Segmentation of medical images using adaptive region growing

By R. Pohle, Klaus D. Tönnies. SPIE Medical Imaging, 2001

423 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/e13f41c246398691714d16d66edaebf037961511

How researchers see this paper

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

In Artificial Intelligence

1. A. Ramli
2. Ivo Wolf
3. Horst Bischof
4. Hans-Peter Meinzer
5. Syamsiah Mashohor
6. M. Iqbal Bin Saripan
7. M. Balafar
8. Tobias Heimann

In Computer Vision and Pattern Recognition

1. Tobias Heimann
2. Hans-Peter Meinzer
3. B. van Ginneken
4. Joachim Hornegger
5. Luc Van Gool
6. A. Ramli
7. J. Manuel
8. Martin Andreas Styner

Citations in the corpus

• Review of brain MRI image segmentation methods (2010, methodology): “FCM is a clustering algorithm introduced by Bezdek based on minimizing an object function as follows (Pohle and Toennies 2001)”
• Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets (2009, background): “6) Three-Dimensional Region-Growing: Ruskó et al. [49] employ an advanced region-growing technique [50] and subsequent postprocessing to segment the liver.”
• A review of algorithms for medical image segmentation and their applications to the female pelvic cavity (2010, methodology): “To eliminate the dependence on initial seeds and make the algorithm automatically, statistical information and a priori knowledge can be incorporated to the algorithms (Pohle and Toennies, 2001; Dehmeshki, et al., 2003).”