Magnetic resonance brain images algorithm to identify demyelinating and ischemic diseases
| العنوان: | Magnetic resonance brain images algorithm to identify demyelinating and ischemic diseases |
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| المؤلفون: | Darwin Castillo, María José Rodríguez-Álvarez, Llanos Cuenca, René Samaniego, Yuliana Jiménez, Oscar Vivanco |
| المصدر: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia instname |
| بيانات النشر: | The International Society for Optical Engineering., 2018. |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | Encephalomyelitis, Demyelinating, Ischemia, 02 engineering and technology, Fluid-attenuated inversion recovery, Brain ischemia, 03 medical and health sciences, Myelin, 0302 clinical medicine, Image processing, 0202 electrical engineering, electronic engineering, information engineering, medicine, Brain disease, medicine.diagnostic_test, business.industry, Multiple sclerosis, Leukodystrophy, Magnetic resonance imaging, medicine.disease, medicine.anatomical_structure, 020201 artificial intelligence & image processing, business, MATEMATICA APLICADA, Algorithm, 030217 neurology & neurosurgery, MRI |
| الوصف: | [EN] Brain demyelination lesions occur due to damage of the myelin layer of nerve fibers, this deterioration is the cause of pathologies such as multiple sclerosis, leukodystrophy, encephalomyelitis. Brain ischemia is the interruption of the blood supply to the brain, and the flow of oxygen and nutrients needed to maintain the correct functioning of brain cells. This project presents the results of an algorithm processing images with the the main objective of identify and differentiate between demyelination and ischemic brain diseases through the automatic detection, classification and identification of their features found in the magnetic resonance images. The sequences of images used were T1, T2, and FLAIR and with a dataset of 300 patients with and without these or other pathologies, respectively. The algorithm in this stage uses Discrete Wavelet Transform (DWT), principal component analysis (PCA) and a kernel support vector machine (SVM). The algorithm developed indicates a 75% of accuracy, for that reason, with an effective validation could be applied for the fast diagnosis and contribute to an effective treatment of these brain diseases especially in the rural places. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| DOI: | 10.1117/12.2322048 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::36806e44d5bb8e75dd2c3588d166e9f5 https://doi.org/10.1117/12.2322048 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....36806e44d5bb8e75dd2c3588d166e9f5 |
| قاعدة البيانات: | OpenAIRE |
| DOI: | 10.1117/12.2322048 |
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