Matches in SemOpenAlex for { <https://semopenalex.org/work/W2468126466> ?p ?o ?g. }
Showing items 1 to 76 of
76
with 100 items per page.
- W2468126466 abstract "Image segmentation is good way to analyze information in various fields of life. Image processing, especially image segmentation is very important and beneficial especially for the medical image segmentation and many other fields, from the application of segmentation know how the segmentation is important in our life. Image segmentation is the process of partitioning a digital image into sets of pixels. The aim of recognition system is to automatically identify the brain and extract the tumor from it. Several approaches have been proposed for medical segmentation. Some of the methods use color and brightness to reduce the complexity of the problem. Although such approaches solve the detect edges of regions. They are not able to handle almost any variation on the brain physical structure. There are many techniques have been proposed for tumor brain segmentation and Hill climbing is one of these techniques. The combination of the different approaches for the segmentation of brain images is presented in this project. Propose a color-based segmentation method that uses the K-means clustering technique with Hill climbing method to track tumor objects in magnetic resonance (MR) brain images, K-means clustering is used to cluster the image from gray to RGB scale while Hill climbing has been applied for the segmentation after that to overcome the problem with empty holes and Incoherent borders in the image , this project can successfully achieve segmentation for MR brain images to help pathologists distinguish exactly lesion size and region." @default.
- W2468126466 created "2016-07-22" @default.
- W2468126466 creator A5080516591 @default.
- W2468126466 date "2010-04-01" @default.
- W2468126466 modified "2023-09-23" @default.
- W2468126466 title "Tumor detection based on enhanced hill climbing method" @default.
- W2468126466 cites W1510355813 @default.
- W2468126466 cites W2009561775 @default.
- W2468126466 cites W2019027165 @default.
- W2468126466 cites W2050132071 @default.
- W2468126466 cites W2096579040 @default.
- W2468126466 cites W2099505818 @default.
- W2468126466 cites W2104839952 @default.
- W2468126466 cites W2116087808 @default.
- W2468126466 cites W2116444896 @default.
- W2468126466 cites W2133003941 @default.
- W2468126466 cites W2163255932 @default.
- W2468126466 cites W2229412420 @default.
- W2468126466 cites W1539360628 @default.
- W2468126466 hasPublicationYear "2010" @default.
- W2468126466 type Work @default.
- W2468126466 sameAs 2468126466 @default.
- W2468126466 citedByCount "0" @default.
- W2468126466 crossrefType "dissertation" @default.
- W2468126466 hasAuthorship W2468126466A5080516591 @default.
- W2468126466 hasConcept C124504099 @default.
- W2468126466 hasConcept C153180895 @default.
- W2468126466 hasConcept C154945302 @default.
- W2468126466 hasConcept C160633673 @default.
- W2468126466 hasConcept C206824153 @default.
- W2468126466 hasConcept C25694479 @default.
- W2468126466 hasConcept C31972630 @default.
- W2468126466 hasConcept C41008148 @default.
- W2468126466 hasConcept C42314347 @default.
- W2468126466 hasConcept C65885262 @default.
- W2468126466 hasConcept C73555534 @default.
- W2468126466 hasConcept C89600930 @default.
- W2468126466 hasConceptScore W2468126466C124504099 @default.
- W2468126466 hasConceptScore W2468126466C153180895 @default.
- W2468126466 hasConceptScore W2468126466C154945302 @default.
- W2468126466 hasConceptScore W2468126466C160633673 @default.
- W2468126466 hasConceptScore W2468126466C206824153 @default.
- W2468126466 hasConceptScore W2468126466C25694479 @default.
- W2468126466 hasConceptScore W2468126466C31972630 @default.
- W2468126466 hasConceptScore W2468126466C41008148 @default.
- W2468126466 hasConceptScore W2468126466C42314347 @default.
- W2468126466 hasConceptScore W2468126466C65885262 @default.
- W2468126466 hasConceptScore W2468126466C73555534 @default.
- W2468126466 hasConceptScore W2468126466C89600930 @default.
- W2468126466 hasLocation W24681264661 @default.
- W2468126466 hasOpenAccess W2468126466 @default.
- W2468126466 hasPrimaryLocation W24681264661 @default.
- W2468126466 hasRelatedWork W2016293927 @default.
- W2468126466 hasRelatedWork W2104174412 @default.
- W2468126466 hasRelatedWork W2164605742 @default.
- W2468126466 hasRelatedWork W2184291480 @default.
- W2468126466 hasRelatedWork W2185116456 @default.
- W2468126466 hasRelatedWork W2325462231 @default.
- W2468126466 hasRelatedWork W2377187814 @default.
- W2468126466 hasRelatedWork W2552184260 @default.
- W2468126466 hasRelatedWork W2732910062 @default.
- W2468126466 hasRelatedWork W2780202401 @default.
- W2468126466 hasRelatedWork W2780337677 @default.
- W2468126466 hasRelatedWork W2888347706 @default.
- W2468126466 hasRelatedWork W2988907687 @default.
- W2468126466 hasRelatedWork W2992675616 @default.
- W2468126466 hasRelatedWork W2998139471 @default.
- W2468126466 hasRelatedWork W3158608834 @default.
- W2468126466 hasRelatedWork W2182035896 @default.
- W2468126466 hasRelatedWork W2182542920 @default.
- W2468126466 hasRelatedWork W2619283684 @default.
- W2468126466 hasRelatedWork W2750540800 @default.
- W2468126466 isParatext "false" @default.
- W2468126466 isRetracted "false" @default.
- W2468126466 magId "2468126466" @default.
- W2468126466 workType "dissertation" @default.