Segmentation of Lung Nodules in CT Scan Data: A Review

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Author(s): Shehzad Khalid (Bahria University, Pakistan), Anwar C. Shaukat (Bahria University, Pakistan), Amina Jameel (Bahria University, Pakistan)and Imran Fareed (Bahria University, Pakistan)
Copyright: 2017
Pages: 13
Source title: Medical Imaging: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-0571-6.ch023

Keywords: Medical Information Science Reference / Medical Technologies / Medicine & Healthcare

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Abstract

Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. Several studies have shown the feasibility and robustness of automated matching of corresponding nodule pairs between follow up examinations. Different image pre-processing and segmentation techniques are used in various research sides to segment different tumors or ulcers from different images. This paper aims to make a review on the existing segmentation algorithms used for CT images of pulmonary nodules and presents a study of the existing methods on automated lung nodule detection. It provides a comparison of the performance of the existing approaches in regards to effective domain results.

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Segmentation of Lung Nodules in CT Scan Data: A Review

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