Separation of prosthesis projections and tissue implants in spiral CT scan sinogram images using active contour methods

Number of pages: 146 File Format: word File Code: 32240
Year: 2012 University Degree: Master's degree Category: Electronic Engineering
  • Part of the Content
  • Contents & Resources
  • Summary of Separation of prosthesis projections and tissue implants in spiral CT scan sinogram images using active contour methods

    Medical Engineering Master's Thesis, Bioelectrical Orientation

    Abstract

    Separation of prosthetic projections and tissue implants in spiral CT scan sinogram images using contour methods Active

     

    Metal implants such as prostheses and tooth filling materials during reconstruction of CT images with different methods, cause artifacts that appear as bright and dark radial lines around the metal object and seriously limit the medical value of CT images, because diagnostic and treatment decisions are under It affects Projection data that are affected by this artifact are called missing projections and appear in the sinogram image as distinct areas with very high intensity and values. Therefore, if these areas are identified as much as possible, their values ??are replaced with appropriate values, and another image is reconstructed from the improved sinogram, then a high quality image is created. In this dissertation, a method for separating the mentioned areas is introduced. In this way, first, several active contour methods are introduced and the best of them is selected for the approximate separation of the border of the respective areas. Then, for more accurate separation, a generalized Hough transform is defined to find sinusoidal curves. By applying the Hough transformation to the separated border and cross-border points, sinusoidal curves corresponding to these points, that is; The relevant areas are identified. Finally, with an algorithm after processing, this separation ends well. The results of image reconstruction from the improved sinogram show that the proposed algorithm has been able to identify the areas related to metal objects in the sinogram well.

    In this chapter, first, a brief explanation about the need for body imaging systems and the emergence of the CT scan machine, then the general process of image creation and the causes of artifacts and distortions caused by metal texture in the image are described, and finally, the general objectives and parts of the thesis are presented.

     

     

    1-1- Introduction

     

    Maintaining and promoting human health as the axis of sustainable development has long been the focus of scientists in various fields of science. Acquiring knowledge and gaining skills in the field of using different tools to serve mankind, especially for early diagnosis of diseases and their timely treatment, is and is the daily concern of medical scientists. The production and progress of science and technology has not only provided huge developments in this direction, but has significantly increased the reasons for human desire to use technology.

    Getting information about how the body's systems are established and function accurately in normal conditions and their changes due to illness is necessary to understand and justify the occurrence of symptoms and signs. Although anatomy or the knowledge of internal organs and systems through direct contact and cutting of the human body provides comprehensive information to doctors and medical scholars, but relying on the exclusive use of this method to know the physical and functional condition of internal tissues is not always possible, but can cause considerable harm to humans. Using previous experiences on corpses and generalizing individual findings is also not possible due to the considerable differences in the anatomy of the human body. Therefore, the use of tools and technology to obtain accurate information from the inside of humans has been one of the never-ending ideals of medical scholars. Throughout history, the idealism of medical scholars in obtaining more complete information from the inside of the human body has led them to create, develop and use technology.

    Konrad Rongten[1], a German scientist, discovered the existence of X-rays for the first time in 1895 and accidentally discovered the unique capabilities of this ray in imaging the internal organs of the body, especially bones. In this way, a new beginning was made for a great development in the field of medical imaging.

    X-ray is a series of electromagnetic rays that covers a wavelength from a few picometers to a few nanometers and passes through the materials with different attenuation coefficients depending on the material. This is the same feature that is used in normal radiology. In such a way that X-rays pass through bones and hard tissues and are absorbed more than other tissues, and when it is converted into an image by X-ray sensitive films, this weakening of the rays is seen as bright areas and can be recognized. This process can be seen in Figure 1-1.

    Figure 1-1: Conventional radiology. a) How to place components. b) An example of chest image [1].

    Although conventional radiology has helped medicine a lot and even today it is used in many cases, but the following weaknesses made its transformation and progress inevitable:

    High and uncontrolled dose of X-rays which is harmful to the body.

