Because of its high clinical significance and varied modalities; magnetic resonance (MR)
imaging procedures are widely adopted in medical discipline to record the abnormalities …

There are various distributions of image histograms where regions form symmetrically or
asymmetrically based on the frequency of the intensity levels inside the image. In pure …

Background Brain tumor extraction from magnetic resonance (MR) images is challenging
due to variations in the location, shape, size and intensity of tumors. Manual delineation of …

The manuscript authenticates the effectiveness of fusing texture and geometrical (GEO)
features in magnetic resonance imaging (MRI) for tumor classification. The presented …

Magnetic resonance imaging segmentation refers to a process of assigning labels to set of
pixels or multiple regions. It plays a major role in the field of biomedical applications as it is …

Automation in detecting and segmenting brain tumor is the need of the era in order to
diagnose human brain magnetic resonance images (MRIs) and required for better treatment …

The primary brain image segmentation goal is to partition a given brain image into different
regions representing anatomical structures. Magnetic resonance image (MRI) segmentation …

BACKGROUND: Brain tumor segmentation is a challenging task for its variation in intensity.
The phenomenon is caused by the inhomogeneous content of tumor tissue and the choice …

Automatic image segmentation becomes very crucial for tumor detection in medical image
processing. In general, manual and semi automatic segmentation techniques require more …

The diffusion‐weighted (DW) MR signal sampled over a wide range of b‐values potentially
allows for tissue differentiation in terms of cellularity, microstructure, perfusion, and T2 …