DocumentCode :
693805
Title :
Segmentation of the Left Ventricle from Ultrasound Using Random Forest with Active Shape Model
Author :
Belous, Gregg ; Busch, Andrew ; Rowlands, David
Author_Institution :
Centre for Wireless Monitoring & Applic., Griffith Univ., Brisbane, QLD, Australia
fYear :
2013
fDate :
3-5 Dec. 2013
Firstpage :
315
Lastpage :
319
Abstract :
This paper presents a model-based learning segmentation algorithm to detect the left ventricle (LV) boundary of the heart from ultrasound (US) images by combining a random forest classifier with an active shape model (ASM). Our method applies an ASM for initial detection of the LV landmarks. Each landmark is subsequently directed radially inward or outward as a result of the random forest classifier identifying the landmark as outside or inside the LV boundary, respectively. This is done while preserving the shape characteristics obtained from the ASM. Our objective is to evaluate the combined application of a random forest classifier with an ASM for detecting the LV boundary with US images. Accuracy of this method is evaluated by comparing both our method and ASM to LV contours traced by an expert. A dataset of 85 randomly selected patient studies was chosen. The method exhibits improved accuracy compared to the ASM, producing a global overlap coefficient of 90.09% compared to 83.8% obtained with an active shape model.
Keywords :
echocardiography; image classification; image segmentation; learning (artificial intelligence); medical image processing; object detection; ultrasonic imaging; ASM; LV landmark detection; active shape model; heart left ventricle boundary detection; left ventricle segmentation; model-based learning segmentation algorithm; random forest classifier; ultrasound images; Active shape model; Image segmentation; Shape; Training; Ultrasonic imaging; Vectors; Vegetation; active shape model; random forest; ultrasound;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Artificial Intelligence, Modelling and Simulation (AIMS), 2013 1st International Conference on
Conference_Location :
Kota Kinabalu
Print_ISBN :
978-1-4799-3250-4
Type :
conf
DOI :
10.1109/AIMS.2013.58
Filename :
6959936
Link To Document :
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