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Detection of Lung Nodules using Image Processing Techniques

by Roena Afroze Aenney, Md. Atikur Rahman, Md. Karam Newaz
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 177 - Number 19
Year of Publication: 2019
Authors: Roena Afroze Aenney, Md. Atikur Rahman, Md. Karam Newaz
10.5120/ijca2019919638

Roena Afroze Aenney, Md. Atikur Rahman, Md. Karam Newaz . Detection of Lung Nodules using Image Processing Techniques. International Journal of Computer Applications. 177, 19 ( Nov 2019), 31-37. DOI=10.5120/ijca2019919638

@article{ 10.5120/ijca2019919638,
author = { Roena Afroze Aenney, Md. Atikur Rahman, Md. Karam Newaz },
title = { Detection of Lung Nodules using Image Processing Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2019 },
volume = { 177 },
number = { 19 },
month = { Nov },
year = { 2019 },
issn = { 0975-8887 },
pages = { 31-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume177/number19/31010-2019919638/ },
doi = { 10.5120/ijca2019919638 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:46:22.486885+05:30
%A Roena Afroze Aenney
%A Md. Atikur Rahman
%A Md. Karam Newaz
%T Detection of Lung Nodules using Image Processing Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 19
%P 31-37
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Lung cancer is characterized by uncontrolled cell growth in tissues of the lung. Recently, image processing techniques are widely used in several medical areas for image improvement in earlier detection and treatment stages, where the time factor is very important to discover the abnormality issues in target images, especially in various cancer tumors such as lung cancer, breast cancer, etc. This paper aims to develop an efficient lung nodule detection system by performing nodule segmentation through thresholding and morphological operations. Thresholding is one of the most powerful tools for image segmentation. The segmented image obtained from thresholding has the advantages of smaller storage space, fast processing speed and ease in manipulation, compared with the gray level image which usually contains 256 levels. Image Segmentation using the watershed transforms works well if we can identify or “mark” foreground objects and background locations, to find “catchment basins” and “watershed ridge lines” in an image by treating it as a surface where light pixels are high and dark pixels are low. Morphological operations apply a structuring element to an input image, creating an output image of the same size. By choosing the size and shape of the neighborhood, it can possible to construct a morphological operation that is sensitive to specific shapes in the input image. Morphological operation has been widely used to produce binary and grayscale images, with morphological techniques being applied to noise reduction, image enhancement, and feature detection. The proposed method has two stages: lung region segmentation through thresholding and then segmenting the lung nodules through thresholding and morphological operations.

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Index Terms

Computer Science
Information Sciences

Keywords

Computed Tomography Morphological Operations Segmentation Thresholding.

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