A space-occupying lesion automatic quantification from abdominal contrast-enhanced computerized tomography images
| datacite.rights | http://purl.org/coar/access_right/c_abf2 | eng |
| dc.contributor.author | Bravo, Antonio José | |
| dc.contributor.author | Vera, Miguel Ángel | |
| dc.contributor.author | Huérfano, Yoleidy Katherine | |
| dc.date.accessioned | 2021-01-14T15:17:34Z | |
| dc.date.available | 2021-01-14T15:17:34Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | Space-occupying lessions represent a healt higt risk of subjects affected by this kind of pathology. From a medical point of view, the volume occupied by each of these lesions constitutes the most important descriptor when addressing them, and especially for the respective decision-making process that guides their control, mitigation or elimination. In such context, this paper proposes a strategy based on computer-aided image processing techniques to extract the three-dimensional morphology of a space-occupying lesion, of the amoebic liver abscess type, and calculate its volume. In this sense, in order to attenuate poissonian noise and improve the abscess edge information, the abdominal contrast computed tomography images are preprocessed using a Gaussian filter, and edge detector and a median filter, sequentially. Then, a clustering algorithm based on region growing procedure is applied to the enhanced images, obtaining the space occupying lesion three-dimensional shape. Additionally, the Dice coefficient is considered as a metric to establish the correlation between the shapes, automatic and manual lesion, the latter described by a mastologist. Then, in order to characterize the liver abscess, its volume is quantified considering both the voxels occupied by the lesion obtained by applying of the computer-aided image processing, and the physical dimensions of the voxel. Finally, the automatically calculated volume is compared to that generated manually by the medical specialist. The results reveal an excellent correspondence between manual results and those produced by the proposed technique. This type of technique can be used as a resource not only to obtain, precisely, the value of the aforementioned descriptor, but also to monitor the process of the abscess evolution by means imaging control. | eng |
| dc.description.abstract | Las lesiones que ocupan espacio representan un alto riesgo para la salud de los sujetos afectados por este tipo de patología. Desde el punto de vista médico, el volumen ocupado por cada una de estas lesiones constituye el descriptor más importante al abordarlas, y especialmente para el respectivo proceso de toma de decisiones que guía su control, mitigación o eliminación. En este contexto, este artículo propone una estrategia basada en técnicas de procesamiento de imágenes asistidas por computadora para extraer la morfología tridimensional de una lesión que ocupa espacio, del tipo de absceso hepático amebiano, y calcular su volumen. En este sentido, para atenuar el ruido poissoniano y mejorar la información del borde del absceso, las imágenes de tomografía computarizada de contraste abdominal se preprocesan utilizando un filtro gaussiano, un detector de borde y un filtro de mediana, secuencialmente. Luego, se aplica un algoritmo de agrupamiento basado en el procedimiento de crecimiento de regiones a las imágenes mejoradas, obteniendo la forma tridimensional de la lesión que ocupa espacio. Además, el coeficiente Dice se considera como una métrica para establecer la correlación entre las formas, lesión automática y manual, la última descrita por un mastólogo. Luego, para caracterizar el absceso hepático, su volumen se cuantifica considerando tanto los voxeles ocupados por la lesión obtenida mediante la aplicación del procesamiento de imágenes asistido por computadora, como las dimensiones físicas del voxel. Finalmente, el volumen calculado automáticamente se compara con el generado manualmente por el médico especialista. Los resultados revelan una excelente correspondencia entre los resultados manuales y los producidos por la técnica propuesta. Este tipo de técnica puede usarse como un recurso no solo para obtener, precisamente, el valor del descriptor mencionado anteriormente, sino también para monitorear el proceso de evolución del absceso mediante el control de imágenes. | spa |
| dc.format.mimetype | spa | |
| dc.identifier.issn | 26107988 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12442/6954 | |
| dc.identifier.url | http://www.revistaavft.com/images/revistas/2020/avft_4_2020/20_a_space.pdf | |
| dc.language.iso | eng | eng |
| dc.publisher | Sociedad Venezolana de Farmacología Clínica y Terapéutica | spa |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | eng |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | eng |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.source | Revista AVFT - Archivos Venezolanos de Farmacología y Terapéutica | spa |
