Lung Cancer Detection using Digital Image Processing Techniques: a Review.

MEHWISH BARI; Adeel Ahmed; Muhammad Sabir; Sajid Naveed; Mutiullah

doi:10.22581/muet1982.1902.10

Author(s)

MEHWISH BARI Department of Mathematics, National College of Business Administration & Economics, Bahawalpur, Pakistan

Adeel Ahmed Department of Computer Science, Quaid-e-Azam University, Islamabad, Pakistan

Muhammad Sabir Department of Computer Science, National College of Business Administration & Economics, Bahawalpur, Pakistan

Sajid Naveed Department of Computer Science, National College of Business Administration & Economics, Bahawalpur, Pakistan

Mutiullah Department of Computer Science, National College of Business Administration & Economics, Bahawalpur, Pakistan

Abstract

From last decade, lung cancer become sign of fear among the people all over the world. As a result, many countries generate funds and give invitation to many scholars to overcome on this disease. Many researchers proposed many solutions and challenges of different phases of computer aided system to detect the lung cancer in early stages and give the facts about the lung cancer. CV (Computer Vision) play vital role to prevent lung cancer. Since image processing is necessary for computer vision, further in medical image processing there are many technical steps which are necessary to improve the performance of medical diagnostic machines. Without such steps programmer is unable to achieve accuracy given by another author using specific algorithm or technique. In this paper we highlight such steps which are used by many author in pre-processing, segmentation and classification methods of lung cancer area detection. If pre-processing and segmentation process have some ambiguity than ultimately it effects on classification process. We discuss such factors briefly so that new researchers can easily understand the situation to work further in which direction.

Publication Details

Page(s) 351-360

DOI 10.22581/muet1982.1902.10

Published Journal: Mehran University Research Journal of Engineering and Technology, Volume: 38, Issue: 2, Year: 2019

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References

Al-Tarawneh, M.S., “Lung Cancer Detection Using Image Processing Techniques”, Leonardo Electronic Journal of Practices and Technologies, Volume 11, No. 21, pp. 147-58, 2012

Malik, B., Singh, J.P., Singh, V.B.P., and Naresh, P., “Lung Cancer Detection at Initial Stage by Using Image Processing and Classification Techniques”, Lung Cancer, Volume 3, No. 11, 2016.

Sharma, D., and Jindal, G., “Identifying Lung Cancer Using Image Processing Techniques”, International Conference on Computational Techniques and Artificial Intelligence, pp. 115-120, 2011.

Rani, J., “Noise Removal in Medical Images Using Filters”, International Journal of Engineering Research & Technology, Volume 2, pp. 1013-1016, March, 2013.

Gajdhane, M.V.A., and Deshpande, L., “Detection of Lung Cancer Stages on CT scan Images by Using Various Image Processing Techniques”, International Organization of Scientific Research Journal of Computer Engineering, pp. 2278-0661, 2014.

Patil, B.G., and Jain, S.N., “Cancer Cells Detection using Digital Image Processing Methods”, International Journal of Latest Trends in Engineering & Technology, Volume 3, 2014.

Onizawa, N., Katagiri, D., Matsumiya, K., Gross, W.J., and Hanyu, T., “Gabor Filter Based on Stochastic Computation”, IEEE Signal Processing Letters, Volume 22, No. 9, pp. 1224-1228, 2015.

Avinash, S., Manjunath, K., and Senthilkumar, S., “Analysis and Comparison of Image Enhancement Techniques for the Prediction of Lung Cancer”, 2ndIEEE International Conference on Recent Trends in Electronics, Information & Communication Technology, pp. 1535-1539, 2017

Gauthier, J.-P., Stephant, N., Rondeau, B., Cody, J.A., and Fritsch, E., “Aluminium Diboride-Type Structure in Ethiopian Opal-CT Revealed by Fast Fourier Transform”, Journal of Applied Crystallography, Volume 51, No. 1, pp. 27-34, 2018.

