Fully automatic multisegmentation approach for magnetic resonance imaging brain tumor detection using improved region‐growing and quasi‐Monte Carlo‐expectation maximization algorithm. Issue 1 (6th November 2019)

• Record Type: Journal Article
• Authors: Hachemi, Belkacem; Chama, Zouaoui; Alim‐Ferhat, Fatiha; Lamini, El‐Sedik; Abderrahmane, Abdelkader; Anani, Macho; Choquet, Catherine
• Is Part Of: International journal of imaging systems and technology. Volume 30:Issue 1(2020)
• Page Start: 104
• Page End: 111
• Publication Date: 2019-11-06
• Subjects: brain tumor -- expectation maximization -- multisegmentation -- quasi‐Monte Carlo -- region growing; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22376
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: Magnetic resonance imaging (MRI) is widely used in the medical field, especially for detecting serious abnormalities affecting the organs of the human body, such as tumors. Automatic detection of tumors needs high‐performance recognition techniques. In this paper, we have developed a new automatic method based on the multisegmentation of brain tumor region. We used an improved region‐growing algorithm, which is based on quasi‐Monte Carlo and expectation maximization methods to define the desired classes. Several metrics were calculated to evaluate the performance of our technique. The fully automatic multisegmentation approach, developed in this study, showed good performance, and it can offer a new option to replace conventional techniques used for tumor detection in MRI images.