Automatic tumor lesion detection and segmentation using histogram-based gravitational optimization algorithm
Nooshin Nabizadeh, Mohsen Dorodchi · 2014
In this paper, an automated and customized brain tumor segmentation method is presented and validated against ground truth applying simulated T1-weighted magnetic resonance images in 25 subjects. A new intensity-based segmentation technique called histogram based gravitational optimization algorithm is developed to segment the brain image into discriminative sections (segments) with high accuracy. While the mathematical foundation of this algorithm is presented in details, the application of the proposed algorithm in the segmentation of single T1-weighted images (T1-w) modality of healthy and lesion MR images is also presented. The results show that the tumor lesion is segmented from the detected lesion slice with 89.6% accuracy.