Automatic ROI positioning in ultrasound TCS images using artificial intelligence to Parkinson's disease risk

Authors:
Jiří Blahuta;Tomáš Soukup;Petr Čermák;David Novák;Michal Večerek
Affiliations:
Silesian University in Opava, Department of Informatics, Opava, Czech Republic;Silesian University in Opava, Department of Informatics, Opava, Czech Republic;Silesian University in Opava, Department of Informatics, Opava, Czech Republic;Silesian University in Opava, Department of Informatics, Opava, Czech Republic;Vysoká škola báňská, Technická univerzita Ostrava, Czech Republic
Venue:
BICA'12 Proceedings of the 5th WSEAS congress on Applied Computing conference, and Proceedings of the 1st international conference on Biologically Inspired Computation
Year:
2012

Citing 4
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The ROI defect statistical analysis of substantia nigra to reproducibility of designed experimental algorithm for potential Parkinson's disease diagnosis

MACMESE'11 Proceedings of the 13th WSEAS international conference on Mathematical and computational methods in science and engineering

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Abstract

The aim of this work is semi-automatic ROI positioning in transcranial images based on ANN module. We need to learn ANN to accurate positioning of ROI inside substantia nigra of transcranial images. Designed approach is based on image processing and is realized by means of artificial intelligence which has been experimentally simulated in MATLAB software environment. This method is well applicable with Neural Network Toolbox in MATLAB. Within this processing has been worked with a set of TCS images in grayscale and/or binary representation to experimental testing to automatic positioning.