Lung nodule identification and classification from distorted CT images for diagnosis and detection of lung cancer

G. Savitha; P. Jidesh

doi:10.1007/978-981-13-0923-6_2

Lung nodule identification and classification from distorted CT images for diagnosis and detection of lung cancer

G. Savitha^*
, P. Jidesh

^*Corresponding author for this work

School of Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

Abstract

An automated computer-aided detection (CAD) system is being proposed for identification of lung nodules present in computed tomography (CT) images. This system is capable of identifying the region of interest (ROI) and extracting the features from the ROI. Feature vectors are generated from the gray-level covariance matrix using the statistical properties of the matrix. The relevant features are identified by adopting principle component analysis algorithm on the feature space (the space formed from the feature vectors). Support vector machine and fuzzy C-means algorithms are used for classifying nodules. Annotated images are used to validate the results. Efficiency and reliability of the system are evaluated visually and numerically using relevant measures. Developed CAD system is found to identify nodules with high accuracy.

Original language	English
Title of host publication	Machine Intelligence and Signal Analysis
Editors	M. Tanveer, Ram Bilas Pachori
Publisher	Springer Verlag
Pages	11-23
Number of pages	13
ISBN (Print)	9789811309229
DOIs	https://doi.org/10.1007/978-981-13-0923-6_2
Publication status	Published - 2019
Event	International conference on Machine Intelligence and Signal Processing, MISP 2017 - Indore, India Duration: 22-12-2017 → 24-12-2017

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	748
ISSN (Print)	2194-5357

Conference

Conference	International conference on Machine Intelligence and Signal Processing, MISP 2017
Country/Territory	India
City	Indore
Period	22-12-17 → 24-12-17

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
General Computer Science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-981-13-0923-6_2

Cite this

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title = "Lung nodule identification and classification from distorted CT images for diagnosis and detection of lung cancer",

abstract = "An automated computer-aided detection (CAD) system is being proposed for identification of lung nodules present in computed tomography (CT) images. This system is capable of identifying the region of interest (ROI) and extracting the features from the ROI. Feature vectors are generated from the gray-level covariance matrix using the statistical properties of the matrix. The relevant features are identified by adopting principle component analysis algorithm on the feature space (the space formed from the feature vectors). Support vector machine and fuzzy C-means algorithms are used for classifying nodules. Annotated images are used to validate the results. Efficiency and reliability of the system are evaluated visually and numerically using relevant measures. Developed CAD system is found to identify nodules with high accuracy.",

author = "G. Savitha and P. Jidesh",

note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd 2019.; International conference on Machine Intelligence and Signal Processing, MISP 2017 ; Conference date: 22-12-2017 Through 24-12-2017",

year = "2019",

doi = "10.1007/978-981-13-0923-6\_2",

language = "English",

isbn = "9789811309229",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Verlag",

pages = "11--23",

editor = "M. Tanveer and Pachori, \{Ram Bilas\}",

booktitle = "Machine Intelligence and Signal Analysis",

address = "Germany",

}

Savitha, G & Jidesh, P 2019, Lung nodule identification and classification from distorted CT images for diagnosis and detection of lung cancer. in M Tanveer & RB Pachori (eds), Machine Intelligence and Signal Analysis. Advances in Intelligent Systems and Computing, vol. 748, Springer Verlag, pp. 11-23, International conference on Machine Intelligence and Signal Processing, MISP 2017, Indore, India, 22-12-17. https://doi.org/10.1007/978-981-13-0923-6_2

Lung nodule identification and classification from distorted CT images for diagnosis and detection of lung cancer. / Savitha, G.; Jidesh, P.
Machine Intelligence and Signal Analysis. ed. / M. Tanveer; Ram Bilas Pachori. Springer Verlag, 2019. p. 11-23 (Advances in Intelligent Systems and Computing; Vol. 748).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Lung nodule identification and classification from distorted CT images for diagnosis and detection of lung cancer

AU - Savitha, G.

AU - Jidesh, P.

N1 - Publisher Copyright: © Springer Nature Singapore Pte Ltd 2019.

PY - 2019

Y1 - 2019

N2 - An automated computer-aided detection (CAD) system is being proposed for identification of lung nodules present in computed tomography (CT) images. This system is capable of identifying the region of interest (ROI) and extracting the features from the ROI. Feature vectors are generated from the gray-level covariance matrix using the statistical properties of the matrix. The relevant features are identified by adopting principle component analysis algorithm on the feature space (the space formed from the feature vectors). Support vector machine and fuzzy C-means algorithms are used for classifying nodules. Annotated images are used to validate the results. Efficiency and reliability of the system are evaluated visually and numerically using relevant measures. Developed CAD system is found to identify nodules with high accuracy.

AB - An automated computer-aided detection (CAD) system is being proposed for identification of lung nodules present in computed tomography (CT) images. This system is capable of identifying the region of interest (ROI) and extracting the features from the ROI. Feature vectors are generated from the gray-level covariance matrix using the statistical properties of the matrix. The relevant features are identified by adopting principle component analysis algorithm on the feature space (the space formed from the feature vectors). Support vector machine and fuzzy C-means algorithms are used for classifying nodules. Annotated images are used to validate the results. Efficiency and reliability of the system are evaluated visually and numerically using relevant measures. Developed CAD system is found to identify nodules with high accuracy.

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M3 - Conference contribution

AN - SCOPUS:85051935927

SN - 9789811309229

T3 - Advances in Intelligent Systems and Computing

SP - 11

EP - 23

BT - Machine Intelligence and Signal Analysis

A2 - Tanveer, M.

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PB - Springer Verlag

T2 - International conference on Machine Intelligence and Signal Processing, MISP 2017

Y2 - 22 December 2017 through 24 December 2017

ER -

Lung nodule identification and classification from distorted CT images for diagnosis and detection of lung cancer

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

UN SDGs

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