A Markov model of secondary prevention of osteoportic hip fractures.

Tapp, Stephanie

doi:10.18297/etd/1419

Cited by 2 publications

(4 citation statements)

References 38 publications

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“…Without diagnosis and prevention, a woman can lose 20%-30% of her bone mass during the first 10 years of menopause [70].…”

Section: What Is Osteoporosis?mentioning

confidence: 99%

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Probabilistic and geometric shape based segmentation methods.

Aslan¹

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Aslan, Melih Seref, "Probabilistic and geometric shape based segmentation methods." (2012 Image segmentation is one of the most important problems in image processing, object recognition, computer vision, medical imaging, etc. In general, the objective of the segmentation is to partition the image into the meaningful areas using the existing (low level) information in the image and prior (high level) information which can be obtained using a number of features of an object. As stated in [1,2], the human vision system aims to extract and use as much information as possible in the image including but not limited to the intensity, possible motion of the object (in sequential images), spatial relations (interaction) as the existing information, and the shape of the object which is learnt from the experience as the prior information. The main objective of this dissertation is to couple the prior information with the existing information since the machine vision system cannot predict the prior information unless it is given.To label the image into meaningful areas, the chosen information is modelled to fit progressively in each of the regions by an optimization process. The intensity and spatial interaction (as the existing information) and shape (as the prior information) are modelled to obtain the optimum segmentation in this study.The intensity information is modelled using the Gaussian distribution. Spatial inv teraction that describes the relation between neighboring pixels/voxels is modeled by assuming that the pixel intensity depends on the intensities of the neighboring pixels. The shape model is obtained using occurrences of histogram of training shape pixels or voxels. The main objective is to capture the shape variation of the object of interest. Each pixel in the image will have three probabilities to be an object and a background class based on the intensity, spatial interaction, and shape models. These probabilistic values will guide the energy (cost) functionals in the optimization process.This dissertation proposes segmentation frameworks which has the following properties: i) original to solve some of the existing problems, ii) robust under various segmentation challenges, and iii) fast enough to be used in the real applications. In this dissertation, the models are integrated into different methods to obtain the optimum segmentation: 1) variational (can be considered as the spatially continuous), and 2) statistical (can be considered as the spatially discrete) methods.The proposed segmentation frameworks start with obtaining the initial segmentation using the intensity / spatial interaction models. The shape model, which is obtained using the training shapes, is registered to the image domain. Finally, the optimal segmentation is obtained using the optimization of the energy functionals. Experiments show that the use of the shape prior improves considerably the accuracy of the alternative methods which use only existing or both information in the image. The proposed methods are tested on the syntheti...

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“…Without diagnosis and prevention, a woman can lose 20%-30% of her bone mass during the first 10 years of menopause [70].…”

Section: What Is Osteoporosis?mentioning

confidence: 99%

“…70 V(Jp, Jq) is the pairwise interaction model which represents the penalty for the discontinuity between voxels p and q. The simplest model of spatial interaction is the MGRF with the nearest 6-neighborhood.…”

Section: Statistical Segmentation Approachmentioning

confidence: 99%

Probabilistic and geometric shape based segmentation methods.

Aslan¹

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“…The bone begins loosing its weight and calcium soon after menopause. Without diagnosis and prevention, a woman can lose 20%-30% of her bone mass during the first 10 years of menopause [104]. Based on the Surgeon General report [104] General's report, it is strongly stated that the relationship between the BMD score and future fracture is stronger than the relationship between cholesterol and heart attack [60].…”

Section: Ncc)mentioning

confidence: 99%

“…, there were approximately 10 million people over age 50 with osteoporosis and an additional 34 million with low bone mass or osteopenia in the United States in 2002. Unfortunately, the total number is expected to be increased to 61.4 million in 2020 as in[104]. These changes could cause the number of vertebrae, hip, and wrist fractures to increase rapidly by 2040.…”

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confidence: 99%

Shape/image registration for medical imaging : novel algorithms and applications.

Shalaby

Magdy²

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This dissertation looks at two different categories of the registration approaches: Shape registration, and Image registration. It also considers the applications of these approaches into the medical imaging field. Shape registration is an important problem in computer vision, computer graphics and medical imaging. It has been handled in different manners in many applications like shapebased segmentation, shape recognition, and tracking. Image registration is the process of overlaying two or more images of the same scene taken at different times, from different viewpoints, and/or by different sensors. Many image processing applications like remote sensing, fusion of medical images, and computer-aided surgery need image registration. This study deals with two different applications in the field of medical image analysis. The first one is related to shape-based segmentation of the human vertebral bodies (VBs). The vertebra consists of the VB, spinous, and other anatomical regions. Spinous pedicles, and ribs should not be included in the bone mineral density (BMD) measurements. The VB segmentation is not an easy task since the ribs have similar gray level information. vi This dissertation investigates two different segmentation approaches. Both of them are obeying the variational shape-based segmentation frameworks. The first approach deals with two dimensional (2D) case. This segmentation approach starts with obtaining the initial segmentation using the intensity/spatial interaction models. Then, shape model is registered to the image domain. Finally, the optimal segmentation is obtained using the optimization of an energy functional which integrating the shape model with the intensity information. The second one is a 3D simultaneous segmentation and registration approach. The information of the intensity is handled by embedding a Willmore flow into the level set segmentation framework. Then the shape variations are estimated using a new distance probabilistic model. The experimental results show that the segmentation accuracy of the framework are much higher than other alternatives. Applications on BMD measurements of vertebral body are given to illustrate the accuracy of the proposed segmentation approach. The second application is related to the field of computer-aided surgery, specifically on ankle fusion surgery. The long-term goal of this work is to apply this technique to ankle fusion surgery to determine the proper size and orientation of the screws that are used for fusing the bones together. In addition, we try to localize the best bone region to fix these screws. To achieve these goals, the 2D-3D registration is introduced. The role of 2D-3D registration is to enhance the quality of the surgical procedure in terms of time and accuracy, and would greatly reduce the need for repeated surgeries; thus, saving the patients time, expense, and trauma. vii

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A Markov model of secondary prevention of osteoportic hip fractures.

Cited by 2 publications

References 38 publications

Probabilistic and geometric shape based segmentation methods.

Probabilistic and geometric shape based segmentation methods.

Shape/image registration for medical imaging : novel algorithms and applications.

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