Brain tumor segmentation using Magnetic Resonance (MR) Imaging technology plays a
significant role in computer-aided brain tumor diagnosis. However, when applying classic …

Image segmentation is an important field of computer vision and has attracted significant
research attention in the recent years. In this paper, we provide a survey of regional level set …

A novel level set model is proposed by regularizing local fitting energy to segment the
intensity inhomogeneous images. In proposed model, local image fitting energy information …

Picture fuzzy C-means clustering is a novel computational intelligence method that has
some advantages over fuzzy clustering in pattern analysis and machine intelligence …

Image segmentation is a fundamental and challenging task in image processing and
computer vision. The colour image segmentation is attracting more attention as the colour …

Automatic methods for morphological characterisation of nano-particulate from microscopy
images usually involve the use of thresholding procedures, which cannot effectively deal …

It is difficult to obtain accurate segmentation results for a color map when there are shadows,
low-contrast edges, or blurred regions in the image. The depth discontinuity of the image …

The detection of the lumen and media-adventitia (MA) borders in intravascular ultrasound
(IVUS) images is crucial for quantifying plaque burdens. The challenge of the segmentation …

The level set is a classical image segmentation model. In order to achieve its stable
evolution, the level set function should be a signed distance function (SDF). However, due to …

Objectives: Glioblastoma multiforme (GBM) is a highly infiltrative primary brain tumour with
an aggressive clinical course. Diffusion tensor imaging (DT-MRI or DTI) is a recently …