A Fuzzy Locally Adaptive Bayesian Segmentation Approach for Volume Determination in PET

By Mathieu Hatt, Catherine Cheze Le Rest, Alexandre Turzo, Christian Roux, Dimitris Visvikis. IEEE Transactions on Medical Imaging, 2009

373 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a22764c90ddeeb715a6e0b00276c25d19e0bbbfc

How researchers see this paper

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

In Biomedical Engineering

1. I. E. El Naqa
2. Habib Zaidi

In Radiology, Nuclear Medicine and Imaging

1. I. E. El Naqa
2. R. Larue
3. D. de Ruysscher
4. W. van Elmpt
5. G. Defraene
6. Habib Zaidi
7. Philippe Lambin

Citations in the corpus

• Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures (2017, methodology): “performance when compared with fuzzy c-means or fuzzy hidden Markov chains.(51,52) Two other studies demonstrated that the FLAB algorithm had a good correlation with pathology(53) and that measures derived from FLAB-segmented volumes had a significantly higher predictive value in oesophageal cancer ”
• PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques (2010, methodology): “This is partly due to the high heterogeneity of PET images that complicates the identification of proper stable patterns for training purposes from a finite number of observations [89].”
• Intratumor Heterogeneity Characterized by Textural Features on Baseline 18F-FDG PET Images Predicts Response to Concomitant Radiochemotherapy in Esophageal Cancer (2011, methodology): “Tumors were then delineated automatically using the previously validated fuzzy locally adaptive Bayesian algorithm (26).”