U-net is an image segmentation technique developed primarily for image segmentation
tasks. These traits provide U-net with a high utility within the medical imaging community …

The images captured by the cameras contain distortions, misclassified pixels, uncertainties
and poor contrast. Therefore, the multi-focus image fusion (MFIF) integrates various input …

Background White matter hyperintensities (WMH), of presumed vascular origin, are visible
and quantifiable neuroradiological markers of brain parenchymal change. These changes …

Radiomics is an emerging field using the extraction of quantitative features from medical
images for tissue characterization. While MRI-based radiomics is still at an early stage, it …

In cardiology, ultrasound is often used to diagnose heart disease associated with myocardial
infarction. This study aims to develop robust segmentation techniques for segmenting the left …

White matter hyperintensity (WMH) is associated with various aging and neurodegenerative
diseases. In this paper, we proposed and validated a fully automatic system which integrates …

White matter hyperintensities (WMHs) are frequently observed on structural neuroimaging of
elderly populations and are associated with cognitive decline and increased risk of …

We developed a fully automated, two-step deep learning approach for characterizing
coronary calcified plaque in intravascular optical coherence tomography (IVOCT) images …

The ore fragment size on the conveyor belt of concentrators is not only the main index to
verify the crushing process, but also affects the production efficiency, operation cost and …

Background and objectives: Brain MR images consist of three major regions: gray matter,
white matter and cerebrospinal fluid. Medical experts make decisions on different serious …