Abstract
Purpose
Lung cancer is the second most common type of cancer prevalent in men worldwide. The early diagnosis of lung cancer can reduce cancer-related deaths considerably and increase the survival rate for a few years. In recent years, computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems emerged as promising techniques for radiologists for the early diagnosis of lung cancer. An efficient pre-processing technique and accurate segmentation of the lung parenchyma in medical images will reduce false positives, which in turn can considerably improve the specificity of classification of the lung nodules as benign or malignant.
Methods
A novel framework for preprocessing lung images and segmentation of the region of interest is proposed in this study. The noise removal in low-dose computed tomography (LDCT) images is performed by discrete wavelet transform with adaptive thresholding (DWTWAT). The segmentation of the lung region is performed by genetic algorithm enhanced K-means clustering (GAK-means) and genetic algorithm enhanced Fuzzy c-means clustering (GAFCM) in LDCT images. The segmentation is followed by lung reconstruction to preserve the juxta-pleural and Pleural tail nodules attached to the lung boundary.
Results
The proposed methods were individually evaluated with the commonly used metrics of sensitivity, specificity, and accuracy. The novel noise removal technique of DWTWAT and segmentation with GAFCM has achieved a sensitivity, specificity, and accuracy of 99.54%, 99.99%, and 99.54%, respectively. The noise removal technique of DWTWAT for preprocessing and segmentation with GAK-means has achieved sensitivity, specificity, and accuracy of 99.47%, 99.77%, and 99.26%.
Conclusion
The proposed techniques of noise removal and segmentation is a novel combination that showed improved results compared to the existing state-of-the-art method.
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Data availability
The datasets analyzed in the current study are available in the LIDC-IDRI repository (https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI).
Code availability
The code used for implementation of algorithm is available. It will be provided on request.
Abbreviations
- CADe:
-
Computer-aided detection
- CADx:
-
Computer-aided diagnosis
- CNN:
-
Convolution neural network
- DICOM:
-
Digital Imaging and Communication in Medicine
- DWT:
-
Discrete wavelet transform
- FCM:
-
Fuzzy c-means clustering
- FP:
-
False positive
- FN:
-
False negative
- GAK-means:
-
Genetic algorithm enhanced K-means clustering
- GAFCM:
-
GENETIC algorithm enhanced Fuzzy c-means clustering
- HU:
-
Hounsfield units
- LDCT:
-
Low-dose computed tomography
- LIDC-IDRI :
-
Lung Image Database Consortium and Image Database Resource Initiative
- MRI:
-
Magnetic resonance imaging
- MSE:
-
Mean square error
- NSCLC:
-
Non-small cell lung cancer
- PET:
-
Positron emission tomography
- SCLC :
-
Small cell lung cancer
- TN:
-
True negative
- TP :
-
True positive
- WHO :
-
World Health Organization
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Acknowledgements
We would like to extend our sincere thanks to Dr. Jayamohan Unnithan, Pulmonologist, Kovai Respiratory Care and Research Center, Coimbatore, India, for his constant and valuable support to this research work. This publication was supported by Deanship of Scientific Research at Prince Sattam Bin Abdulaziz University, Al Kharj, Saudi Arabia.
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Ziyad, S.R., Radha, V. & Vayyapuri, T. A novel lung extraction approach for LDCT images using discrete wavelet transform with adaptive thresholding and Fuzzy C-means clustering enhanced by genetic algorithm. Res. Biomed. Eng. 38, 581–598 (2022). https://doi.org/10.1007/s42600-022-00210-6
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DOI: https://doi.org/10.1007/s42600-022-00210-6