A space-occupying lesion automatic quantification from abdominal contrast-enhanced computerized tomography images

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.

• Autores: Bravo, Antonio José; Vera, Miguel Ángel; Huérfano, Yoleidy Katherine
• Fecha de publicación: 2020
• Institución: Universidad Simón Bolívar
• Repositorio: Repositorio Digital USB
• Idioma: eng
• OAI Identifier: oai:bonga.unisimon.edu.co:20.500.12442/6954
• Acceso en línea: https://hdl.handle.net/20.500.12442/6954; http://www.revistaavft.com/images/revistas/2020/avft_4_2020/20_a_space.pdf
• Palabra clave: Computerized tomography; Space-occupying lesion; imaging filters; clustering techniques; Dice coefficient; Tomografía computarizada; Filtrado de imágenes; Técnicas de agrupamiento; Coeficiente de Dice
• Rights: openAccess
• License: Attribution-NonCommercial-NoDerivatives 4.0 Internacional