    Compatibility of the three-dimensional volume of the body on a two-dimensional image causes the organs to overlap in the image and decrease the accuracy of the doctor's diagnosis.

    The low contrast of the image is due to the diffusion of the source of radiation and the lack of production of completely parallel rays.

    And for this reason, the need for computerized radiography devices with CT scans [2] emerged [1]. The details of CT scan are described in chapter "2". Image with FBP method was introduced for physical applications. To date, FBP remains the most widely used image reconstruction method in CT. This method is called a so-called transformation method because it is based on the assumption that the measurements, the Radon transformation of the linear attenuation coefficient distribution and the analytical inverse of the Radon transformation are a direct solution for image reconstruction. If this algorithm to ideal projections; That is, to apply an unlimited number of measurements with unlimited narrow beams and without noise, then this solution is ideal. But in practice it is not like this. Because first, the measurements are read with a limited number of detectors in a limited rotation range and using limited wide beams. Secondly, the x-ray tube emits a continuous spectrum that is created as a result of the hardening of the rays [3]. Thirdly, the measurements are noisy and include scattered radiation. There are many other differences between the actual measurements and the Radon transform, all of which cause artifacts in the reconstructed images. But usually the errors caused by these approximations are relatively small and the FBP results are satisfactory. In addition, artifacts can be reduced by applying corrections before or after applying FBP. For example, noise and artifacts caused by aliasing are reduced by applying a low-pass filter to the raw data. However, under many circumstances, including the presence of high-density objects, the artifacts become incredibly intense. Figures "1-2" and "1-3" show examples of striatal artifacts caused by the presence of metallic objects.

    Figure 1-2: CT scan of a patient with a hip prosthesis. (a) Patient with prosthesis. (b) CT image of a cross-sectional surface including a prosthesis that has a streak artifact caused by the prosthesis [3].

    Many researchers have tried with different methods to eliminate metal artifacts and usually they are based on the assumption that the measured information that is affected by the metal objects is either ignored or through interpolation [4] between Neighboring sizes are replaced. These existing algorithms lead to a drastic reduction of metal artifacts, but some of them create new artifacts. Therefore, better algorithms are needed to reduce metallic artifacts more effectively.

  • Contents & References of Separation of prosthesis projections and tissue implants in spiral CT scan sinogram images using active contour methods

    List:

    1- Chapter 1: Introduction 1

    1-1- Introduction 2

    1-2- Filtered Back Projection (FBP) and artifacts in CT 4

    1-2-1- Beam hardening 6

    1-2-2- Photon deficiency 7

    1-2-3- Scattering 7

    1-2-4- Relative volume effect 7

    1-3- General goals of this thesis 8

    2- Chapter two: Theoretical foundations Research 9

    2-1- History of CT scan 10

    2-2- The main components of CT scan device 11

    2-3- Different generations of CT scan device until today 14

    2-3-1- The first generation 15

    2-3-2- second generation 16

    2-3-3- third generation 17

    2-3-4- fourth generation 17

    2-3-5- fifth generation, EBCT CT scans 18

    2-3-6 Sixth generation, spiral CT scans (spiral or helical) 19 2-3-7 Seventh generation, multi-slice CT scans 20 2-4 Image reconstruction algorithms 21 2-4-1 Sineogram 23

    2-4-2- ART Algorithm 27

    2-4-3- Back Projection Fourier Slice Algorithm 28

    2-4-4- Filtered Back Projection Fourier Slice Algorithm 32

    2-5- Image reconstruction in fan beam mode 34

    2-5-1- Image reconstruction In the fan beam mode with equal angles 35 2-5-2- Image reconstruction in the fan beam mode with equal coverage spaces 38 2-6 Image reconstruction in spiral generation cities 38 2-7- Active contours 40 2-7-1- Active contour models Parametric 41 2-7-2-Geometric active contour models 53 8-2-Hough transform 62 3-Chapter 3: Review of research done 68 4-Chapter 4: Research method 75