Gu, S., Meng, D., Zuo, W., and Zhang. L., “Joint Convolutional Analysis and Synthesis Sparse Representation for Single Image Layer Separation”, IEEE International Conference on Computer Vision, pp. 1717-1725, 2017.

Yan, X., and Li, Y., “A Method of Lane Edge Detection Based on Canny Algorithm”, IEEE, pp. 2120-2124, 2017

Nagao, M., Miyake, N. Yoshino, Y., Lu, H., Tan, J.K., Kim, H., Murakami, S., Aoki, T., Hirano, Y., and Kido, S., “Detection of Abnormal Candidate Regions on Temporal Subtraction Images Based on DCNN”, Proceedings of 17th International Conference on Control, Automation and Systems, pp. 1444-1448, 2017

Shao, W., Liu, M., Xu, Y.-Y., Shen, H.B., and Zhang, D., “An Organelle Correlation-Guided Feature Selection Approach for Classifying Multi-Label Subcellular Bioimages”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2017

Al-Zubaidi, A.K., Sideseq, F.B. Faeq, A., and Basil, M., “Computer Aided Diagnosis in Digital Pathology Application: Review and Perspective Approach in Lung Cancer Classification”, Annual Coference on New Trends in Information & Communication Technology, Application, pp. 219-224, 2017

Zubair, A.R., “Frequency Domain Image Restoration”, International Journal of Computer and Information Technology, Volume 7, No. 2, pp. 64-73, 2018.

Li, Y., Qian, M., Liu, P., Cai, Q., Li, X., Guo, J., Yan, H., Yu, F., Yuan, K., and Yu, J., “The Recognition of Rice Images by UAV Based on Capsule network”, Cluster Computing, pp. 1-10, 2018

Berahim, M., Samsudin, N.A., and Nathan, S.S., “A Review: Image Analysis Techniques to Improve Labeling Accuracy of Medical Image Classification”, Recent Advances on Soft Computing and Data Mining, pp. 298-307, 2018.

Talukdar, J., and Sarma, D.P., “A Survey on Lung Cancer Detection in CT scans Images Using Image Processing Techniques”, International Journal of Current Trends in Science and Technology, Volume 8, No. 3, pp. 20181-20186, 2018.

Jain, S., and Laxmi, V., “Color Image Segmentation Techniques: A Survey”, Proceedings of International Conference on Microelectronics, Computing & Communication Systems, pp. 189-197, 2018

Chowdhury, K., Chaudhuri, D., and Pal, A.K., “Seed Point Selection Algorithm in Clustering of Image Data”, Progress in Intelligent Computing Techniques: Theory, Practice, and Applications, pp. 119-126,

Wang, P., Xu, S., Li, Y., Wang, L., and Song, Q., “Feature- Based Analysis of Cell Nuclei Structure for Classification of Histopathological Images”, Digital Signal Processing, 2018.

Chouhan, S.S., Kaul, A., and Singh, U.P., “Image Segmentation Using Computational Intelligence Techniques”, Archives of Computational Methods in Engineering, pp. 1-64, February, 2018

Vesal, S., Ravikumar, N., Ellman, S., and Maier, A., “Comparative Analysis of Unsupervised Algorithms for Breast MRI Lesion Segmentation”, Bildverarbeitung Für Die Medizin, pp. 257-262, 2018.

Chandra, M.A., and Bedi, S., “Survey on SVM and Their Application in Image Classification”, International Journal of Information Technology, pp. 1-2018.

Hussein, S., Chuquicusma, M.M., Kandel, P., Bolan, C.W., Wallace, M.B., and Bagci, U. “Supervised and Unsupervised Tumor Characterization in the Deep Learning Era”, arXiv preprint arXiv:1801.03230, 2018.

Read, J., Pfahringer, B., Holmes, G., and Frank, E., “Classifier Chains for Multi-Label Classification”, Machine Learning, Volume 85, pp. 333, 2011