Brain tumor segmentation using DE embedded OTSU method and neural network

• Published in: Multidimensional systems and signal processing Vol. 30; no. 3; pp. 1263 - 1291
• Main Authors: Sharma, Anshika, Kumar, Sushil, Singh, Shailendra Narayan
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2019; Springer Nature B.V
• Subjects: Artificial Intelligence; Brain; Brain cancer; Circuits and Systems; Electrical Engineering; Engineering; Evolutionary algorithms; Evolutionary computation; Image classification; Image segmentation; Magnetic resonance imaging; Medical imaging; Medical research; Neural networks; Signal,Image and Speech Processing; Soft computing; Tumors; Magnetic resonance imaging (MRI); Brain tumor extraction; Feature extraction; Neural network; Segmentation; Differential Evolution
• ISSN: 0923-6082; 1573-0824
• DOI: 10.1007/s11045-018-0603-3

In the past few decades, medical imaging and soft computing have shown a symbolic growth in brain tumor segmentation. Research in medical imaging is becoming quite popular field, particularly in magnetic resonance images of brain tumor, because of the tremendous need of efficient and effective technique for evaluation of large amount of data. Image segmentation is considered as one of the most crucial techniques for visualizing tissues in human being. In considering brain tumor image segmentation, manually with an expert, it is more likely that the errors are present in it. To automate image segmentation, we have proposed an algorithm to obtain a global thresholding value for a particular image. To find out an optimal threshold value we have used Differential Evolution algorithm embedded with OTSU method and trained neural network for future use. Proposed Methodology provides classification of the images successfully for brain tumors. Results show its efficiency over other methods.