László Ruskó
General Electric
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Publication
Featured researches published by László Ruskó.
Medical Image Analysis | 2009
László Ruskó; György Bekes; Marta Fidrich
Segmentation of contrast-enhanced abdominal CT images is required by many clinical applications of computer aided diagnosis and therapy planning. The research on automated methods involves different organs among which the liver is the most emphasized. In the current clinical practice more images (at different phases) are acquired from the region of interest in case of a contrast-enhanced abdominal CT examination. The majority of the existing methods, however, use only the portal-venous image to segment the liver. This paper presents a method that automatically segments the liver by combining more phases of the contrast-enhanced CT examination. The method uses region-growing facilitated by pre- and post-processing functions, which incorporate anatomical and multi-phase information to eliminate over- and under-segmentation. Another method, which uses only the portal-venous phase to segment the liver automatically, is also presented. Both methods were evaluated using different datasets, which showed that the result of multi-phase method can be used without or after minor correction in nearly 94% of the cases, and the single-phase method can provide result comparable with non-expert manual segmentation in 90% of the cases. The comparison of the two methods demonstrates that automatic segmentation is more reliable when the information of more phases is combined.
Computers in Biology and Medicine | 2016
Márton József Tóth; László Ruskó; Balázs Csébfalvi
This paper presents a method that detects anatomy regions in three-dimensional medical images. The method labels each axial slice of the image according to the anatomy region it belongs to. The detected regions are the head (and neck), the chest, the abdomen, the pelvis, and the legs. The proposed method consists of two main parts. The core of the algorithm is based on a two-dimensional feature extraction that is followed by a random forest classification. This recognition process achieves an overall accuracy of 91.5% in slice classification, but it cannot always provide fully consistent labeling. The subsequent post-processing step incorporates the expected sequence and size of the human anatomy regions in order to improve the accuracy of the labeling. In this part of the algorithm the detected anatomy regions (represented by Gaussian distributions) are fitted to the region probabilities provided by the random forest classifier. The proposed method was evaluated on a set of whole-body MR images. The results demonstrate that the accuracy of the labeling can be increased to 94.1% using the presented post-processing. In order to demonstrate the robustness of the proposed method it was applied to partial MRI scans of different sizes (cut from the whole-body examinations). According to the results the proposed method works reliably (91.3%) for partial body scans (having as little length as 35cm) as well.
Archive | 2007
László Ruskó; György Bekes; Gábor Németh; Marta Fidrich
computer assisted radiology and surgery | 2011
László Ruskó; György Bekes
Archive | 2008
Marta Fidrich; László Ruskó; Gyorgi Bekes
Archive | 2007
Marta Fidrich; Gyorgi Bekes; László Ruskó
Archive | 2007
László Ruskó; György Bekes; Marta Fidrich
computer assisted radiology and surgery | 2014
László Ruskó; Ádám Perényi
Archive | 2012
Gyula Molnar; László Ruskó
Archive | 2013
Tamas Blaskovics; László Ruskó; Marta Fidrich