| Autor: |
Hamzah, Ayad Muslim, Mhaidi, Nameer Falih |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2/27/2023, Vol. 2776 Issue 1, p1-9, 9p |
| Abstrakt: |
Medical image processing techniques are essential in detecting diseases at present. Usually, MRI images detect the presence and type of disease. The brain comprises a group of nerve cells and supportive tissues. Brain tumors are one of the most dangerous types of diseases because of the high mortality rates they cause worldwide. The detection of the tumor may be a fundamental reason to save the patient's life. The mortality rate was very high before early diagnosis began. The mortality rate was very high before early diagnosis began. After starting early diagnosis, the death rate was noticeably reduced due to the accurate identification of brain tumors in the early stages. Currently, researchers have stepped up their efforts by using computer programs to help clinicians in the early detection and classification of brain tumors. In this research, an algorithm was presented to overcome the problems of detecting and diagnosing brain tumors, consisting of several stages: The first stage is to get the real data and then make improvements using the Wiener filter. Then the tumor is determined by the threshold, and then the image is divided into more than one section. Thus, the image is entered on the morphological process to enhance the segmentation result by remove distortion and to filter out smaller regions. The next step is watershed segmentation used to separate different region according to intensity value which leads to diagnoses the tumor. Finally feature extraction is used to obtain important information about the image and then make the optimal diagnosis of the brain tumor. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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