@article{(Open Science Index):https://publications.waset.org/pdf/11281, title = {Automatic Segmentation of Lung Areas in Magnetic Resonance Images}, author = {Alireza Osareh and Bita Shadgar}, country = {}, institution = {}, abstract = {Segmenting the lungs in medical images is a challenging and important task for many applications. In particular, automatic segmentation of lung cavities from multiple magnetic resonance (MR) images is very useful for oncological applications such as radiotherapy treatment planning. However, distinguishing of the lung areas is not trivial due to largely changing lung shapes, low contrast and poorly defined boundaries. In this paper, we address lung segmentation problem from pulmonary magnetic resonance images and propose an automated method based on a robust regionaided geometric snake with a modified diffused region force into the standard geometric model definition. The extra region force gives the snake a global complementary view of the lung boundary information within the image which along with the local gradient flow, helps detect fuzzy boundaries. The proposed method has been successful in segmenting the lungs in every slice of 30 magnetic resonance images with 80 consecutive slices in each image. We present results by comparing our automatic method to manually segmented lung cavities provided by an expert radiologist and with those of previous works, showing encouraging results and high robustness of our approach.}, journal = {International Journal of Computer and Information Engineering}, volume = {3}, number = {8}, year = {2009}, pages = {1908 - 1917}, ee = {https://publications.waset.org/pdf/11281}, url = {https://publications.waset.org/vol/32}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 32, 2009}, }