“Brain Image Segmentation using Expectation Maximization and K-Nearest Neighbor Algorithms”

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DC Field	Value	Language
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.identifier.citation	Master of Science in Information and Communication Engineering	en_US
dc.identifier.uri	https://elibrary.tucl.edu.np/handle/123456789/8960	-
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.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.institute.title	Institute of Engineering	en_US
local.academic.level	Masters	en_US
local.affiliatedinstitute.title	Pulchowk Campus	en_US
Appears in Collections:	Electronics and Computer Engineering

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