@article{MTMT:32472290,
	title = {Regression Based Iterative Illumination Compensation Method for Multi-Focal Whole Slide Imaging System},
	url = {https://m2.mtmt.hu/api/publication/32472290},
	author = {Paulik, Róbert and Kozlovszky, Miklós and Molnár, Béla},
	doi = {10.3390/s21217085},
	journal-iso = {SENSORS-BASEL},
	journal = {SENSORS},
	volume = {21},
	unique-id = {32472290},
	abstract = {Image quality, resolution and scanning time are critical in digital pathology. In order to create a high-resolution digital image, the scanner systems execute stitching algorithms to the digitized images. Due to the heterogeneity of the tissue sample, complex optical path, non-acceptable sample quality or rapid stage movement, the intensities on pictures can be uneven. The evincible and visible intensity distortions can have negative effect on diagnosis and quantitative analysis. Utilizing the common areas of the neighboring field-of-views, we can estimate compensations to eliminate the inhomogeneities. We implemented and validated five different approaches for compensating output images created with an area scanner system. The proposed methods are based on traditional methods such as adaptive histogram matching, regression-based corrections and state-of-the art methods like the background and shading correction (BaSiC) method. The proposed compensation methods are suitable for both brightfield and fluorescent images, and robust enough against dust, bubbles, and optical aberrations. The proposed methods are able to correct not only the fixed-pattern artefacts but the stochastic uneven illumination along the neighboring or above field-of-views utilizing iterative approaches and multi-focal compensations.},
	year = {2021},
	eissn = {1424-8220},
	orcid-numbers = {Paulik, Róbert/0000-0001-9738-8336; Molnár, Béla/0000-0001-6655-7942}
}

@article{MTMT:3392656,
	title = {Hierarchical Histogram-based Median Filter for GPUs},
	url = {https://m2.mtmt.hu/api/publication/3392656},
	author = {Szántó, Péter and Fehér, Béla},
	doi = {10.12700/APH.15.1.2018.2.3},
	journal-iso = {ACTA POLYTECH HUNG},
	journal = {ACTA POLYTECHNICA HUNGARICA},
	volume = {15},
	unique-id = {3392656},
	issn = {1785-8860},
	abstract = {Median filtering is a widely used non-linear noise-filtering algorithm, which can efficiently remove salt and pepper noise while it preserves the edges of the objects. Unlike linear filters, which use multiply-and-accumulate operation, median filter sorts the input elements and selects the median of them. This makes it computationally more intensive and less straightforward to implement. This paper describes several algorithms which could be used on parallel architectures and propose a histogram based algorithm which can be efficiently executed on GPUs, resulting in the fastest known algorithm for medium sized filter windows. The paper also presents an optimized sorting network based implementation, which outperforms previous solutions for smaller filter window sizes.},
	keywords = {FILTER; CUDA; GPGPU; SIMD; Median},
	year = {2018},
	eissn = {1785-8860},
	pages = {49-68}
}

@article{MTMT:2383237,
	title = {Evolutionary Algorithm for Optimizing Parameters of GPGPU-based Image Segmentation},
	url = {https://m2.mtmt.hu/api/publication/2383237},
	author = {Szénási, Sándor and Vámossy, Zoltán Imre},
	doi = {10.12700/APH.10.05.2013.5.2},
	journal-iso = {ACTA POLYTECH HUNG},
	journal = {ACTA POLYTECHNICA HUNGARICA},
	volume = {10},
	unique-id = {2383237},
	issn = {1785-8860},
	abstract = {The use of digital microscopy allows diagnosis through automated quantitative and qualitative analysis of the digital images. Often to evaluate the samples, the first step is determining the number and location of cell nuclei. For this purpose, we have developed a GPGPU based data-parallel region growing algorithm that is equally as accurate as the already existing sequential versions, but its speed is two or three times faster (implementing in CUDA environment), but this algorithm is very sensitive to the appropriate setting of different parameters. Due to the large number of parameters and due to the big set of possible values setting those parameters manually is a quite hard task, so we have developed a genetic algorithm to optimize these values. Our evolution-based algorithm that is described in this paper was used to successfully determine a set of parameters that compared to the results with the previously known best set of parameters means a significantly improvement.},
	year = {2013},
	eissn = {1785-8860},
	pages = {7-28},
	orcid-numbers = {Szénási, Sándor/0000-0002-7292-0717; Vámossy, Zoltán Imre/0000-0002-6040-9954}
}

@article{MTMT:2377578,
	title = {Implementation of a Distributed Genetic Algorithm for Parameter Optimization in a Cell Nuclei Detection Project},
	url = {https://m2.mtmt.hu/api/publication/2377578},
	author = {Szénási, Sándor and Vámossy, Zoltán Imre},
	doi = {10.12700/APH.10.04.2013.4.4},
	journal-iso = {ACTA POLYTECH HUNG},
	journal = {ACTA POLYTECHNICA HUNGARICA},
	volume = {10},
	unique-id = {2377578},
	issn = {1785-8860},
	year = {2013},
	eissn = {1785-8860},
	pages = {59-86},
	orcid-numbers = {Szénási, Sándor/0000-0002-7292-0717; Vámossy, Zoltán Imre/0000-0002-6040-9954}
}