Novel and powerful 3D adaptive crisp active contour method applied in the segmentation of CT lung images

• Type: Article in International Scientific Journal
• Date: 2017-01

Authors

Pedro Pedrosa Rebouças Filho (Author) Other; Paulo César Cortez (Author) Other; Antônio C. da Silva Barros (Author) Other; Victor Hugo C. Albuquerque (Author) Other; João Manuel R. S. Tavares (Author) FEUP

Journal

Title: Medical Image Analysis

Vol. 35

Pages: 503-516

ISSN: 1361-8415

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 30 Citations

ISI Web of Science

Scopus - 108 Citations

INSPEC

Scientific classification

CORDIS: Technological sciences

FOS: Engineering and technology

Other information

Authenticus ID: P-00M-8VH

DOI: 10.1016/j.media.2016.09.002

Abstract (EN): The World Health Organization estimates that 300 million people have asthma, 210 million people have Chronic Obstructive Pulmonary Disease (COPD), and, according to WHO, COPD will become the third major cause of death worldwide in 2030. Computational Vision systems are commonly used in pulmonology to address the task of image segmentation, which is essential for accurate medical diagnoses. Segmentation defines the regions of the lungs in CT images of the thorax that must be further analyzed by the system or by a specialist physician. This work proposes a novel and powerful technique named 3D Adaptive Crisp Active Contour Method (3D ACACM) for the segmentation of CT lung images. The method starts with a sphere within the lung to be segmented that is deformed by forces acting on it towards the lung borders. This process is performed iteratively in order to minimize an energy function associated with the 3D deformable model used. In the experimental assessment, the 3D ACACM is compared against three approaches commonly used in this field: the automatic 3D Region Growing, the level-set algorithm based on coherent propagation and the semi-automatic segmentation by an expert using the 3D OsiriX toolbox. When applied to 40 CT scans of the chest the 3D ACACM had an average F-measure of 99.22%, revealing its superiority and competency to segment lungs in CT images.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14