Resumen

ORELLANA GARCIA, Arturo  y  GARCIA PORTAL, Luis Manuel. Segmentation and processing techniques for the detection of Renal Carcinomas in Abdominal Tomography images. RCIM [online]. 2020, vol.12, n.2, e386.  Epub 01-Dic-2020. ISSN 1684-1859.

One of the most recognized campaigns in the world is the fight against cancer, the kidney system being one of the most affected by this pathology. Renal cell carcinoma (RCC), the most common form of kidney cancer in adults, represents the sixth leading cause of cancer death. Due to the increased use of diagnostic imaging techniques, kidney injuries can be diagnosed incidentally in approximately 50% of cases. Cuba is committed to the use of technology in health and a system for the storage, transmission and display of medical images (XAVIA PACS) has been developed at the University of Computer Sciences (UCI), which is implanted in several hospitals of the country, but it does not have alternatives to detect RCC in tomographic images, slowing down the diagnosis, which translates into fewer possibilities for the patient.

The objective of this research is to carry out an analysis on the main segmentation and processing techniques for the detection of renal carcinomas in abdominal tomography images, which provides development teams with the theoretical basis necessary to face the problem in question. For this, a documentary analysis was carried out on works related to the subject and that provide solutions to the problem. Algorithms and effective computational techniques for the segmentation and processing of abdominal images were studied. As a result of the research, the most suitable algorithms for the XAVIA PACS system and the Cuban medical context were obtained.

Palabras clave : algorithm analysis; renal carcinoma; Watersheds Transform; medical informatics; XAVIA PACS.

        · resumen en Español     · texto en Español     · Español ( pdf )