Research on a Pulmonary Nodule Segmentation Method Combining Fast Self-Adaptive FCM and Classification

• Published in: Computational and mathematical methods in medicine Vol. 2015; no. 2015; pp. 1 - 14
• Main Authors: Wang, Kai, Su, Zhi-Yuan, Zhang, Cai-Ming, Liu, Hui, Deng, Kai
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2015; Hindawi
• Subjects: Algorithms; BASIC BIOLOGICAL SCIENCES; Cluster Analysis; Databases, Factual; Diagnosis, Computer-Assisted - methods; Early Detection of Cancer - methods; Fuzzy Logic; Humans; Image Processing, Computer-Assisted - methods; Lung - blood supply; Lung Neoplasms - diagnosis; Lung Neoplasms - diagnostic imaging; Mathematical & Computational Biology; MATHEMATICS AND COMPUTING; Models, Statistical; Radiographic Image Interpretation, Computer-Assisted - methods; Solitary Pulmonary Nodule - diagnosis; Solitary Pulmonary Nodule - diagnostic imaging; Tomography, X-Ray Computed
• ISSN: 1748-670X; 1748-6718; 1748-6718
• DOI: 10.1155/2015/185726

The key problem of computer-aided diagnosis (CAD) of lung cancer is to segment pathologically changed tissues fast and accurately. As pulmonary nodules are potential manifestation of lung cancer, we propose a fast and self-adaptive pulmonary nodules segmentation method based on a combination of FCM clustering and classification learning. The enhanced spatial function considers contributions to fuzzy membership from both the grayscale similarity between central pixels and single neighboring pixels and the spatial similarity between central pixels and neighborhood and improves effectively the convergence rate and self-adaptivity of the algorithm. Experimental results show that the proposed method can achieve more accurate segmentation of vascular adhesion, pleural adhesion, and ground glass opacity (GGO) pulmonary nodules than other typical algorithms.