Fuzzy c-means clustering with spatial information for image segmentation

By K. Chuang, H. Tzeng, Sharon Chen, Jay Wu, Tzong-Jer Chen. Comput. Medical Imaging Graph., 2006

1,435 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation (2011, background): “One of the problems of standard FCM algorithms in an image segmentation is the lack of spatial information [4,5,9].”
State-of-the-Art Methods for Brain Tissue Segmentation: A Review (2017, methodology): “Thus, many modified FCM approaches presented in the literature help us to prepare FCM for better tissue segmentation [72]–[75].”
Image segmentation by generalized hierarchical fuzzy C-means algorithm (2015, background): “These neighboring pixels possess similar membership, and the probability that they belong to the same cluster is great [17].”
Understanding Deep Learning Techniques for Image Segmentation (2019, methodology): “Generally traditional methods such as histogram thresholding [195], hybridization [193, 87] feature space clustering [40], regionbased approaches [59], edge detection approaches [184], fuzzy approaches [39], entropy-based approaches [47], neural networks (Hopfield neural network [35], self-organizin”