About: Machine learning techniques for prostate ultrasound image diagnosis

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	Estimation of prostate location and volume is essential in determining a dose plan for ultrasound-guided brachytherapy, a common prostate cancer treatment. However, manual segmentation is difficult, time consuming and prone to variability. In this chapter, we present a machine learning scheme, employing a combination of fuzzy sets, wavelets and rough sets, for analyzing prostrate ultrasound images in order diagnose prostate cancer. To address the image noise problem we first utilize an algorithm based on type-II fuzzy sets to enhance the contrast of the ultrasound image. This is followed by performing a modified fuzzy c-mean clustering algorithm in order to detect the boundary of the prostate pattern. Then, a wavelet features are extracted and normalized, followed by application of a rough set analysis for discrimination of different regions of interest to determine whether they represent cancer or not. The experimental results obtained, show that the overall classification accuracy offered by the employed rough set approach is high compared with other machine learning techniques including decision trees, discriminant analysis, rough neural networks, and neural networks. Estimation of prostate location and volume is essential in determining a dose plan for ultrasound-guided brachytherapy, a common prostate cancer treatment. However, manual segmentation is difficult, time consuming and prone to variability. In this chapter, we present a machine learning scheme, employing a combination of fuzzy sets, wavelets and rough sets, for analyzing prostrate ultrasound images in order diagnose prostate cancer. To address the image noise problem we first utilize an algorithm based on type-II fuzzy sets to enhance the contrast of the ultrasound image. This is followed by performing a modified fuzzy c-mean clustering algorithm in order to detect the boundary of the prostate pattern. Then, a wavelet features are extracted and normalized, followed by application of a rough set analysis for discrimination of different regions of interest to determine whether they represent cancer or not. The experimental results obtained, show that the overall classification accuracy offered by the employed rough set approach is high compared with other machine learning techniques including decision trees, discriminant analysis, rough neural networks, and neural networks. (en)
Title	Machine learning techniques for prostate ultrasound image diagnosis Machine learning techniques for prostate ultrasound image diagnosis (en)
skos:prefLabel	Machine learning techniques for prostate ultrasound image diagnosis Machine learning techniques for prostate ultrasound image diagnosis (en)
skos:notation	RIV/61989100:27240/10:86080854!RIV12-MSM-27240___
http://linked.open...avai/riv/aktivita	Z
http://linked.open...avai/riv/aktivity	Z(MSM6198910027)
http://linked.open...iv/cisloPeriodika	2010
http://linked.open...vai/riv/dodaniDat	2012
http://linked.open...aciTvurceVysledku	Snášel, Václav
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Vysoká škola báňská - Technická univerzita Ostrava / Fakulta elektrotechniky a informatiky
http://linked.open...dnocenehoVysledku	269098
http://linked.open...ai/riv/idVysledku	RIV/61989100:27240/10:86080854
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Wavelet; Rough set; Prostate ultrasound imaging; Machine learning; Intelligent hybrid approach; Fuzzy type-II; Computational intelligence (en)
http://linked.open.../riv/klicoveSlovo	Machine learning Computational intelligence Wavelet Rough set Fuzzy type-II Intelligent hybrid approach Prostate ultrasound imaging
http://linked.open...odStatuVydavatele	DE - Spolková republika Německo
http://linked.open...ontrolniKodProRIV	[EC94902E5749]
http://linked.open...i/riv/nazevZdroje	Studies in Computational Intelligence
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	1 (xsd:int)
http://linked.open...cetTvurcuVysledku	4 (xsd:int)
http://linked.open...UplatneniVysledku	2010
http://linked.open...v/svazekPeriodika	262
http://linked.open...iv/tvurceVysledku	Hassanien, A. E. Snášel, Václav Al-Qaheri, H. Peters, J. F.
http://linked.open...n/vavai/riv/zamer	Výpočetně náročné počítačové simulace a optimalizace
issn	1860-949X
number of pages	19 (xsd:int)
http://bibframe.org/vocab/doi	10.1007/978-3-642-05177-7_19
http://localhost/t...ganizacniJednotka	27240

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