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- Type of Document: M.Sc. Thesis
- Language: English
- Document No: 41617 (55)
- University: Sharif University of Technology, International Campus, Kish Island
- Department: Science and Engineering
- Advisor(s): Manzuri Shalmani, Mohammad Taghi; Ghorshi, Mohammad Ali
- Abstract:
- Medical images contain information about vital organic tissues inside of the human body and are widely used for diagnoses of diseases or for surgical purposes. Image Reconstruction is essential for medical imaging for medical systems like suppression of noise or de-blurring from projections in order to have images with better quality and contrast. Due to the vital role of image reconstruction in medical sciences the corresponding algorithms with better efficiency and higher speed is preferable. Most algorithms in the field of image reconstruction are operated in frequency domain such as the filtered back projection which is a popular algorithm for the reconstruction of the medical images. In this thesis we introduce a new algorithm for image reconstruction which is operated in time domain with the use of recursive optimal filter known as Kalman filter. The proposed technique uses X-ray scan geometry in order to collect the projection data which is called ray sum. The data are used to build a new model known as linear observation model in addition to extra noise that comes from the sensors. Finally, the proposed reconstruction method is applied to more than fifty images, and the performance is measured based on the noise reduction ability of the algorithm
- Keywords:
- Medical Images ; Noise ; Reconstruction ; Kalman Filters ; Tomography ; Bluring Effect
Digital Object List-
محتواي پايان نامه - view
Bookmark- Introduction
- Background and Related works
- / Figure 2.2 Discretization of the projection scene with parallel projection [1].
- Reconstruction via Frequency Domain
- Reconstruction from Parallel Projections by Filtered Back-Projection
- Reconstruction from Fan Projections
- Algebraic Methods of Reconstruction for fan projection
- Reconstruction under Nonzero Attenuation
- SPECT Type Imaging
- PET Type Imaging
- Reconstruction from Stochastic Projections
- Stochastic Models of Projections
- Principle of Maximum-Likelihood Reconstruction
- Image Reconstruction using morphological operation
- Image Reconstruction using wiener filtering
- Motivation
- Proposed Method
- Kalman Filter
- Statement of the Kalman Filtering Problem
- The Innovation Process
- Correlation Matrix of the Innovation process
- Estimation of State Using the Innovation Process
- Kalman Gain
- Riccati Equation solver
- Tomography Image Reconstruction using Kalman filter
- Introduction
- Reconstruction Results for Different Images
- Surface analysis of different Reconstruction images
- Error index analysis
- Reconstruction of blurred image from blurred projection
- References