Visualisation of curved tubular structures in medical databases: An application to virtual colonoscopy

Medical conditions affecting the colon are problematic to diagnose due to the difficulty in examining this particular internal organ. To date, the most widely used approach is to perform a colonoscopy; a procedure in which a small camera is inserted into the colon to examine its surface. This procedure is unpleasant and potentially dangerous for the patient, and is expensive and time consuming for the hospital. As a result, patients at risk of developing the conditions are not always screened as often as would be desirable. Over the last few years a new approach known as virtual colonoscopy has been gaining popularity. The method uses information from a CT scan to reconstruct a 3D model of the colon which can then be examined without the patient needing to undergo a colonoscopy. This approach is now commonly used when screening for polyps (an indication of colon cancer) but can not be so easily used on conditions such as Inflammatory Bowel Disease (IBD) where information beyond the shape of the surface is required. This thesis forms part of a larger project which aims to diagnose conditions such as IBD by using image processing algorithms on CT data and presenting the results to the user in an easy to interpret way. Specifically we are concerned with this visualisation stage of the system and so have developed a new visualisation approach which we call Volumetric CPR. This can be used to supplement the more traditional virtual flythrough visualisation and is applicable to IBD detection as well as screening for polyps. Our technique builds on the concept of Curved Planar Reformation (CPR), which has proved to be a practical and widely used tool for the visualisation of curved tubular structures within the human body. It has been useful in medical procedures involving the examination of blood vessels and the spine. However, it is more difficult to use it for structures such as the colon because abnormalities are smaller relative to the size of the structure and may not have such distinct density and shape characteristics. Our new approach improves on this situation by using volume rendering for hollow regions of the structure and standard CPR, for the surrounding tissue. This effectively combines grey scale contextual information with detailed colour information from the area of interest. The approach is successfully used with each of the standard CPR types and the resulting images are promising as an alternative for virtual colonoscopy. We also demonstrate how systems can effectively utilize this new visualisation in order to convey maximum information to the user. We show how overlays can be used to present surface coverage data and how sophisticated lighting models can improve the users understanding of the 3D structure. We also present details of how to integrate our visualisation into existing systems and work flows

Exploring 3D Shapes through Real Functions

This thesis lays in the context of research on representation, modelling and coding knowledge related to digital shapes, where by shape it is meant any individual object having a visual appareance which exists in some two-, three- or higher dimensional space. Digital shapes are digital representations of either physically existing or virtual objects that can be processed by computer applications. While the technological advances in terms of hardware and software have made available plenty of tools for using and interacting with the geometry of shapes, to manipulate and retrieve huge amount of data it is necessary to define methods able to effectively code them. In this thesis a conceptual model is proposed which represents a given 3D object through the coding of its salient features and defines an abstraction of the object, discarding irrelevant details. The approach is based on the shape descriptors defined with respect to real functions, which provide a very useful shape abstraction method for the analysis and structuring of the information contained in the discrete shape model. A distinctive feature of these shape descriptors is their capability of combining topological and geometrical information properties of the shape, giving an abstraction of the main shape features. To fully develop this conceptual model, both theoretical and computational aspects have been considered, related to the definition and the extension of the different shape descriptors to the computational domain. Main emphasis is devoted to the application of these shape descriptors in computational settings; to this aim we display a number of application domains that span from shape retrieval, to shape classification and to best view selection.Questa tesi si colloca nell\u27ambito di ricerca riguardante la rappresentazione, la modellazione e la codifica della conoscenza connessa a forme digitali, dove per forma si intende l\u27aspetto visuale di ogni oggetto che esiste in due, tre o pi? dimensioni. Le forme digitali sono rappresentazioni di oggetti sia reali che virtuali, che possono essere manipolate da un calcolatore. Lo sviluppo tecnologico degli ultimi anni in materia di hardware e software ha messo a disposizione una grande quantit? di strumenti per acquisire, rappresentare e processare la geometria degli oggetti; tuttavia per gestire questa grande mole di dati ? necessario sviluppare metodi in grado di fornirne una codifica efficiente. In questa tesi si propone un modello concettuale che descrive un oggetto 3D attraverso la codifica delle caratteristiche salienti e ne definisce una bozza ad alto livello, tralasciando dettagli irrilevanti. Alla base di questo approccio ? l\u27utilizzo di descrittori basati su funzioni reali in quanto forniscono un\u27astrazione della forma molto utile per analizzare e strutturare l\u27informazione contenuta nel modello discreto della forma. Una peculiarit? di tali descrittori di forma ? la capacit? di combinare propriet? topologiche e geometriche consentendo di astrarne le principali caratteristiche. Per sviluppare questo modello concettuale, ? stato necessario considerare gli aspetti sia teorici che computazionali relativi alla definizione e all\u27estensione in ambito discreto di vari descrittori di forma. Particolare attenzione ? stata rivolta all\u27applicazione dei descrittori studiati in ambito computazionale; a questo scopo sono stati considerati numerosi contesti applicativi, che variano dal riconoscimento alla classificazione di forme, all\u27individuazione della posizione pi? significativa di un oggetto

Computation of Watersheds Based on Parallel Graph Algorithms

In this paper the implementation of a parallel watershed algorithm is described. The algorithm has been implemented on a Cray J932, which is a shared memory architecture with 32 processors. The watershed transform has generally been considered to be inherently sequential, but recently a few research groups, have designed parallel algorithms for computing watersheds. Most of these parallel algorithms are based on splitting the source image in blocks, computing the watersheds of these blocks and merging the resulting images into the desired result. A disadvantage of this approach is that a lot of communication is necessary at the boundaries of the blocks. It is possible to formulate the computation of the watershed transform as a shortest path searching problem that is commonly found in algorithmic graph theory. In this paper we use a parallel adapted version of Dijkstra's algorithm for computing shortest paths in undirected graphs.

