dc.creatorRodríguez-Ibáñez, R
dc.creatorVera, M I
dc.creatorVera, M
dc.creatorGelvez-Almeida, E
dc.creatorHuérfano, Y
dc.creatorValbuena, O
dc.creatorSalazar-Torres, J
dc.date.accessioned2020-03-26T21:36:37Z
dc.date.accessioned2022-11-14T20:00:12Z
dc.date.available2020-03-26T21:36:37Z
dc.date.available2022-11-14T20:00:12Z
dc.date.created2020-03-26T21:36:37Z
dc.date.issued2019
dc.identifier17426596
dc.identifierhttps://hdl.handle.net/20.500.12442/5070
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5185529
dc.description.abstractRenal lithiasis is the pathology that causes nephritic colic, which is one of the most frequent reasons for consultation in emergency medical services. According to the size, location, hardness and number of stones present in the urinary system, usually in the human kidney, it is established to which form of treatment is suitable for the patient. These kidney stones can be analyzed by means of biopsy or imaging modalities such as computed tomography images. This type of images has challenging problems called noise, artifacts and low contrast. In this paper, in order to address these problems, a non-parametric semi-automatic computational technique is developed for detecting kidney stones, present in computed tomography images, using digital image processing techniques based on a smoothing filter and an edge detector. Finally, the size and position of the stones present in the images are calculated and a precision metric is considered to compare the manual segmentation, performed by an urologist, and the one generated by the NPCT, obtaining an excellent correlation. This technique can be useful in the renal lithiasis detection and if it is considering this kind of computational strategy, medical specialists can establish the clinic or surgical actions oriented to address this pathology.
dc.languageeng
dc.publisherIOP Publishing
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.sourceJournal of Physics: Conference Series
dc.sourceVol. 1414 (2019)
dc.sourcehttps://iopscience.iop.org/article/10.1088/1742-6596/1414/1/012019
dc.subjectRenal lithiasis
dc.subjectUrinary system
dc.subjectComputed tomography images
dc.subjectKidney stones
dc.titleRenal lithiasis detection in uro-computed tomography using a non-parametric technique
dc.typearticle


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