This paper presents an integrated method of the adaptive enhancement for an unsupervised
global-to-local segmentation of brain tissues in three-dimensional (3-D) MRI (Magnetic …

Gliomas are among the most common and aggressive brain tumours. Segmentation of these
tumours is important for surgery and treatment planning, but also for follow-up evaluations …

Early brain tumor detection and diagnosis are critical to clinics. Thus segmentation of
focused tumor area needs to be accurate, efficient, and robust. In this paper, we propose an …

Gliomas are the most common and aggressive primary brain tumours, with a short-life
expectancy in their highest grade. Magnetic Resonance Imaging is the most common …

This paper presents a probabilistic neural network based technique for unsupervised
quantification and segmentation of brain tissues from magnetic resonance images. It is …

Brain tumor segmentation in magnetic resonance imaging (MRI) is considered a complex
procedure because of the variability of tumor shapes and the complexity of determining the …

Purpose Brain tumor segmentation is a required step before any radiation treatment or
surgery. When performed manually, segmentation is time consuming and prone to human …

Purpose Gliomas are rapidly progressive, neurologically devastating, largely fatal brain
tumors. Magnetic resonance imaging (MRI) is a widely used technique employed in the …

Quantitative analysis of magnetic resonance imaging (MRI) scans of the brain requires
accurate automated segmentation of anatomical structures. A desirable feature for such …

Robust brain magnetic resonance (MR) segmentation algorithms are critical to analyze
tissues and diagnose tumor and edema in a quantitative way. In this study, we present a …