| dc.source | Vol. 39, No. 4 (2020) | |
| dc.subject | Computerized tomography | eng |
| dc.subject | Space-occupying lesion | eng |
| dc.subject | imaging filters | eng |
| dc.subject | clustering techniques | eng |
| dc.subject | Dice coefficient | eng |
| dc.subject | Tomografía computarizada | spa |
| dc.subject | Filtrado de imágenes | spa |
| dc.subject | Técnicas de agrupamiento | spa |
| dc.subject | Coeficiente de Dice | spa |
| dc.title | A space-occupying lesion automatic quantification from abdominal contrast-enhanced computerized tomography images | eng |
| dc.title.translated | Cuantificación automática de lesión ocupante de espacio a partir de tomografía computarizada contrastada del abdomen | spa |
| dc.type.driver | info:eu-repo/semantics/article | eng |
| dc.type.spa | Artículo científico | spa |
| dcterms.references | Machadoa, I., Cruza, J., Laverniab, J., Carbonellc, F. Space-occupy- ing lesions of the abdominal wall (not associated with hernia). The pathologist's view. Revista Hispanoamericana de Hernia. 3(3): 85- 94, 2015 | eng |
| dcterms.references | Muller, M.A., Marincek, B., Frauenfelder, I. State of the art 3-D imag- ing of abdominal organs. JBR-BTR, 90: 467-474, 2007 | eng |
| dcterms.references | Shevchenko, N., Seidl, B., Schwaiger, J., Markert, M., Lueth, T.C. MiMed liver: A planning system for liver surgery. Annual International Conference of the IEEE Engineering in Medicine and Biology. 1882- 1885, 2010 | eng |
| dcterms.references | Schiavon, L., Tyng, C., Travesso, D., Rocha, R., Schiavon, A., Biten- court, A. Computed tomography-guided percutaneous biopsy of ab- dominal lesions: indications, techniques, results, and complications. Radiologia brasileira. 2018; 51(3): 141-146. | eng |
| dcterms.references | Ratib, O. Quantitative analysis of cardiac function, in: Bankman, I. (Ed.), Handbook of Medical Imaging: Processing and Analysis. San Diego: Academic Press, 2000:359-374 | eng |
| dcterms.references | Rangayyan, R. Biomedical Image Analysis. CRC Press, 2004 | eng |
| dcterms.references | González, R., Woods, R. Digital Image Processing. México: Prentice Hall, 2008 | eng |
| dcterms.references | Okada, T., Shimada, R., Hori, M., Nakamoto, M., Chen, Y., Nakamu- ra, H., Sato, Y. Automated segmentation of the liver from 3-D CT im- ages using probabilistic atlas and multilevel statistical shape model. Academic Radiology. 15(11): 1390-1403, 2008 | eng |
| dcterms.references | Massoptier, L., Casciaro, S. A new fully automatic and robust algo- rithm for fast segmentation of liver tissue and tumors from CT scans. Eur. Radiol. 18(8): 1658-1665, 2008 | eng |
| dcterms.references | Linguraru, G., Richbourg, W., Liu, J., Watt, J., Pamulapati, V. Wang, S., Summers, R. Tumor burden analysis on computed tomography by automated liver and tumor segmentation. IEEE Transactions on Medical Imaging. 31(10): 1965-1976, 2012 | eng |
| dcterms.references | Pauwels, E., Frederix, G. Finding salient regions in images: Non– parametric clustering for image segmentation and grouping. Com- puter Vision and Image Understanding. 1999; 18(1-2): 73-85, spe- cial issue | eng |
| dcterms.references | Yin, L., Yang, R., Gabbouj, M., Neuvo, Y. Weighted median flters: a tutorial. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing. 1996; 43(3): 157-192 | eng |
| dcterms.references | Sapiro, G. Geometric partial differential equations and image analy- sis. UK: Cambridge University Press, 2001 | eng |
| dcterms.references | Vera, M., Martinez, L., Huérfano, Y., Molina, V., Vargas, S., Vera, M.I, Salazar, W., Rodríguez, J., Rodríguez, R., Chacón, G., Isaza, A., Saenz, F., Gelvez, E., Salazar, J., Automatic segmentation of subdural hematomas using a computational technique based on smart opera- tors. Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE), Porto: IEEE. 2018 Avail- able from: https://www.researchgate.net/publication/326151751_Au- tomatic_segmentation_of_subdural_hematomas_using_a_computa- tional_technique_based_on_smart_operators | eng |
| dcterms.references | Suykens, J., Van Gestel, T., De Brabanter, J., De Moor, B., Vande- walle. J. Least squares support vector machines. UK: World Scien- tifc Publishing Co, 2002 | eng |
| dcterms.references | Zhu, H., Rohwer, R. No free lunch for crossvalidation. Neural Com- putation, 8(7):1421-1426, 1996 | eng |
| dcterms.references | Dice, L. Measures of the amount of ecologic association between species. Ecology. 26: 297-302, 1945 | eng |
| dcterms.references | Pratt, W. Digital Image Processing. USA: John Wiley & Sons Inc, 2007 | eng |
| dcterms.references | Fischer, M., Paredes, J., Arce, G. Image sharpeners using permuta- tion weighted median flters, en EUROSIP, Tampere, Finland, 2010: 299–303 | eng |
| oaire.version | info:eu-repo/semantics/publishedVersion | eng |