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Brain Tumor Detection Using Ripplet and Support Vector Machine (SVM)
| Content Provider | Semantic Scholar |
|---|---|
| Author | Moradjou, Hamed Bekravi, Masoud |
| Copyright Year | 2018 |
| Abstract | The main objective of the method is to automatically segment and detect brain tumor using Ripplet and Support Vector Machine. An automatic segmentation of brain images is needed to correctly segment tumor from other brain tissues. Accurate detection of size and location of brain tumor plays a vital role in the diagnosis of tumor. This method propose an classification and the efficiency in representing edges and textures brain image for tumor detection which utilizes the complementary and redundant information from the Computed Tomography (CT) image and Magnetic Resonance Imaging (MRI) images. The reason for going onto image classification is that, in the medical image processing, different sources of images produce complementary information and so one has to fuse all the sources of images to get more details required for the diagnosis of the patients. Hence this ripplet uses the information provided by the CT image and MRI images there by providing a resultant fused image which increases the efficiency of tumor detection. Segmentation of the fused image is performed using thresholding. Feed Forward support vector machine is used to automatically detect brain tumor from segmented brain image. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.ijocit.org/journal/v05_i01/IJOCIT-V05I01P08.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
