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Image Processing on Projections and Its Applications for Image Reconstruction

Lifetime from: 
2009
Lifetime to: 
2013
Short description: 
The aim of this project is to improve the well-known methods of continuous and discrete tomography. We study image processing methods that can be applied directly on the projections to improve reconstruction quality. We improve techniques to measure the quality of the reconstructed images, and develop image reconstruction methods based on parallel processing using GPUs. We also investigate which are the most valuable projections, if the reconstruction is performed from just a few of them.
Description: 

Reconstruction tomography produces 2-dimensional cross-sections of 3-dimensional objects from their projections taken from several directions. Depending on the reconstruction task, projections can be acquisited by X-rays, gamma-rays, etc. In the most common situation radiation is generated outside the object (CT), in other scenarios the object itself emits gamma-rays (SPECT, PET). In image reconstruction the task is – roughly speaking – to backproject the sinogram consisting of the projections of the original image. This is usually performed by filtered backprojection (FBP) or algebraic iterative methods (ART, SART, SIRT). In practice, projections are taken from discrete angles, and they are noisy, too. The aim of this project is to improve the well-known methods of continuous and discrete tomography, and especially the followings:

  • study of image processing methods, that can be applied directly on the projections to improve reconstruction quality (active contours, segmentation, edge-detection, denoising, etc.)
  • incremental reconstruction by identifying important projection angles
  • improving techniques to measure the quality of the reconstructed images (similarity to model image, statistical features, smoothness, etc.)
  • measuring the information content of projections
  • determination of attenuation coefficients from the sinogram for certain classes of images
  • developing efficient discrete image reconstruction methods of novel types, based on parallel processing using GPUs where possible
Publications: 
Varga L, Balázs P, Nagy A. Projection selection dependency in binary tomography. ACTA CYBERNETICA-SZEGED. 2011;20(1):167-187.
Varga L, Balázs P, Nagy A. Direction-dependency of a binary tomographic reconstruction algorithm. In: Barneva RP, Brimkov VE, Hauptman HA, Jorge R M N, Tavares JManuel RS, editors. Computational Modeling of Objects Represented in Images. Buffalo, NY, USA: Springer Verlag; 2010. 2. p. 242-253p. (Lecture Notes in Computer Science).
Varga L, Balázs P, Nagy A. Projection selection algorithms for discrete tomography. In: Blanc-Talon J, Bone D, Philips W, Popescu D, Scheunders P, editors. Advanced Concepts for Intelligent Vision Systems. Sydney, Australia : Springer Verlag; 2010. 3. p. 390-401p.
Varga L, Balázs P, Nagy A. An energy minimization reconstruction algorithm for multivalued discrete tomography. In: Di Giamberardino P, Iacoviello D, Jorge R M N, Taveres JManuel RS, editors. Computational Modelling of Objects Represented in Images: Fundamentals, Methods and Applications III. London: CRC Press - Taylor and Frances Group; 2012. 1. p. 179-185p.
Balázs P, Batenburg JK. A central reconstruction based strategy for selecting projection angles in binary tomography. In: Campilho A, Kamel M, editors. Image Analysis and Recognition. Berlin; Heidelberg; New York; London; Paris; Tokyo: Springer; 2012. 3. p. 382-391p. (Lecture Notes in Computer Science).
Varga L, Nyúl LG, Nagy A, Balázs P. Local uncertainty in binary tomographic reconstruction. In: Kampel M, editor. Proceedings of the IASTED International Conference on Signal Processing, Pattern Recognition and Applications (SPPRA 2013). Calgary: IASTED - Acta Press; 2013. 4. p. 490-496p.
Batenburg JK, Palenstijn WJ, Balázs P, Sijbers J. Dynamic angle selection in binary tomography. COMPUTER VISION AND IMAGE UNDERSTANDING. 2013;117(4):306-318.
Kategória: 
Tomography - Discrete Tomography