Detection of Brain Tumor from MR Images Based on Co-occurrence Matrix: Collabration Work between Ministry Of Education, Baghdad University and Al-Nahrain University. Baghdad Iraq. 2019

Khalaph, Kawther Ali/ Ali, Alyaa Hussein/ Nema, Ihssan S.

ISBN 10: 6200287848 / ISBN 13: 9786200287847

Published by LAP LAMBERT Academic Publishing, 2019

About this Item

108 pages. 8.66x5.91x0.25 inches. In Stock.

Seller Inventory # zk6200287848

Bibliographic Details

• Title: Detection of Brain Tumor from MR Images Based on Co-occurrence Matrix: Collabration Work between Ministry Of Education, Baghdad University and Al-Nahrain University. Baghdad Iraq. 2019
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2019
• Language: English
• ISBN 10: 6200287848
• ISBN 13: 9786200287847
• Binding: Paperback
• Condition: Brand New
• Weight: 0.21 kilograms

About this title

Synopsis

The Automatic analysis of Medical images using computer analysis diagnosis is one of the most interesting field in biomedical image processing. The proposed system gives techniques related to MRI analysis. A statistical structure analysis based on tumor segmentation scheme is presented, which focuses on the structural analysis in both normal and abnormal tissues,will help doctors to avoid the human error in manual interpretation of medical content. In this study, an enhanced thresholding algorithm is applied to extract the abnormal part from the 2D MRI. Samples of different ages and cases are taken from the AL-Imammain Al-Kadhimain Medical city and the Radiology Institute.Calculating the area of the abnormal tissue (tumor), the Wavelet transformation is then applied which is a signal estimation technique that exploits the capabilities to denoising the signal. A statistical feature has been obtained; then a hybrid method is applied in which k-mean clustering is a method of cluster analysis which aims to partitioned the images into clusters. Finally,an algorithm has been created to colored images depending on the boundary. This helps to separate the abnormal part into k clusters.