Glioblastoma multiforme identification from medical imaging using computer vision

• Main Authors: Mohd. Azhari, Ed-Edily, Mohd. Hatta, Muhd. Mudzakkir, Htike@Muhammad Yusof, Zaw Zaw, Shoon , Lei Win
• Format: Article
• Language: English
• Published: Wireilla Scientific Publications, Australia 2014
• Subjects: Q Science (General)
• Online Access: http://irep.iium.edu.my/38134/1/3214ijscmc01.pdf; http://irep.iium.edu.my/38134/; http://wireilla.com/ns/maths/current2014.html

A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the surrounding tissue by its structure. A tumor may lead to cancer, which is a major leading cause of death and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in the world. Great knowledge and experience on radiology are required for accurate tumor detection in medical imaging. Automation of tumor detection is required because there might be a shortage of skilled radiologists at a time of great need. We propose an automatic brain tumor detection and localization framework that can detect and localize brain tumor in magnetic resonance imaging. The proposed brain tumor detection and localization framework comprises five steps: image acquisition, pre-processing, edge detection, modified histogram clustering and morphological operations. After morphological operations, tumors appear as pure white color on pure black backgrounds. We used 50 neuroimages to optimize our system and 100 out-of-sample neuroimages to test our system. The proposed tumor detection and localization system was found to be able to accurately detect and localize brain tumor in magnetic resonance imaging. The preliminary results demonstrate how a simple machine learning classifier with a set of simple image-based features can result in high classification accuracy. The preliminary results also demonstrate the efficacy and efficiency of our five-step brain tumor detection and localization approach and motivate us to extend this framework to detect and localize a variety of other types of tumors in other types of medical imagery.