MR brain image segmentation using an enhanced fuzzy C-means algorithm
By L. Szilágyi, Z. Benyó, S. Szilágyi, H. S. Adam. Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439), 2003
480 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/cc4897585a9ac0858d31c8cc3e82d3155a420a2a
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In Computer Vision and Pattern Recognition
- Yanning Zhang
- Hongying Meng
- Tao Lei
- Asoke Kumar Nandi
- Xiaohong Jia
- N. Gordillo
- Pilar Sobrevilla
- Eduard Montseny
Citations in the corpus
- State of the art survey on MRI brain tumor segmentation. (2013, methodology): “Szilágyi et al. [75], and Chen and Zhang [76] proposed to evaluate the neighborhoods of each pixel as a pre-fi ltering step, and perform FCM afterwards.”
- A survey of MRI-based brain tumor segmentation methods (2014, methodology): “A novel fast and robust FCM framework was introduced for brain tumor segmentation called FGFCM clustering algorithms by incorporating local information [70] .”
- Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation (2007, methodology): “Besides, the quality of image segmented by EnFCM is comparable to that of FCM_S [16].”
- Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering (2018, methodology): “EnhancedFCM (EnFCM) [20] is an excellent algorithm from the viewpoint of its low computational time; it performs clustering based on gray level histograms instead of pixels of a summed image.”
- Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation (2013, unknown): “To speed up the image segmentation process, Szilagyi et al. [10] proposed the enhanced FCM (EnFCM), which form a linearly-weighted sum image from both the local neighborhood average gray level of each pixel and original image, and then clustering is performed on the basis of the gray level…”