    4-1- Introduction 76

    4-2-Spiral imaging method and its comparison with normal scanner 77

    4-3- Sineogram formation 78

    4-4- Proposed algorithm 79

    4-4-1- Contour model Active 80 4-4-2 Algorithm based on transformation                                              84

    4-4-3- Post-processing algorithm 86

    4-5- Conclusion 86

    5- Chapter Five: Results 88

    5-1- Applying the proposed algorithm to real data and comparing intuitively 89

    5-2- Applying the proposed algorithm to the mollage and comparison quantitatively 90 5-2-1- Mean and variance of the gray level 92 5-2-2 Mean square errors 93 5-2-3 Maximum signal to noise ratio 93 5-2-4- Parameter Q 94 5-3-Comparison of proposed algorithm with separation method with threshold limit and Hough transform 119 5-4 Conclusion 122 References Source: [1] Hsieh, J. (2009), Computed Tomography Principles, Design, Artifacts, and Recent Advances, 2nd ed. Washington: Bellingham.

    [2] Yazdi, M., and Beaulieul, L. (2007). "Artifacts in Spiral X-ray CT Scanners: Problems and Solutions," Proceeding of World Academy of Science, Engineering and Technology 26: 376-380.

    [3] DeMan, B. "Iterative Reconstruction for Reduction of Metal Artifacts in Computed Tomography," PhD. Dissertation, Katholieke Universiteit Leuven, Belgium, 2001.

    [4] Xu, C., Pham, D.L., and Prince, J.L. (2000). "Medical Image Segmentation Using Deformable Models," Handbook of Medical Imaging, Volume 2: Medical Image Processing and Analysis, PP.129-174, edited by Fitzpatrick, J.M., and Sonka, M., SPIE Press.

    [5] Xu, C., and Prince, J.L. "Gradient Vector Flow: A New External Force for Snakes," Proc. IEEE Conf. on CVPR, Los Alamitos Comp. Soc. Press, PP.66-71, June 1997.

    [6] Azizi, Amir, Haf conversion, Electronics Research Institute of Iran University of Science and Technology: Department of Machine Vision and Image Processing, Azar 1389, [Online], .

    [7] Zamyatin, A. "Method, Apparatus, and Computer Program Product for Sinogram" Completions". Patent Number: 7515676, Patent Date: Apr. 7, 2009.

    [8] Zamyatin, A.A., and Nakanishi, S. (2006). "Sinogram Correction Methods using Sinogram Decomposition," IEEE Nuclear Science Symposium Conference Record, vol.6, pp.3438-3440.

    [9] Changchun, Z., and Ge, S. "A Hough Transform-Based Method for Fast Detection of Fixed Period Sinusoidal Curves in Images," in Proceedings of the 6th International Conference on Signal Processing, vol. 1, 2002, pp. 909-912, Aug. 26-30.

    [10] Ballard, D.H. (1987). "Generalizing the Hough Transform to Detect Arbitrary Shapes," Readings in Computer Vision: issues, problems, principles, and paradigms, pp. 714-725.

    [11] Ebraheim, N.A., Coombs, R., and Rusin, J.J. (1990). "Reduction of Postoperative CT Artifacts of Pelvis Fractures by Use of Titanium Implants," Orthopedics, vol. 13, pp. 1357-1358.

    [12] Kalender, W., Hebel, R., and Ebersberger, J. (1987). "Reduction of CT Artifacts Caused by Metallic Implants," Radiology, vol. 164, No. 2, pp. 576-577.

    [13] Klotz, E., and Kalender, W. (1990). "Algorithms for the Reduction of CT Artifacts Caused by Metallic Implants," Medical Imaging, vol. 1234, pp. 642-650.

    [14] Liu, J., Watt-Smith, S., and Smith, S. (2003). "CT Reconstruction Using FBP with Sinusoidal Amendment for Metal Artifact Reduction," Proceeding of 7th Digital Image Computing: Techniques and Applications, pp. 439-447.

    [15] Mahnken, A.H., Raupach, R., and Wildberger, J.E. (2003).

Separation of prosthesis projections and tissue implants in spiral CT scan sinogram images using active contour methods