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Simultaneous reconstruction, segmentation, and edge enhancement of relatively piecewise continuous images with intensity‐level information
Author(s) -
Liang Z.,
Jaszczak R.,
Coleman R.,
Johnson V.
Publication year - 1991
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.596685
Subject(s) - imaging phantom , projection (relational algebra) , intensity (physics) , iterative reconstruction , piecewise , segmentation , noise (video) , mathematics , image segmentation , pixel , poisson distribution , physics , artificial intelligence , image (mathematics) , computer vision , computer science , optics , algorithm , statistics , mathematical analysis
A multinomial image model is proposed which uses intensity‐level information for reconstruction of contiguous image regions. The intensity‐level information assumes that image intensities are relatively constant within contiguous regions over the image‐pixel array and that intensity levels of these regions are determined either empirically or theoretically by information criteria. These conditions may be valid, for example, for cardiac blood‐pool imaging, where the intensity levels (or radionuclide activities) of myocardium, blood‐pool, and background regions are distinct and the activities within each region of muscle, blood, or background are relatively uniform. To test the model, a mathematical phantom over a 64×64 array was constructed. The phantom had three contiguous regions. Each region had a different intensity level. Measurements from the phantom were simulated using an emission‐tomography geometry. Fifty projections were generated over 180°, with 64 equally spaced parallel rays per projection. Projection data were randomized to contain Poisson noise. Image reconstructions were performed using an iterative maximum a p o s t e r i o r i probability procedure. The contiguous regions corresponding to the three intensity levels were automatically segmented. Simultaneously, the edges of the regions were sharpened. Noise in the reconstructed images was significantly suppressed. Convergence of the iterative procedure to the phantom was observed. Compared with maximum likelihood and filtered‐backprojection approaches, the results obtained using the maximum a p o s t e r i o r i probability with the intensity‐level information demonstrated qualitative and quantitative improvement in localizing the regions of varying intensities.