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Lung nodule segmentation in chest computed tomography using a novel background estimation method

  
@article{QIMS9308,
	author = {Pablo G. Cavalcanti and Shahram Shirani and Jacob Scharcanski and Crystal Fong and Jane Meng and Jane Castelli and David Koff},
	title = {Lung nodule segmentation in chest computed tomography using a novel background estimation method},
	journal = {Quantitative Imaging in Medicine and Surgery},
	volume = {6},
	number = {1},
	year = {2016},
	keywords = {},
	abstract = {Background: Lung cancer results in the highest number of cancer deaths worldwide. The segmentation of lung nodules is an important task in computer systems to help physicians differentiate malignant lesions from benign lesions. However, it has already been observed that this may be a difficult task, especially when nodules are connected to an anatomical structure.
Methods: This paper proposes a method to estimate the background of the nodule area and how this estimation is used to facilitate the segmentation task.
Results: Our experiments indicate more than 99% of accuracy with less than 1% of false positive rate (FPR).
Conclusions: The proposed methods achieved better results than a state-of-the-art approach, indicating potential to be used in medical image processing systems.},
	url = {http://qims.amegroups.com/article/view/9308}
}