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All Projects
Theoretical aspects of main problems in Discrete Tomography are studied such as existence, consistency, and reconstruction under several assumptions. Efficient reconstruction algorithms are also developed by incorporating prior knowledge into the reconstruction.
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An automatic registration method utilizing mutual information and its various medical and neutron tomography applications.
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Skeletonization has been successfully applied in the following three medical applications: assessment of laryngotracheal stenosis, assessment of infrarenal aortic aneurysm, and unravelling the colon.
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The goal of this project is to propose a method which is able to segment a color image without any human intervention. The only input is the observed image, all other parameters are estimated during the segmentation process. The algorithm finds the most likely number of classes, their associated model parameters and generates a segmentation of the image by classifying the pixels into these classes.
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The thinning is an iterative layer by layer erosion until only the "skeletons" of the objects are left. We proposed various 3D thinning algorithms capable of extracting medial lines or medial surfaces as well.
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We investigated registration methods based on interactively identified point pairs used in medical image registration. We proposed an affine search method and gave a sufficient existence condition for the unique solution. The properties of rigid-body and affine methods were examined via numerical simulations.
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We developed an image processing method for MRI intensity standardization. We also introduced new, fast implementations of the fuzzy connectedness algorithm that allows segmentation at interactive speeds. We developed a new segmentation "workshop" for brain MRI segmentation using standardized MR images and the fast fuzzy connectedness algorithms.
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Scanned cadastral maps have been vectorized, and map objects have been recognized using graph algorithms and neural network.
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The main contribution of this project is the implementation of a Markovian image segmentation model on a new chip, called CNN (Cellular Neural Networks), which is capable to do the task in real time.
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SZOTE-PACS is the Picture Archiving and Communication System of Albert Szent-Gyorgyi Medical University. It is able to collect studies from CT, MR, SPECT, US and modalities and convert them into DICOM format.
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