3D-MRI Obstruction and Visualization of Pharyngeal Airway Tract using Open Source Seeded Technique

Abstract: Obstructive Sleep Apnea(OSA) is breathing disorder syndrome in which the airway tract pauses during sleep due to collapse of pharyngeal airway. It is occurred at the sleep time, with fourth dimensional high resolution in airway tract Obstruction in children and adults with OSA. Here, we the operator places the seeds that includes the Oesopharyngeal air tract and found out a threshold for the first frame in order to determine the affected tissues which blocks the patients pharyngeal tract. In this automated segmentation method it shows the process of MRI studies of the pharyngeal air pathway and enable diagnose of obstructive tissues with the collapse tissues. Region growing method results well in Dice Coefficients compared with manual segmentation. It automatically detects 90% of collapse tissues. This approach leads to segment the pharyngeal pathway correctly. It uses long MRI scans in order to diagnosis the collapsed tissues with graph, accurate details and coefficients in a short span of duration

Chapter 1 : Hybrid Segmentation Methods

We propose a Hybrid Segmentation Engine that consists of component modules, for automated segmentation of radiological patient and the Visible Human data. We integrate boundary-based and region-based segmentation methods to exploit the strength of each method hopefully to cover the weakness of the other method

Tie-zone : the bridge between watershed transforms and fuzzy connectedness

Orientador: Roberto de Alencar LotufoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoResumo: Esta tese introduz o novo conceito de transformada de zona de empate que unifica as múltiplas soluções de uma transformada de watershed, conservando apenas as partes comuns em todas estas, tal que as partes que diferem constituem a zona de empate. A zona de empate aplicada ao watershed via transformada imagem-floresta (TZ-IFT-WT) se revela um elo inédito entre transformadas de watershed baseadas em paradigmas muito diferentes: gota d'água, inundação, caminhos ótimos e floresta de peso mínimo. Para todos esses paradigmas e os algoritmos derivados, é um desafio se ter uma solução única, fina, e que seja consistente com uma definição. Por isso, propõe-se um afinamento da zona de empate, único e consistente. Além disso, demonstra-se que a TZ-IFT-WT também é o dual de métodos de segmentação baseados em conexidade nebulosa. Assim, a ponte criada entre as abordagens morfológica e nebulosa permite aproveitar avanços de ambas. Em conseqüência disso, o conceito de núcleo de robustez para as sementes é explorado no caso do watershed.Abstract: This thesis introduces the new concept of tie-zone transform that unifies the multiple solutions of a watershed transform, by conserving only the common parts among them such that the differing parts constitute the tie zone. The tie zone applied to the watershed via image-foresting transform (TZ-IFTWT) proves to be a link between watershed transforms based on very different paradigms: drop of water, flooding, optimal paths and forest of minimum weight. For all these paradigms and the derived algorithms, it is a challenge to get a unique and thin solution which is consistent with a definition. That is why we propose a unique and consistent thinning of the tie zone. In addition, we demonstrate that the TZ-IFT-WT is also the dual of segmentation methods based on fuzzy connectedness. Thus, the bridge between the morphological and the fuzzy approaches allows to take benefit from the advance of both. As a consequence, the concept of cores of robustness for the seeds is exploited in the case of watersheds.DoutoradoEngenharia de ComputaçãoDoutor em Engenharia Elétric

Image Segmentation by Image Foresting Transform with Non-smooth Connectivity Functions

Abstract-In the framework of the Image Foresting Transform (IFT), there is a class of connectivity functions that were vaguely explored, which corresponds to the non-smooth connectivity functions (NSCF). These functions are more adaptive to cope with the problems of field inhomogeneity, which are common in MR images of 3 Tesla. In this work, we investigate the NSCF from the standpoint of theoretical and experimental aspects. We formally classify several non-smooth functions according to a proposed diagram representation. Then, we investigate some theoretical properties for some specific regions of the diagram. Our analysis reveals that many NSCFs are, in fact, the result of a sequence of optimizations, each of them involving a maximal set of elements, in a well-structured way. Our experimental results indicate that substantial improvements can be obtained by NSCFs in the 3D segmentation of MR images of 3 Tesla, when compared to smooth connectivity functions

A framework for tumor segmentation and interactive immersive visualization of medical image data for surgical planning

This dissertation presents the framework for analyzing and visualizing digital medical images. Two new segmentation methods have been developed: a probability based segmentation algorithm, and a segmentation algorithm that uses a fuzzy rule based system to generate similarity values for segmentation. A visualization software application has also been developed to effectively view and manipulate digital medical images on a desktop computer as well as in an immersive environment.;For the probabilistic segmentation algorithm, image data are first enhanced by manually setting the appropriate window center and width, and if needed a sharpening or noise removal filter is applied. To initialize the segmentation process, a user places a seed point within the object of interest and defines a search region for segmentation. Based on the pixels\u27 spatial and intensity properties, a probabilistic selection criterion is used to extract pixels with a high probability of belonging to the object. To facilitate the segmentation of multiple slices, an automatic seed selection algorithm was developed to keep the seeds in the object as its shape and/or location changes between consecutive slices.;The second segmentation method, a new segmentation method using a fuzzy rule based system to segment tumors in a three-dimensional CT data was also developed. To initialize the segmentation process, the user selects a region of interest (ROI) within the tumor in the first image of the CT study set. Using the ROI\u27s spatial and intensity properties, fuzzy inputs are generated for use in the fuzzy rules inference system. Using a set of predefined fuzzy rules, the system generates a defuzzified output for every pixel in terms of similarity to the object. Pixels with the highest similarity values are selected as tumor. This process is automatically repeated for every subsequent slice in the CT set without further user input, as the segmented region from the previous slice is used as the ROI for the current slice. This creates a propagation of information from the previous slices, used to segment the current slice. The membership functions used during the fuzzification and defuzzification processes are adaptive to the changes in the size and pixel intensities of the current ROI. The proposed method is highly customizable to suit different needs of a user, requiring information from only a single two-dimensional image.;Segmentation results from both algorithms showed success in segmenting the tumor from seven of the ten CT datasets with less than 10% false positive errors and five test cases with less than 10% false negative errors. The consistency of the segmentation results statistics also showed a high repeatability factor, with low values of inter- and intra-user variability for both methods.;The visualization software developed is designed to load and display any DICOM/PACS compatible three-dimensional image data for visualization and interaction in an immersive virtual environment. The software uses the open-source libraries DCMTK: DICOM Toolkit for parsing of digital medical images, Coin3D and SimVoleon for scenegraph management and volume rendering, and VRJuggler for virtual reality display and interaction. A user can apply pseudo-coloring in real time with multiple interactive clipping planes to slice into the volume for an interior view. A windowing feature controls the tissue density ranges to display. A wireless gamepad controller as well as a simple and intuitive menu interface control user interactions. The software is highly scalable as it can be used on a single desktop computer to a cluster of computers for an immersive multi-projection virtual environment. By wearing a pair of stereo goggles, the surgeon is immersed within the model itself, thus providing a sense of realism as if the surgeon is inside the patient.;The tools developed in this framework are designed to improve patient care by fostering the widespread use of advanced visualization and computational intelligence in preoperative planning, surgical training, and diagnostic assistance. Future work includes further improvements to both segmentation methods with plans to incorporate the use of deformable models and level set techniques to include tumor shape features as part of the segmentation criteria. For the surgical planning components, additional controls and interactions with the simulated endoscopic camera and the ability to segment the colon or a selected region of the airway for a fixed-path navigation as a full virtual endoscopy tool will also be implemented. (Abstract shortened by UMI.