Automatic tumor segmentation in single-spectral MRI using a texture-based and contour-based algorithm
| Autor: | Nooshin Nabizadeh, Miroslav Kubat |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
medicine.diagnostic_test
Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION General Engineering Magnetic resonance imaging 02 engineering and technology 030218 nuclear medicine & medical imaging Computer Science Applications 03 medical and health sciences 0302 clinical medicine Artificial Intelligence 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Algorithm Classifier (UML) Tumor segmentation |
| Zdroj: | Expert Systems with Applications. 77:1-10 |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2017.01.036 |
| Popis: | We propose a new fully automatic method to detect and segment brain lesions.The method is based on a texture-based and a contour-based algorithm.The algorithm is independent of multi-spectral MRI, and local or global registration. Automatic detection of brain tumors in single-spectral magnetic resonance images is a challenging task. Existing techniques suffer from inadequate performance, dependence on initial assumptions, and, sometimes, the need for manual interference. The research reported in this paper seeks to reduce some of these shortcomings, and to remove others, achieving satisfactory performance at reasonable computational costs. The success of the system described here is explained by the synergy of the following aspects: (1) a broad choice of high-level features to characterize the images texture, (2) an efficient mechanism to eliminate less useful features (3) a machine-learning technique to induce a classifier that signals the presence of a tumor-affected tissue, and (4) an improved version of the skippy greedy snake algorithm to outline the tumors contours. The paper describes the system and reports experiments with synthetic as well as real data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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