Statistical features segmentation technique for MR images of brain’s tumors
Other Title(s)
تقنية الانقسام باستخدام الخصائص الإحصائية لصور الرنين المغناطيسي لأورام الدماغ
Joint Authors
Ali, Salih Mahdi
Mahmud, Falih Hasan
Source
Issue
Vol. 53, Issue 4 (sup) (31 Dec. 2012), pp.1148-1155, 8 p.
Publisher
University of Baghdad College of Science
Publication Date
2012-12-31
Country of Publication
Iraq
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Abstract EN
Medical image analysis has great significance in the field of treatment, especially in non-invasive and clinical studies.
Medical imaging techniques and it analysis and diagnoses analysis tools enable the physicians and Radiologists to reach at a specific diagnosis.
In this study, MR images have been used for discriminating the infected tissues from normal brain’s tissues.
A semi-automatic segmentation technique based on statistical futures has been introduced to segment the brain’s MR image tissues.
The proposed system used two stages for extracting the image texture features.
The first stage is based on utilizing the 1st order statistical futures histogram based features such as (the mean, standard deviation, and image entropy ) which is local in nature, while the second stage is based on utilizing the 2nd order statistical futures (i.e Co-Occurrence matrices features).Similar coloring and semi-equal statistical features of the tumor area and the Gray Matter (GM) brain’s tissue was the main encountered problem in the first presented segmentation method.
To overcome this problem, an adaptive multi-stage segmentation technique is presented, in which the mean value of each pre-segmented classes has been used to distinguish the tumor tissue from others.
The segmentation process is followed by a 2nd order classification method to assign image pixels accurately to their regions, using the invariant moments parameters weighted together with the Co-Occurrence parameters.
Different samples of MR images for normal and abnormal brains (i.e.
T1 and T2-weighted) have been tested, for different patients.
American Psychological Association (APA)
Ali, Salih Mahdi& Mahmud, Falih Hasan. 2012. Statistical features segmentation technique for MR images of brain’s tumors. Iraqi Journal of Science،Vol. 53, no. 4 (sup), pp.1148-1155.
https://search.emarefa.net/detail/BIM-955592
Modern Language Association (MLA)
Ali, Salih Mahdi& Mahmud, Falih Hasan. Statistical features segmentation technique for MR images of brain’s tumors. Iraqi Journal of Science Vol. 53, no. 4 (Supplement) (Dec. 2012), pp.1148-1155.
https://search.emarefa.net/detail/BIM-955592
American Medical Association (AMA)
Ali, Salih Mahdi& Mahmud, Falih Hasan. Statistical features segmentation technique for MR images of brain’s tumors. Iraqi Journal of Science. 2012. Vol. 53, no. 4 (sup), pp.1148-1155.
https://search.emarefa.net/detail/BIM-955592
Data Type
Journal Articles
Language
English
Notes
Text in English ; abstracts in English and Arabic.
Record ID
BIM-955592