Lung Lesion Enhancement Using Adaptive Median Filter with Curvelet Transform

  • Authors

    • Lim J Seelan
    • Dr Padma Suresh
    2018-06-08
    https://doi.org/10.14419/ijet.v7i2.33.18130
  • Histogram equalization, Adaptive median filter, Curvelet transform

  • Abstract

    Lung cancer is one of the major troubles of cancer in the worldwide among people. Initial identification of lung cancer will highly decreases the mortality rate to save the patient lifetime. To detect the lung nodule, the lung image must be enhanced, thus a novel method is proposed for improving image quality, which is the combination of adaptive median filter and Curvelet transform. During image acquisition, the noise content also include in the image. In order to eliminate the noise, the following steps are carried out; first the input image is changed into grayscale image for reduce the processing delay. Then adaptive median filter is performed along the image to remove the noise and histogram equalization is used to improve the image contrast. Finally, the curvelet transform is applied for sharpening the image edge. Performances are analyzed based on the PSNR, MSE, SNR and SSIM values.

     

  • References

    1. [1] B. Abeer A. Raweh, Mohammed Nassef, Amr Badr, “A Hybridized Feature Selection and Extraction Approach for Enhancing Cancer Prediction based on DNA Methylationâ€, 10.1109/ACCESS.2018.2812734, IEEE Access

      [2] American Cancer Society,“What are the risk factors for non-small cell lungcancer? â€, 2013, http://www.cancer.org.

      [3] Albert A.J. & Samet J.M,“Epidemiology of lung cancerâ€, Chest. 123, 2003, pp. 21S-49S

      [4] Candès E. J and. Donoho D. L, “Curvelets,†[Online] Available: http://www-stat.stanford.edu/~donoho/Reports/1999/curvelets.pdf,1999.

      [5] Diwakar M and Kumar M, “CT image noise reduction based on adaptive Wiener filtering with Wavelet Packet Thresholdingâ€, IEEE 2014.

      [6] Donoho D. L and. Duncan M. R, “Digital curvelet transform: Strategy, implementation and experiments,†Proc. SPIE, vol. 4056, pp. 12–29,2000.

      [7] Hua, Panfang, et al., ‘Segmentation of pathological and diseased lung tissue in CT images using a graph-search algorithm’, in: IEEE - 2011.

      [8] Japanese Society of Radiology Technology, “Digital Image Databaseâ€, 1997, http://www.jsrt.or.jp.

      [9] Jiantao Pu, et al., ‘Adaptive border marching algorithm: automatic lung segmentation on chest CT images’, Comput. Med. Imag. Graph. 32 (6) (2008) 452462

      [10] Lilik Anifah, Haryanto, Rina Harimurti, “Cancer Lungs Detection on CT Scan Image Using Artificial Neural Network Backpropagation Based Gray Level Coocurrence Matrices Featureâ€, IEEE 2017.

      [11] Oulhaj H., Amine, Rziza M and Aboutajdine D, “Noise reduction in medical images - comparison of noise removal algorithmsâ€, IEEE 2012,

      [12] Pandey M, Bhatia M and Bhansal A, “An anatomization on noise removal techniques on medical imagesâ€, IEEE 2016

      [13] Renu Ann Thomas, Kumar S. S “Automatic Detection of Lung Nodules Using Classifiersâ€, IEEE 2014

      [14] Salasabil amin el-regaley, Mohammed abdel, Megeed salem “Lung nodule segmentation and detection in computed tomographyâ€, IEEE 2017

      [15] Selin Uzelaltinbulat, Buse Ugur, “Lung tumor segmentation algorithmâ€, IEEE 2017.

      [16] Tuan D. Pham, “Complementary Features For Radiomic Analysis Of Malignant And Benign Mediastinal Lymph Nodes, 978-1-5090-/17, IEEE 2017.

      [17] USC-SIPI Image Database [Online]. Available: http://sipi.usc.edu/services/ database/Database.html.

      [18] Wenjin Zhou and Allison M Rossetto, “Deep Learning for Categorization of Lung Cancer CT Imagesâ€, IEEE 2017

      [19] Yadav A.K, Roy R. Kumar A.P, Kumar C.S and. Dhakad S.K, “Denoising of ultrasound image using Discrete Wavelet Transform by Symlet Wavelet and filtersâ€, pp. 1204-1208, IEEE 2015

  • Downloads

  • How to Cite

    J Seelan, L., & Padma Suresh, D. (2018). Lung Lesion Enhancement Using Adaptive Median Filter with Curvelet Transform. International Journal of Engineering & Technology, 7(2.33), 1323-1328. https://doi.org/10.14419/ijet.v7i2.33.18130

    Received date: 2018-08-23

    Accepted date: 2018-08-23

    Published date: 2018-06-08