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

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 abili...