A great number of improved fuzzy c-means (FCM) clustering algorithms have been widely
used for grayscale and color image segmentation. However, most of them are time …

Image segmentation with Fuzzy C-Means (FCM) clustering algorithm is a widely researched
topic. In the literature, region-level information considers more redundancy information of …

This paper presents a deep neural network called DIVA unfolding a baseline adaptive
denoising algorithm (DeQuIP), relying on the theory of quantum many-body physics …

This paper proposes an affinity fusion graph framework to effectively connect different
graphs with highly discriminating power and nonlinearity for natural image segmentation …

Over the last 30 years a plethora of variational regularization models for image
reconstruction have been proposed and thoroughly inspected by the applied mathematics …

In computer aided diagnosis of liver tumor detection, tumor segmentation from the CT image
is an important step. The majority of methods are not able to give an integrated structure for …

Superpixel-based fast fuzzy C-means clustering (SFFCM) is an efficient method for color
image segmentation. However, it is sensitive to noise and blur. Its superpixel method called …

Accurate delineation of liver tumors in medical images is a vital step in diagnosis, treatment
planning, and monitoring. In this paper, we utilize a generative model for segmentation of …

The growing need for smart surveillance solutions requires that modern video capturing
devices to be equipped with advance features, such as object detection, scene …

During the COVID‐19 pandemic, huge interstitial lung disease (ILD) lung images have been
captured. It is high time to develop the efficient segmentation techniques utilized to separate …