Browsing by Subject "Computer vision in medicine"
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Item Automatic segmentation of vertebrae from digitized x-ray images(Texas Tech University, 2002-12) Zamora-Camarena, GilbertoThe segmentation of vertebrae in x-ray images is of prime importance in the assessment of abnormalities of the spine. Manual segmentation is prone to errors due to inter- and intra-subject variabilities due to the subjective judgement that is employed. The use of computer vision methods is, therefore, an attractive alternative to providing an automatic means for segmenting vertebrae. However, general-purpose algorithms present a number of shortcomings that limit their ability to locate and delineate precise vertebral shapes. Therefore, there is a need for a different approach. This work presents the development of an automatic segmentation methodology that employs a hierarchical approach to segmentation. The unique combination of the Generalized Hough Transform, Active Shape Models, and Deformable Models provides three levels of segmentation firom coarse to fine, respectively. Each algorithm has been customized to address the shortcomings of the other two, thus providing a robust framework. Generalized Hough Transform is used to estimate the pose of the spine within a target image. Then, the technique of Active Shape Models is used to find the boundaries of the vertebrae and to give a global approximation to their shape. Finally, the technique of Deformable Models is used to refine the shape of the vertebrae at key points of interest, such as anterior comers. Experimental results with a data set of 100 lateral views of cervical vertebrae and 100 lateral views of lumbar vertebrae have shown a success rate of 75% in finding boundaries of cervical vertebrae and 50% in lumbar vertebrae. The algorithm developed in this work represents a viable alternative to the currently available segmentation methods in which a unique combination of customized algorithms implements a hierarchical firamework.Item Segmentation of cervical and lumbar vertebrae in x-ray images using active appearance models and extensions(Texas Tech University, 2003-12) Howe, Benjamin MThis thesis presents a hierarchical segmentation algorithm tailored to the segmentation of cervical and lumbar vertebrae in digitized X-ray images. The algorithm employs the Generalized Hough Transform (GHT) to obtain a suitable initialization for two segmentation stages that utilize Active Appearance Models (AAMs) that were proposed by Cootes et al. The advantage of using AAMs in medical image segmentation applications is that rather than creating models that are purely data driven, AAMs gain a priori knowledge through a thorough observation of the shape and texture variation across a training set. This thesis presents a detailed summary of the theory behind AAM along with proposed extensions and customizations of AAM. The proposed extensions (1) address the shortcomings of using the basic texture alignment procedures when using Neighborhood AAMs, (2) automate the selection of training parameters, and (3) modify the AAM search criterion to encourage the location of the edges of the vertebrae. In addition, AAM is utilized to rank the quality of multiple initializations provided by GHT. The proposed segmentation algorithm was tested on 273 cervical X-ray images and 262 lumbar images. If a successful segmentation is defined as a case in which the point-to-corresponding-point error is less than ten pixels for cervical images and twenty-five pixels for lumbar images, results from the proposed segmentation algorithm indicate a 65% success rate for segmentation of cervical vertebrae and a 68% success rate for lumbar vertebrae.