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THE CLASSIFICATION OF THE PROGRESSION OF ATHEROSCLEROTIC PLAQUES IN B-IMAGES BETWEEN COMPUTER IMAGE ANALYSIS USING ECHOGENICITY INDEX AND VISUAL ASSESSMENT
Abstract
The aim of the presented paper is to give a review of the classification of atherosclerotic plaques between two different input values; using computer image analysis and a visual assessment by an experienced sonographer. In the case of computer analysis, the echogenicity index (Echo-Index) is used. In the case of visual assessment, echogenicity of the plaques have been evaluated from histological patterns. In the case of computer image analysis, B-images have been processed. To computer image analysis, our developed software B-Mode Assist has been used. For this study, totally of 278 images were analyzed. The plaques were classified into four echogenicity classes by an experienced sonographer. Meanwhile, the images were analyzed by two non-experienced observers using B-Mode Assist software designed for echogenicity index evaluation in B-images. According to the echogenicity index value, images were also classified into 4 classes. The main goal was to observe how many images were classified to the same class using visual assessment and using Echo-Index from ultrasound B-image. Achieved results from initial study showed there is no significant marker for reliable classification the plaques in B-images.
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References3
Skoloudik, D., Bartova, P., Maskova P., Dusek, P., Blahuta, J., Langova, K., Walter, U., Herzig, R. Transcranial Sonography of the Insula: Digitized Image Analysis of Fusion Images with Magnetic Resonance. Ultraschall in der Medizin, Georg Thieme Verlag KG Stuttgart 2016.Vol.6.
Blahuta, J., Soukup, T., Cermak, P. An Expert System Based on Using Artificial Neural Network and Region Based Image Processing to Recognition Substantia Nigra and Atherosclerotic Plaques in B-Images: A Prospective Study. 14th International Work Conference on Artificial Neural Networks, IWANN 2017, Cadiz, Spain, June 14-16, 2017, Proceedings, Part I. Lecture Notes in Computer Science 10305, Springer 2017, pp.236-245, 2017.
Blahuta, J., Soukup, T., Skacel, J. Pilot Design of a Rule Based System and an Artificial Neural Network to Risk Evaluation of Atherosclerotic Plaques in Long Range Clinical Research. ICANN 2018, Lecture Notes in Computer Science book series (LNCS, volume 11140), Springer, 2018, pp. 90-100, ISSN: 978-3-030-01420-9
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