DocumentCode :
3010805
Title :
An intelligent system for renal segmentation
Author :
Aribi, Yassine ; Wali, Ali ; Alimi, Adel M.
Author_Institution :
REGIM: Res. Groups on Intell. Machines, Univ. of Sfax, Sfax, Tunisia
fYear :
2013
fDate :
9-12 Oct. 2013
Firstpage :
11
Lastpage :
15
Abstract :
Scintigraphic images are often characterized with much noise and a badly contrasted resolution which makes the perception of regions of interest very difficult. The renal quantification is how to define the regions of interests whose activities informs on the status of the renal function. In this context, the current study presents an intelligent system for the segmentation of renal regions in order to facilitate the process of quantification. The use of a multi-agent system based on the HOG3D descriptor combined with Fast Marching Method, has made our System of segmentation faster and more accurate. This automatic segmentation system is expected to assist physicians in both clinical diagnosis and educational training. Experiments and tests were developed on a database including 1800 images from 15 patients selected to obtain a variety of images. The results of the application of our method on several dynamic images are presented and discussed.
Keywords :
image segmentation; knowledge based systems; medical image processing; patient diagnosis; radioisotope imaging; HOG3D descriptor; automatic segmentation system; clinical diagnosis; educational training; fast marching method; intelligent system; multiagent system; renal function; renal quantification; renal regions; renal segmentation; scintigraphic images; Algorithm design and analysis; Conferences; Educational institutions; Image segmentation; Kidney; Manuals; Multi-agent systems; Fast Marching Method; Hog3D; Multi-Agent System; Renal Quantification; Scintigraphics images; intelligent system;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
e-Health Networking, Applications & Services (Healthcom), 2013 IEEE 15th International Conference on
Conference_Location :
Lisbon
Print_ISBN :
978-1-4673-5800-2
Type :
conf
DOI :
10.1109/HealthCom.2013.6720629
Filename :
6720629
Link To Document :
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