Automatic segmentation of non-enhancing brain tumors in magnetic resonance images
By Lynn M. Fletcher-Heath, L. Hall, Dmitry Goldgof, F. Murtagh. Artif. Intell. Medicine, 2001
314 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Neurology, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/73a50f51a65826e573fa966cb9a1e3126023f2f1
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Ender Konukoglu
- Ben Glocker
- Trevor Darrell
- Andrew Zisserman
- Dacheng Tao
- Xiaogang Wang
- Cordelia Schmid
In Neurology
- Ben Glocker
- Ender Konukoglu
- Sérgio Pereira
- Carlos Alberto Silva
- Guido Gerig
- Marcel Prastawa
- Charu C. Aggarwal
In Radiology, Nuclear Medicine and Imaging
- Nicholas J. Tustison
- D. Louis Collins
- Marcel Prastawa
- Doina Precup
- Nicholas Ayache
- Tal Arbel
- Ben Glocker
- Polina Golland
Citations in the corpus
- Fully Automatic Brain Tumor Segmentation using End-To-End Incremental Deep Neural Networks in MRI images (2018, unknown): “There are several methods that have been developed for Brain Tumor Detection and Segmentation: threshold-based methods ([21]; [22]), Region-based methods ([23]; [24]; [25]), Edge-based methods ([26]; [27]; [28]), Atlas-based methods ([29]; [30]; [31]), Classification and Clustering methods ([32]; [3”
- Efficient Multilevel Brain Tumor Segmentation With Integrated Bayesian Model Classification (2008, unknown): “[7] Knowledge-based fuzzy clustering NE 6 53%-90% N/A Karayiannis and Pin [8] Fuzzy clustering (VQ) MG 1 N/A N/A Prastawa et al.”
- A survey of MRI-based brain tumor segmentation methods (2014, background): “D connected components to build the tumor shape [68] .”
- Brain tumor detection and classification using machine learning: a comprehensive survey (2021, methodology): “KNN [196], SVM, nearest subspace classifier, and representation classifier [143] are supervised, whereas FCM [197, 198], hidden Markova random field [199] selforganization map [101], and SSAE [200] are unsupervised methods.”
- A brain tumor segmentation framework based on outlier detection (2004, methodology): “Automated segmentation methods based on artificial intelligence techniques were proposed in (Clark et al., 1998; Fletcher-Heath et al., 2001).”
- The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (2015, methodology): “While many technologies have been tested for their applicability to brain tumor detection and segmentation—e.g., algorithms from image retrieval as an early example [9]—we can categorize most current tumor segmentation methods into one of two broad families.”