NDLI: Shape-based kidney detection and segmentation in three-dimensional abdominal ultrasound images

Content Provider	IEEE Xplore Digital Library
Author	Marsousi, M. Plataniotis, K.N. Stergiopoulos, S.
Copyright Year	2014
Description	Author affiliation: Electr. & Comput. Eng. Dept., Commun. Group, Multimedia Process. Lab., Univ. of Toronto, Toronto, ON, Canada (Marsousi, M.) \|\| Defence R&D Canada - Toronto, Candadian Dept. of Defence, Toronto, ON, Canada (Stergiopoulos, S.) \|\| Electr. & Comput. Eng. Dept., Univ. of Toronto, Toronto, ON, Canada (Plataniotis, K.N.)
Abstract	Due to recent technical advancements of three-dimensional ultrasound imaging systems, applications of this imaging modality have been expanding from the fetal imaging to cardiac- and abdominal-diagnosis. Among all internal organs, diagnosing the kidney has a paramount importance for rapid bedside treatment of trauma and kidney stone patients using ultrasound images. Although three-dimensional ultrasound provides higher level of structural information of kidneys, manual kidney diagnosis using three-dimensional ultrasound images requires a highly trained medical staff, due to the extensive visual complexity which three-dimensional images contain. Therefore, computer aided automated kidney diagnosis becomes very essential. Due to the challenging problems of ultrasound images, such as speckle noise and inhomogeneous intensity profile, kidney segmentation in three-dimensional ultrasound images has not been sufficiently investigated by researchers. In this paper, we first propose a new automated kidney detection approach using three-dimensional Morison's pouch ultrasound images. Then, we proposed a shape-based method to segment the detected kidneys. A preprocessing step is utilized to overcome the ultrasound challenges. Based on a set of 14 ultrasound volumes, we have evaluated the detection rate of our proposed kidney detection approach which is 92.86%. For kidney segmentation, we compared our proposed method with an existing approach, and the performed statistical analysis strongly validates the superiority of our proposed method with p = 0.000032.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	2890
Ending Page	2894
File Size	1429239
Page Count	5
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6944227
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kidney Ultrasonic imaging Image segmentation Shape Imaging Histograms Three-dimensional displays
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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