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Detection and Localization of Early-Stage Multiple Brain Tumors Using a Hybrid Technique of Patch-Based Processing, k-means Clustering and Object Counting
Joint Authors
Source
International Journal of Biomedical Imaging
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-01-06
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Brain tumors are a major health problem that affect the lives of many people.
These tumors are classified as benign or cancerous.
The latter can be fatal if not properly diagnosed and treated.
Therefore, the diagnosis of brain tumors at the early stages of their development can significantly improve the chances of patient’s full recovery after treatment.
In addition to laboratory analyses, clinicians and surgeons extract information from medical images, recorded by various systems such as magnetic resonance imaging (MRI), X-ray, and computed tomography (CT).
The extracted information is used to identify the essential characteristics of brain tumors (location, size, and type) in order to achieve an accurate diagnosis to determine the most appropriate treatment protocol.
In this paper, we present an automated machine vision technique for the detection and localization of brain tumors in MRI images at their very early stages using a combination of k-means clustering, patch-based image processing, object counting, and tumor evaluation.
The technique was tested on twenty real MRI images and was found to be capable of detecting multiple tumors in MRI images regardless of their intensity level variations, size, and location including those with very small sizes.
In addition to its use for diagnosis, the technique can be integrated into automated treatment instruments and robotic surgery systems.
American Psychological Association (APA)
Nasor, Mohamed& Obaid, Walid. 2020. Detection and Localization of Early-Stage Multiple Brain Tumors Using a Hybrid Technique of Patch-Based Processing, k-means Clustering and Object Counting. International Journal of Biomedical Imaging،Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1169168
Modern Language Association (MLA)
Nasor, Mohamed& Obaid, Walid. Detection and Localization of Early-Stage Multiple Brain Tumors Using a Hybrid Technique of Patch-Based Processing, k-means Clustering and Object Counting. International Journal of Biomedical Imaging No. 2020 (2020), pp.1-9.
https://search.emarefa.net/detail/BIM-1169168
American Medical Association (AMA)
Nasor, Mohamed& Obaid, Walid. Detection and Localization of Early-Stage Multiple Brain Tumors Using a Hybrid Technique of Patch-Based Processing, k-means Clustering and Object Counting. International Journal of Biomedical Imaging. 2020. Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1169168
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1169168