Lung Parenchyma Segmentation: Fully Automated and Accurate Approach for Thoracic CT Scan Images

ABSTRACT

Computer-aided detection and diagnosis (CAD) of lung-related diseases will be helpful for early detection. Lung parenchyma segmentation is considered as a prerequisite for most of CAD systems. The available traditional methods for lung parenchyma segmentation are not accurate because the nodules that adhere to the lung pleura are recognized as fat. This paper proposes an automated lung parenchyma segmentation for accurate detection of lung nodules, mainly juxtapleural nodules. The proposed method includes the bidirectional chain code to improve the segmentation, and the support vector machine classifier is used to avoid false inclusion of regions. The proposed method is verified on various datasets for robustness of the algorithm. This automated method provides an accuracy of 97% in segmentation compared to ground truth results obtained by experts, which drastically reduces the complexity and intervention of a radiologist.

KEYWORDS:

• Bidirectional chain code
• Mid-point
• Pulmonary parenchyma
• Segmentation
• Thoracic CT slice

Acknowledgements

The authors acknowledge the National Cancer Institute and Foundation for the National Institute of Health for critical role in the creation of publicly available LIDC-IDRI and RIDER databases used in this study.

Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

S. Pramod Kumar  http://orcid.org/0000-0001-7457-3432

Mrityunjaya V. Latte  http://orcid.org/0000-0002-9624-0556

Additional information

Notes on contributors

S. Pramod Kumar

S Pramod Kumar received his BE degree in electronics and communication engineering from VEC Bellay and MTech in VLSI design and embedded Systems from UTL Technologies Limited, Bangalore. Presently, he is pursuing his PhD from VTU-RRC Belgaum and working as a faculty in Electronics and Communication Department, Kalpataru Institute of Technology, Tiptur, India. Corresponding author. Email: [email protected]

Mrityunjaya V. Latte

Mrityunjaya V Latte received his BE degree in electrical engineering and ME in digital electronics from SDM College of Engineering and Technology, Dharwad, Karnataka, India. Dr. Latte was awarded the PhD degree in 2004 for his work in the area of digital signal processing. Presently, he is working as a Principal, JSS Academy of Technical Education, Bangalore. His research interests include coding, image processing and multi resolution transforms. He received best paper award for his paper in a National Conference NCSSS 2002 held at Coimbatore, India. Email: [email protected]