Segmentation of CT Lung Images Using FCM with Active Contour and CNN Classifier

Document Type : Research Articles

Authors

1 Department of Electronics and Communication, Rajalakshmi Instittue of Technology, Chennai, India.

2 Department of EIE, Saveetha Engineering College, India.

3 Department of Medical Electronics, Saveetha Engineering College, Chennai, India.

4 ECE, Hindusthan Institute of Technology, Coimbatore, India.

5 Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences Chennai, India.

Abstract

Objective: Lung cancer is one of the unsafe diseases for human which reduces the patient life time. Generally, most of the lung cancers are identified after it has been spread into the lung parts and moreover it is difficult to find the lung cancer at the early stage. It requires radiologist and special doctors to find the tumoral tissue of the lung cancer. For this reason, the recommended work helps to segment the tumoral tissue of CT lung image in an effective way. Methods:  The research work uses hybrid segmentation technique to separate the lung cancer cells to diagnose the lung tumour. It is a technique which combines active contour along with Fuzzy c means to diagnose the tumoral tissue. Further the segmented portion was trained by Convolutional Neural Network (CNN) in order to classify the segmented region as normal or abnormal. Results: The evaluation of the proposed method was done by analyzing the results of test image with the ground truth image. Finally, the results of the implemented technique provided good accuracy, Peak signal to noise ratio (PSNR), Mean Square Error (MSE) value. In future the other techniques can be utilized to improve the details before segmentation. The proposed work provides 96.67 % accuracy. Conclusion: Hybrid segmentation technique involves several steps like preprocessing, binarization, thresholding, segmentation and feature extraction using GLCM.

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Asian Pacific Journal of Cancer Prevention
Volume 23, Issue 3
March 2022
Pages 905-910

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History

  • Receive Date: 24 September 2021
  • Revise Date: 15 October 2021
  • Accept Date: 04 March 2022

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