Simple Summary Segmentation of brain tumor images from magnetic resonance imaging
(MRI) is a challenging topic in medical image analysis. The brain tumor can take many …

Deep learning techniques have been widely adopted for learning task-adaptive features in
image segmentation applications, such as brain tumor segmentation. However, most of …

Segmenting and quantifying gliomas from MRI is an important task for diagnosis, planning
intervention, and for tracking tumor changes over time. However, this task is complicated by …

Image processing plays a major role in neurologists' clinical diagnosis in the medical field.
Several types of imagery are used for diagnostics, tumor segmentation, and classification …

We present a new method for automatic segmentation of heterogeneous image data that
takes a step toward bridging the gap between bottom-up affinity-based segmentation …

Among brain tumors, gliomas are the most common and aggressive, leading to a very short
life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the …

Abstract Brain lesions, especially White Matter Lesions (WMLs), are associated with cardiac
and vascular disease, but also with normal aging. Quantitative analysis of WML in large …

The objective of this experimentation is to develop an interactive CAD system for assisting
radiologists in multiclass brain tumor classification. The study is performed on a diversified …

This paper proposes a strategy where a structure is developed to recognize and order the
tumor type. Over a time of years, numerous specialists have been examined and proposed a …

Image segmentation refers to the process of partitioning an image into mutually exclusive
regions. It can be considered as the most essential and crucial process for facilitating the …