A Hybrid Enhanced Independent Component Analysis Approach for Segmentation of Brain Magnetic Resonance Image
| Autor: | MSatya Sai Ram, Shaik Basheera |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
medicine.diagnostic_test
Computer science business.industry Magnetic resonance imaging Pattern recognition Human brain Independent component analysis General Biochemistry Genetics and Molecular Biology Accurate segmentation 030218 nuclear medicine & medical imaging Image (mathematics) 03 medical and health sciences 0302 clinical medicine medicine.anatomical_structure medicine Medical imaging Brain magnetic resonance imaging Segmentation Artificial intelligence General Pharmacology Toxicology and Pharmaceutics General Agricultural and Biological Sciences business 030217 neurology & neurosurgery |
| Zdroj: | Defence Life Science Journal. 3:285 |
| ISSN: | 2456-0537 2456-379X |
| DOI: | 10.14429/dlsj.3.11499 |
| Popis: | Medical imaging and analysis plays a crucial role in diagnosis and treatment planning. The anatomical complexity of human brain makes the process of imaging and analyzing very difficult. In spite of huge advancements in medical imaging procedures, accurate segmentation and classification of brain abnormalities remains a challenging and daunting task. This challenge is more visible in the case of brain tumors because of different possible shapes of tumors, locations and image intensities of different types of tumors. In this paper we have presented a method for automated segmentation of brain tumors from magnetic resonance images. An enhanced and modified Gaussian mixture mode model and the independent component analysis segmentation approach has been employed for segmenting brain tumors in magnetic resonance images. The results of segmentation are validated with the help of segmentation evaluation parameters. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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