Název:	DCE MRI Modality Investigation for Cancerous Prostate Region Detection: Case Analysis
Autoři:	Vaitulevicius, Aleksas Treigys, Povilas Bernataviciene, Jolita Surkant, Roman Markeviciute, Jurgita Naruseviciute, Ieva Trakymas, Mantas
Citace zdrojového dokumentu:	WSCG 2022: full papers proceedings: 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 48-55.
Datum vydání:	2022
Nakladatel:	Václav Skala - UNION Agency
Typ dokumentu:	conferenceObject
URI:	http://hdl.handle.net/11025/49578
ISBN:	978-80-86943-33-6
ISSN:	2464-4617
Klíčová slova:	rakovina prostaty;funkční analýza dat;dynamická MRI se zvýšeným kontrastem;strojové učení;komponent
Klíčová slova v dalším jazyce:	prostate cancer;functional data analysis;machine learning;component;dynamic contrast-enhanced MRI
Abstrakt v dalším jazyce:	Typically, prostate evaluation is done by using different imaging sequences of magnetic resonance imaging. Dy- namic contrast enhancement, one of such scanning modalities, allow to spot higher vascular permeability and density caused by the malignant tissue. Authors of this paper investigate the ability to identify malignant prostate regions by the functional data analysis and standard machine learning techniques. The dynamic contrast enhanced images of the prostate are divided into the regions and based on those time-signal intensity curves are calculated. Two classification approaches: functional k-Nearest Neighbors and machine learning Support Vector Machine are used to model signal curve behavior on temporal variation matrix and timestamp based prostate region division of image data. Preliminary research shows that both functional data analysis and machine learning classification methods are able to identify highest saturation timestamp that gives best tissue classification results on timestamp based dynamic contrast enhanced region map obtained by Simple Linear Iterative Clustering algorithm. Cancer region classification results are better when the dynamic contrast enhanced images are subdivided into regions at each timestamp than when using a temporal variation matrix.
Práva:	© Václav Skala - UNION Agency
Vyskytuje se v kolekcích:	WSCG 2022: Full Papers Proceedings

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