Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Call For Paper : Vol 11, Issue 08, August 2024

Call for Paper

Vol 11, Issue 08, August 2024

Indexing

Analysis of Classification Methods for Diagnosis of Pulmonary Nodules in CT Images

Author : Capt. Dr. S.Santhosh Baboo 1 E.Iyyapparaj 2

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Date of Publication :17th May 2017

Abstract: The main aim of this work is to propose a novel Computer-aided detection (CAD) system based on a Contextual clustering combined with region growing for assisting radiologists in early identification of lung cancer from computed tomography(CT) scans. Instead of using conventional thresholding approach, this proposed work uses Contextual Clustering which yields a more accurate segmentation of the lungs from the chest volume. Following segmentation GLCM features are extracted which are then classified using three different classifiers namely Random forest, SVM and k-NN.

Reference :

    1. B. S. Morse, Lecture 18: Segmentation (Region Based), 1998-2000.
    2. Giorgio De Nunzio, Eleonora Tommasi, Antonella Agrusti, Rosella Cataldo, Ivan De Mitri, Marco Favetta, Silvio Maglio, Andrea Massafra, Maurizio Quarta, Massimo Torsello, Ilaria Zecca, Roberto Bellotti, Sabina Tangaro, Piero Calvini, Niccolò Camarlinghi, Fabio Falaschi, Piergiorgio Cerello, and Piernicola Oliva, “Automatic Lung Segmentation in CT Images with Accurate Handling of the Hilar Region”, Journal of digital imaging, Vol 24, No 1, pp 11-27, 2011.
    3. J. Quintanilla-Dominguez, B. Ojeda-Magaña, M. G. Cortina-Januchs, R. Ruelas, A. Vega- Corona, and D. Andina, "Image segmentation by fuzzy and possibilistic clustering algorithms for the identification of microcalcifications," Sharif University of Technology Scientia Iranica, vol. 18, pp. 580–589, Received 21 July 2010; revised 26 October 2010 accepted 8 February 2011.
    4. Eero Salli, Hannu, J, Aronen, Sauli Savolainen, Antti Korvenoja & Ari Visa 2001, „Contextual Clustering for Analysis of Functional MRI Data‟, IEEE transactions on Medical Imaging, vol. 20, no. 5, pp.403-414, 2001
    5. Linda G. Shapiro and George C. Stockman (2001): “Computer Vision”, pp 279-325, New Jersey, Prentice-Hall, ISBN 0-13- 030796-3.
    6. Murat Ceylan, Yuksel Ozbay, O. Nuri Ucan, Erkan Yildirim, 2010, A novel method for lung segmentation on chest CT images: complex-valued artificial neural network with complex wavelet transform , Turk J Elec Eng & Comp Sci, Vol.18, No.4, pp. 613-623.
    7. Elaheh Soleymanpour, Hamid Reza Pourreza, Emad ansaripour and Mehri Sadooghi Yazdi, 2011, Fully Automatic Lung Segmentation and Rib Suppression Methods to Improve Nodule Detection in Chest Radiographs, JMSS, Vol. 1, No. 3, pp. 44-52.
    8. Faizal Khan, Z & Kannan, “Intelligent Segmentation of Medical images using Fuzzy Bitplane Thresholding”, Measurement science and Review, Vol 14, No 2, pp-94-101, 2014.
    9. Faizal Khan, Z & Kavitha, V 2012, „Estimation of objects in Computed Tomography Lung Images using Supervised Contextual Clustering‟, Research Journal of Applied Sciences, vol. 7, no. 9- 12, pp.494-499.
    10. Lin DT, Yan CR, Chen WT, “Autonomous Detection of Pulmonary Nodules on CT Images with a Neural Network-Based Fuzzy system”, Comp Medical. Imaging and Graphics, Vol. 29, pp. 447-458, 2005.
    11. Prasad, M.N., Brown, M.S., Ahmad, S., Abtin, F., Allen, J., Da Costa, I., Kim, H.J., McNitt-Gray, M.F., Goldin, J.G. (2008). Automatic segmentation of lung parenchyma in the presence of diseases based on curvature of ribs. Academic Radiology, 15 (9), 1173- 1180.
    12. Antonelli M, Lazzerini B, Marcelloni F “Segmentation and Reconstruction of the Lung Volume in CT Images”. 20th annual ACM symposium on applied computing, vol I. Santa Fe, New Mexico, pp. 255-259, March 2005.
    13. JiantaoPu, Justus Roos, Chin A. Yi, Sandy Napel, Geoffrey D. Rubin, David S. Paik, “Adaptive Border Matching Algorithm: Automatic lung segmentation on chest CT images”, Comp Medical Imaging and Graphics vol. 32, pp. 452-462, 2008.
    14. Ozekes S, Osman O, Ucan ON, “Nodule detection in a lung region that‟s segmented with using genetic cellular neural networks and 3D template matching with fuzzy rule based Thresholding”, Korean Journal of Radiology, Vol. 9, pp. 1-9, 2008. [15] Cao Lei, Li Xiaojian, Zhan Jie,ChenWufan
    15. “Automated Lung Segmentation Algorithm for CAD System of Thoracic CT”, Journal of Medical Colleges of PLA, Volume 23,Issue 4, pp. 215-222, August 2008.
    16. Hyoungseop Kim, Seiji Mori, Yoshinori Itai, Seiji Ishikawa, Akiyoshi Yamamoto and Katsumi Nakamura, 2007, Automatic Detection of GroundGlass Opacity Shadows by Three Characteristics on MDCT Images, World congress on medical physics and biomedical engineering 2006, IFMBE Pro2.
    17. Breiman, L. (2001) Random forests. Machine Learning Journal Paper, 45, 5-32.
    18. Wu, X.D. and Kumar, V. (2009) The top ten algorithm in data mining. Chapman & Hall/CRC, London.
    19. Biau, G., Devroye, L. and Lugosi, G. (2008) Consistency of random forests and other averaging classifiers. Journal of Machine Learning Research, 9, 2015-2033.
    20. Jeng-Shyang Pan, Shyi-Ming Chen, Ngoc Thanh Nguyen;Nevember 2010;Computational Collective Intelligence. Technologies and Applications; Taiwan;Springer.
    21. S. G. Armato, G.McLennan, L. Bidautb,et.al“The lung image database consortium(LIDC) and image database resource initiative (IDRI): A completereference database of lung nodules on CT scans,” Med. Phys., vol.38,pp.915–931,20

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