Segmentation of Lung Nodules in CT Scan Data: A Review

Segmentation of Lung Nodules in CT Scan Data: A Review

Shehzad Khalid, Anwar C. Shaukat, Amina Jameel, Imran Fareed
Copyright: © 2017 |Pages: 13
DOI: 10.4018/978-1-5225-0571-6.ch023
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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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2. Background

The process of acquiring medical images from imaging modalities is the Image Acquisition process. There exist several common methods for lung imaging. CT enables visualization of small volume or low-contrast nodules by decreasing the thickness of slices and the interval between consecutive slices. CT is preferable for the preliminary analysis of lung nodules screening comparing to other lung imaging methods as they produce more accurate results. Lung CT images can be found in public and private databases. In Image pre-processing, the process of improving both the quality and interpretability of the acquired lung images which reduces noise and artifacts in the lung image slices and hence detecting the position and size of the nodule.

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