Computation of watersheds based on parallel graph algorithms

In this paper the implementation of a parallel watershed algorithm is described. The algorithm has been implemented on a Cray J932, which is a shared memory architecture with 32 processors. The watershed transform has generally been considered to be inherently sequential, but recently a few research groups, see [5, 9, 10], have designed parallel algorithms for computing watersheds. Most of these parallel algorithms are based on splitting the source image in blocks, computing the watersheds of these blocks and merging the resulting images into the desired result. A disadvantage of this approach is that a lot of communication is necessary at the boundaries of the blocks. It is possible to formulate the computation of the watershed transform as a shortest path searching problem that is commonly found in algorithmic graph theory. In this paper we use a parallel adapted version of Dijkstra's algorithm for computing shortest paths in undirected graphs

Segmentation Of Self-organizing Maps With 3-d Output Grids [segmentação De Mapas Auto-organizáveis Com Espaço De Saída 3-d]

The self-organizing map (SOM) has been widely used as a software tool for visualization of high-dimensional data. Important SOM features include information compression while trying to preserve topological and metric relationship of the primary data items. Similar data in the input space would be mapped to the same neuron or in a nearby unit. The clustering properties of a trained SOM 2-D can be visualized by the U-matrix, which is a neuron's neighborhood distance based image. This assumption of topological preservation is not true for many SOM mappings involving dimension reduction. With the automation of cluster detection in SOM network higher output dimensions can be used in problems involving discovery of classes in multidimensional data. Results of topological errors are shown in a simple 2-D clustering in a 1-D output grid SOM. This paper presents the U-array as an extension of the U-matrix for 3-D output grids. The advantage of the method relies in working with higher dimensions in the output space, which can lead to a better topological preservation in data analysis. Examples of automatic class discovery using U-arrays are also presented.182150162Bauer, H.-U., Herrmann, M., Villmann, T., Neural maps and topographic vector quantization (1999) Neural Networks, 12 (4-5), pp. 659-676Bishop, C.M., (1995) Neural Networks for Pattern Recognition, , Oxford: Oxford University PressCios, K.J., Kurgan, L., Trends in Data Mining and Knowledge Discovery (2002) Knowledge Discovery in Advanced Information Systems, , Pal, N.R. et al, Eds, SpringerCosta, J.A.F., (1999) Classificação Automática e Análise de Dados por Redes Neurais Auto-Organizáveis, , Tese de Doutorado, Unicamp, SPCosta, J.A.F., Netto, M.L.A., Estimating the Number of Clusters in Multivariate Data by Self-Organizing Maps (1999) Intl. Journal of Neural Systems, 9, pp. 195-202Costa, J.A.F., Netto, M.L.A., Cluster Analysis Using Self-Organizing Maps and Image Processing Techniques (1999) Proc. of the IEEE Conf. on Systems, Man, and Cybernetics, 5, pp. 367-372. , Tokyo, JapanCosta, J.A.F., Netto, M.L.A., A new tree-structured self-organizing map for data analysis (2001) Proc. of the Intl. Joint Conf. on Neural Networks, pp. 1931-1936. , Washington, DC, ppCosta, J.A.F., Netto, M.L.A., Clustering of complex shaped data sets via Kohonen maps and mathematical morphology (2001) Proceedings of the SPIE, 4384, pp. 16-27Costa, J.A.F., Netto, M.L.A., Segmentação automática de mapas de Kohonen (2002) Congresso Brasileiro de Automática, pp. 1607-1613. , Natal, RN, ppCosta, J.A.F., Netto, M.L.A., Segmentação do SOM Baseada em Particionamento de Grafos (2003) Anais do VI Congresso Brasileiro de Redes Neurais, pp. 451-456. , São Paulo, ppCosta, J.A.F. (2005). Segmentação do SOM por Métodos de Agrupamentos Hierárquicos com Conectividade Restrita. VII Congresso Brasileiro de Redes Neurais, Natal, RN, outubro de 2005Falcão, A.X., Stolfi, J., Lotufo, R.A., The Image Foresting Transform: Theory, Algorithms and Applications (2004) IEEE Trans, on Pattern Analysis and Machine Intelligence, 26 (1), pp. 19-29Flexer, A., Limitations of Self-organizing Maps for Vector Quantization and Multidimensional Scaling (1997) Advances in Neural Information Processing Systems 9, pp. 445-451. , Mozer M.C, et al.eds, MIT Press, ppGonçalves, M., Netto, M.L.A., Costa, J.A.F., Zullo, J., Automatic Remotely Sensed Data Clustering by Tree-Structured Self-Organizing Maps (2005) IGARSS'05 (2005 IEEE International Geoscience and Remote Sensing Symposium), pp. 25-29. , July, Seoul, KoreaGonçalves, M., Netto, M., Costa, J.A.F., Zullo, J., Data Clustering using Self-Organizing Maps segmented by Mathematic Morphology and Simplified Cluster Validity Indexes: An application in remotely sensed image (2006) 2006 IEEE International Joint Conference on Neural Networks, , Vancouver, BC, Canada. 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Opitz et al, Eds, Springer, Berlin, ppUltsch, A., Self-Organizing Neural Networks perform different from statistical k-means clustering (1995) Gesellschaft für Klassifikation, , BaselVesanto, J., Alhoniemi, E., Clustering of the Self-Organizing Map (2000) IEEE Trans. on Neural Netwoks, 11 (3), pp. 586-60