Segmentation of grayscale medical images is challenging because of the similarity of pixel
intensities and poor gradient strength between adjacent regions. The existing image …

Background To clarify the nature of cervical malignant lymphadenopathy is highly important
for the diagnosis and differential diagnosis of head and neck tumors. Purpose To investigate …

Background: After the treatment of the patients with malignant lymphoma, there may persist
lesions that must be labeled either as evolutive lymphoma requiring new treatments or as …

This paper aimed to discuss the denoising ability of magnetic resonance imaging (MRI)
images based on fuzzy C‐means clustering (FCM) algorithm and the influence of …

Simple Summary There is growing interest in applying quantitative diffusion techniques to
magnetic resonance imaging for cancer diagnosis and treatment. These measurements are …

Clustering is an important issue in brain medical image segmentation. Original medical
images used for clinical diagnosis are often insufficient for clustering in the current domain …

The k-means algorithm is a popular clustering method for image segmentation. However,
the main disadvantage of this algorithm is its dependence on the number of initial clusters. In …

In the field of brain tumor image analysis, automatic brain tumor image segmentation
remains a challenging task due to the varying sizes, shapes, and textures of type of these …

Objective The objective of this study was to determine the diagnostic value of quantitative
border irregularity assessment and apparent diffusion coefficient (ADC) in patients with …

Abstract Background and Aim: The aim of this study was to evaluate and analyze the
different Apparent Diffusion Coefficient (ADC) values of components of heterogeneous …