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rdf:type
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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)
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Title
| - Machine learning techniques for prostate ultrasound image diagnosis
- Machine learning techniques for prostate ultrasound image diagnosis (en)
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skos:prefLabel
| - Machine learning techniques for prostate ultrasound image diagnosis
- Machine learning techniques for prostate ultrasound image diagnosis (en)
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skos:notation
| - RIV/61989100:27240/10:86080854!RIV12-MSM-27240___
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http://linked.open...avai/riv/aktivita
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http://linked.open...avai/riv/aktivity
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http://linked.open...iv/cisloPeriodika
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http://linked.open...vai/riv/dodaniDat
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http://linked.open...aciTvurceVysledku
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http://linked.open.../riv/druhVysledku
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http://linked.open...iv/duvernostUdaju
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http://linked.open...titaPredkladatele
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http://linked.open...dnocenehoVysledku
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http://linked.open...ai/riv/idVysledku
| - RIV/61989100:27240/10:86080854
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http://linked.open...riv/jazykVysledku
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http://linked.open.../riv/klicovaSlova
| - Wavelet; Rough set; Prostate ultrasound imaging; Machine learning; Intelligent hybrid approach; Fuzzy type-II; Computational intelligence (en)
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http://linked.open.../riv/klicoveSlovo
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http://linked.open...odStatuVydavatele
| - DE - Spolková republika Německo
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http://linked.open...ontrolniKodProRIV
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http://linked.open...i/riv/nazevZdroje
| - Studies in Computational Intelligence
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http://linked.open...in/vavai/riv/obor
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http://linked.open...ichTvurcuVysledku
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http://linked.open...cetTvurcuVysledku
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http://linked.open...UplatneniVysledku
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http://linked.open...v/svazekPeriodika
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http://linked.open...iv/tvurceVysledku
| - Hassanien, A. E.
- Snášel, Václav
- Al-Qaheri, H.
- Peters, J. F.
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http://linked.open...n/vavai/riv/zamer
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issn
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number of pages
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http://bibframe.org/vocab/doi
| - 10.1007/978-3-642-05177-7_19
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http://localhost/t...ganizacniJednotka
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