, or a study of the relationship between graph theory and digital topology. Part II presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, including a survey of graph based methodologies for pattern recognition and computer vision, a presentation of a series of computationally efficient algorithms for testing graph isomorphism and related graph matching tasks in pattern recognition and a new graph distance measure to be used for solving graph matching problems. Finally, Part III provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks. It includes a critical review of the main graph-based and structural methods for fingerprint classification, a new method to visualize time series of graphs, and potential applications in computer network monitoring and abnormal event detection.