Brain hematoma computational segmentation
| Autor: | Luis Javier Martínez, Valentin Molina, Juan Salazar, Williams Salazar, Miguel Vera, J. Arango, Horderlin Vrangel Robles, Frank Sáenz, O Valbuena, Yoleidy Huérfano, Elkin Gelvez, M. Bautista, María Vera |
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| Rok vydání: | 2018 |
| Předmět: |
History
medicine.medical_specialty Brain hematoma business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 030218 nuclear medicine & medical imaging Computer Science Applications Education 03 medical and health sciences 0302 clinical medicine 030220 oncology & carcinogenesis medicine Segmentation Radiology business |
| Zdroj: | Journal of Physics: Conference Series Vol. 1126, No. 012071 (2018) doi :10.1088/1742-6596/1126/1/012071 Repositorio Digital USB Universidad Simón Bolívar instacron:Universidad Simón Bolívar |
| ISSN: | 1742-6596 1742-6588 |
| DOI: | 10.1088/1742-6596/1126/1/012071 |
| Popis: | In computed tomography imaging, brain hematoma (BH) segmentation is a very challenging process due to a high variability of BH morphology, low contrast and noisy images. Because of this, BH segmentation is an open problem. In order to approach this problem, we propose an automatic technique, named nonlinear technique (NLT), based on a thresholding method, noise suppression filters, intelligent operators, a clustering strategy and a binary morphological operator. NLT performance is assessed by Jaccard's similarity index (JSI) used to compare automatic and manual BH segmentations. This assessment allows developing a tuning process for establishing the optimal parameters of each of the algorithms which constitute the proposed technique. The results indicate a good correlation, based on JSI, between the manual segmentations and the automatic ones. Finally, the BH volume is generated considering the automatic segmentation. This volume indicates whether or not the patient must undergo a surgical intervention for BH treatment. |
| Databáze: | OpenAIRE |
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