“Brain Image Segmentation using Expectation Maximization and K-Nearest Neighbor Algorithms”
| dc.contributor.author | SHRESTHA, BINOD CHANDRA | |
| dc.date.accessioned | 2022-03-11T06:34:54Z | |
| dc.date.available | 2022-03-11T06:34:54Z | |
| dc.date.issued | 2010-06 | |
| dc.description | Segmentation of an image entails the division or separation of the image into regions of similar attribute. | en_US |
| dc.description.abstract | Segmentation of an image entails the division or separation of the image into regions of similar attribute. Segmentation is one of the main problems in image analysis. Expectation Maximization (EM) and K-Nearest Neighbor (KNN) Algorithms have been used for segmentation of Magnetic Resonance (MR) image of brain. The main objective of this thesis is to segment and classify brain image into Gray Matter (GM), White Matter (WM) and Cerebral Spinal Fluid (CSF) using EM algorithm and KNN algorithm. | en_US |
| dc.identifier.citation | Master of Science in Information and Communication Engineering | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.14540/8960 | |
| dc.language.iso | en | en_US |
| dc.publisher | Pulchowk Campus | en_US |
| dc.subject | Expectation Maximization (EM) | en_US |
| dc.subject | K-Nearest Neighbor (KNN) | en_US |
| dc.title | “Brain Image Segmentation using Expectation Maximization and K-Nearest Neighbor Algorithms” | en_US |
| dc.type | Thesis | en_US |
| local.academic.level | Masters | en_US |
| local.affiliatedinstitute.title | Pulchowk Campus | en_US |
| local.institute.title | Institute of Engineering | en_US |