Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/309449
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dc.coverage.spatialComputer science
dc.date.accessioned2020-12-18T12:06:37Z-
dc.date.available2020-12-18T12:06:37Z-
dc.identifier.urihttp://hdl.handle.net/10603/309449-
dc.description.abstractAccuracy of segmentation methods is a great importance in brain image analysis. Tissue classification in Magnetic Resonance brain images (MRI) is an important issue in the analysis of several brain dementias. newlineThis work describes the preprocessing techniques towards brain MR Image segmentation. Preprocessing is an important step in enabling accurate measurement of brain structures. Due to large amount of noise and non-brain region, the accuracy cannot be correctly obtained. Hence the preprocessing techniques are used, the image intensities are firstly standardized using the pixel histograms and Morphological operations are applied to eliminate the non- brain regions or tissue, skull, scalp, dura from brain newlineA large variety of algorithms for segmentation of Brain MRI had been developed. This work aims at to study the performance of segmentation process on MR images of the human brain, using Fuzzy c-means (FCM), Kernel based Fuzzy c-means clustering (KFCM), Spatial Fuzzy c-means (SFCM) and Improved Fuzzy c-means (IFCM). The work covers imaging modalities, MRI and methods for noise reduction and segmentation approaches. All methods are applied on MRI brain images which are degraded by salt-pepper noise demonstrate that the IFCM algorithm performs more robust to noise than the standard FCM algorithm. newlineSegmentation of brain MRIs is a crucial step for many applications in both clinical and neuroscience. It is made difficult by the artifacts inherent in this type of image, their low contrast and the large individual variations that limit the introduction of a priori knowledge. newlineIn preclinical studies of magnetic resonance imaging (MRI) in analysis of several brain dementias, the use of tissue segmentation for its subsequent analysis and / or recording with other imaging modalities is common. This process is usually performed manually, so a large amount of time is often used depending on the study. This research work proposes a method of segmentation on MRI images of the human brain using Fuzzy c-means (FCM). A PSO (Particle
dc.format.extent130p
dc.languageEnglish
dc.relation158b
dc.rightsuniversity
dc.titleStudies on The Segmentation of Brain Tissue and Tumor From 3d Magnetic Resonance Imaging
dc.title.alternative
dc.creator.researcherSuryawanshi Ujwala Vishwanathrao
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Artificial Intelligence
dc.subject.keywordEngineering and Technology
dc.description.noteBibliography
dc.contributor.guideKulkarni U V
dc.publisher.placeNanded
dc.publisher.universitySwami Ramanand Teerth Marathwada University
dc.publisher.institutionDepartment of Computer Science
dc.date.registered2008
dc.date.completed2019
dc.date.awarded2020
dc.format.dimensions
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Department of Computer Science

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01_title.pdfAttached File107.8 kBAdobe PDFView/Open
02 certificate.pdf195.8 kBAdobe PDFView/Open
03_abstract.pdf7.03 kBAdobe PDFView/Open
04_declearation.pdf118.25 kBAdobe PDFView/Open
05_acknowledgement.pdf13.47 kBAdobe PDFView/Open
06_contents.pdf103.15 kBAdobe PDFView/Open
07_list_of_tables.pdf5.36 kBAdobe PDFView/Open
08_list_of_figures.pdf107.04 kBAdobe PDFView/Open
09_chapter 1.pdf720.4 kBAdobe PDFView/Open
10_chapter 2.pdf428.56 kBAdobe PDFView/Open
11_chapter 3.pdf864.69 kBAdobe PDFView/Open
12_chapter 4.pdf1.3 MBAdobe PDFView/Open
13_chapter 5.pdf869.24 kBAdobe PDFView/Open
14_conclusion.pdf237.12 kBAdobe PDFView/Open
15_bibliography.pdf531.3 kBAdobe PDFView/Open
80_recommendation.pdf338.45 kBAdobe PDFView/Open


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