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A Method for Improving Renogram Production and Detection of Renal Pelvis using Mathematical Morphology on Scintigraphic Images
Author(s) -
Stefanos Xefteris,
Konstantinos Tserpes,
Theodora Varvarigou
Publication year - 2012
Publication title -
engineering, technology and applied science research/engineering, technology and applied science research
Language(s) - English
Resource type - Journals
eISSN - 2241-4487
pISSN - 1792-8036
DOI - 10.48084/etasr.206
Subject(s) - computer science , pixel , artificial intelligence , scintigraphy , computer vision , process (computing) , radiology , medicine , nuclear medicine , operating system
Dynamic renal scintigraphy is a well-established imaging technique in nuclear medicine, used to detail both the organ's anatomy and function. However, the quality of the produced scintigrams provides an often unreliable diagnostic tool because of a rather bad signal-to-noise ratio and the fact that in certain occasions the regions of interest are too concentrated making it difficult for physician evaluation. The goal of this paper is to achieve a more accurate production of the renal activity graph, by avoiding the inclusion of image artifacts in the detection process. This is achieved by treating pixels as points in a two-dimensional Euclidean space, and exploiting set-theoretic properties and morphological operators. The evaluation of the method in a number of real patient’s scintigrams obtained in a depth of 5 years, showed that, in the majority of the cases, it is feasible to produce a more accurate renogram, for both kidneys and the renal pelvis region, that was helpful for the interpretation of the findings

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