Automatic segmentation of pathological lung using incremental nonnegative matrix factorization

• Published in: 2015 IEEE International Conference on Image Processing (ICIP) pp. 3111 - 3115
• Main Authors: Hosseini-Asl, Ehsan, Zurada, Jacek M., El-Baz, Ayman
• Format: Conference Proceeding
• Language: English
• Published: IEEE 09.12.2015
• Subjects: Computed tomography; Context; Image segmentation; incremental learning; lung segmentation; Lungs; Matrix decomposition; Nonnegative matrix factorization; Pathology; Three-dimensional displays
• DOI: 10.1109/ICIP.2015.7351376

Accurate segmentation of pathological lungs from large-size chest computed tomographic images is crucial for computer-assisted lung cancer diagnostics. In this paper, a new framework for automatic pathological lung segmentation is proposed. The proposed INMF-based segmentation approach has the ability to handle the in-homogeneities caused by the arteries, veins, bronchi, and possible pathologies that may exist in the lung tissues, and to detect the number of clusters in the image in an automated manner. The proposed INMF-based segmentation framework is quantitatively validated on simulated realistic lung phantoms that mimic different lung pathologies (7 datasets), in vivo data sets for 17 subjects, and for lung disease with severe pathologies. Three metrics are used: the Dice coefficient, modified Hausdorff distance, and absolute lung volume difference. Results show that the proposed approach outperforms existing lung segmentation techniques and can handle in-homogenities caused by different pathologies.