Automatic selection of localized region-based active contour models using image content analysis applied to brain tumor segmentation
| Autor: | Claire Chalopin, Elisee Ilunga-Mbuyamba, Mario Alberto Ibarra-Manzano, Juan Gabriel Avina-Cervantes, Jonathan Cepeda-Negrete |
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| Rok vydání: | 2017 |
| Předmět: |
Computer science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Scale-space segmentation Health Informatics 02 engineering and technology 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Image Interpretation Computer-Assisted 0202 electrical engineering electronic engineering information engineering Humans Computer vision Selection (genetic algorithm) Active contour model business.industry Segmentation-based object categorization Brain Neoplasms Process (computing) Brain Pattern recognition Image segmentation Distribution fitting Magnetic Resonance Imaging Computer Science Applications Region growing 020201 artificial intelligence & image processing Artificial intelligence business Algorithms |
| Zdroj: | Computers in biology and medicine. 91 |
| ISSN: | 1879-0534 |
| Popis: | Brain tumor segmentation is a routine process in a clinical setting and provides useful information for diagnosis and treatment planning. Manual segmentation, performed by physicians or radiologists, is a time-consuming task due to the large quantity of medical data generated presently. Hence, automatic segmentation methods are needed, and several approaches have been introduced in recent years including the Localized Region-based Active Contour Model (LRACM). There are many popular LRACM, but each of them presents strong and weak points. In this paper, the automatic selection of LRACM based on image content and its application on brain tumor segmentation is presented. Thereby, a framework to select one of three LRACM, i.e., Local Gaussian Distribution Fitting (LGDF), localized Chan-Vese (C-V) and Localized Active Contour Model with Background Intensity Compensation (LACM-BIC), is proposed. Twelve visual features are extracted to properly select the method that may process a given input image. The system is based on a supervised approach. Applied specifically to Magnetic Resonance Imaging (MRI) images, the experiments showed that the proposed system is able to correctly select the suitable LRACM to handle a specific image. Consequently, the selection framework achieves better accuracy performance than the three LRACM separately. |
| Databáze: | OpenAIRE |
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