The new era of artificial intelligence (AI) has introduced revolutionary data‑driven analysis
paradigms that have led to significant advancements in information processing techniques …

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 …

Purpose The study aimed to develop the open-source body and organ analysis (BOA), a
comprehensive computed tomography (CT) image segmentation algorithm with a focus on …

Objective This study aimed to evaluate a deep learning protocol to identify neoplasia in
intraductal papillary mucinous neoplasia (IPMN) in comparison to current radiographic …

Space travel causes rapid weight loss of astronauts, but the underlying reasons are still
obscure. Brown adipose tissue (BAT) is a well-known thermogenesis tissue that is …

Purpose Quantification of body tissue composition is important for research and clinical
purposes, given the association between the presence and severity of several disease …

Positron emission tomography (PET) and computed tomography (CT) are widely used
imaging modalities in radiation oncology. PET imaging has a high contrast but often leads to …

Purpose Radiological imaging and image interpretation for clinical decision making are
mostly specific to each body region such as head and neck, thorax, abdomen, pelvis, and …

Magnetic resonance imaging (MRI) is the non-invasive modality of choice for body tissue
composition analysis due to its excellent soft-tissue contrast and lack of ionizing radiation …

A pulse-coupled neural network (PCNN) is a promising image segmentation approach that
requires no training. However, it is challenging to successfully apply a PCNN to medical …