What has happened in machine learning lately, and what does it mean for the future of
medical image analysis? Machine learning has witnessed a tremendous amount of attention …

Despite the remarkable performance of deep learning methods on various tasks, most
cutting-edge models rely heavily on large-scale annotated training examples, which are …

Deep learning has an enormous impact on medical image analysis. Many computer-aided
diagnostic systems equipped with deep networks are rapidly reducing human intervention in …

The fuzzy C-means (FCM) clustering procedure is an unsupervised form of grouping the
homogenous pixels of an image in the feature space into clusters. A brain magnetic …

Multi-level thresholding of a gray image is one of the basic operations in computer vision,
with applications in image enhancement and segmentation. Various criteria for the selection …

This paper proposes a fully automatic method to segment the inner boundary of the bony
orbit in two different image modalities: magnetic resonance imaging (MRI) and computed …

The accuracy of the magnetic resonance (MR) image diagnosis depends on the quality of
the image, which degrades mainly due to noise and artifacts. The noise is introduced …

The segmentation of brain tissue in MRI is valuable for extracting brain structure to aid
diagnosis, treatment and tracking the progression of different neurologic diseases. Medical …

Detection of sickle cell disease is a crucial job in medical image analysis. It emphasizes
elaborate analysis of proper disease diagnosis after accurate detection followed by a …

Abstract Fuzzy C-Means (FCM) plays a major role in brain tissue segmentation. The
proposed method aims to implements rapid brain tissue segmentation from MRI human …