CLASSIFICATION OF LUNG DISEASES FROM CHEST X-RAY IMAGES USING MODIFIED HOMOMORPHIC FILTERING AND HYBRID GLCM-MORPHOLOGY
DOI:
https://doi.org/10.11113/jurnalteknologi.v88.25057Keywords:
Homomorphic Filtering, GLCM, Morphological Operation, Chest X-Ray, SVMAbstract
Infectious diseases such as Coronavirus disease (COVID-19), viral pneumonia, and other pulmonary conditions continue to significantly affect people, leading to a high number of deaths in populations worldwide. Early testing and diagnosis are essential for improving patient outcomes within the healthcare system. This paper introduces a new classification framework, namely MHF-GM, which combines Modified Homomorphic Filtering (MHF) with a hybrid Gray Level Co-occurrence Matrix (GLCM) and a morphological feature extraction scheme for chest X-ray (CXR) images. The proposed MHF enhances image quality by incorporating a directional filter emphasizing diagonal features critical for diagnostic analysis. Key features, including texture descriptors (contrast, correlation, energy, homogeneity) and morphological parameters (area and perimeter), are integrated into a unified feature vector. A Support Vector Machine (SVM) is employed to differentiate between normal, viral pneumonia, and COVID-19 CXR images. Experimental evaluations on a dataset of 3,000 images across multiple classes show the superiority of our proposed method, achieving a classification accuracy of up to 98% in the training and testing stage by split the whole data to 70/30 for input images, thereby outperforming conventional techniques in terms of image enhancement and diagnostic performance. The findings underscore the effectiveness of the MHF-GM-based hybrid approach as a robust and cost-efficient solution for automating lung disease classification in clinical settings.
References
Cucinotta, D., and M. Vanelli. 2020. WHO Declares COVID-19 Pandemic. Acta Bio-Medica: Atenei Parmensis. 91(1): 157.
Center for Systems Science and Engineering (CSSE), Johns Hopkins University. 2021. COVID-19 Dashboard. Accessed October 15, 2021. https://coronavirus.jhu.edu/map.html.
Lauer, Stephen A., Kyra H. Grantz, Qifang Bi, Forrest K. Jones, Qulu Zheng, Hannah R. Meredith, Andrew S. Azman, Nicholas G. Reich, and Justin Lessler. 2020. The Incubation Period of Coronavirus Disease 2019 (COVID-19) from Publicly Reported Confirmed Cases: Estimation and Application. Annals of Internal Medicine. 172(9): 577–82. https://doi.org/10.7326/M20-0504.
Wan Ahmad, Wan Siti Halimatul Munirah, Wan Mimi Diya Wan Zaki, Mohammad Faizal Ahmad Fauzi, and Wooi Haw Tan. 2016. Classification of Infection and Fluid Regions in Chest X-Ray Images. In 2016 IEEE International Conference on Digital Image Computing: Techniques and Applications (DICTA). 1–5. https://doi.org/10.1109/DICTA.2016.7797020.
Candemir, Sema, Stefan Jaeger, Kannappan Palaniappan, Jonathan P. Musco, Rahul K. Singh, Zhiyun Xue, Alexandros Karargyris, Sameer Antani, George Thoma, and Clement J. McDonald. 2014. Lung Segmentation in Chest Radiographs Using Anatomical Atlases with Nonrigid Registration. IEEE Transactions on Medical Imaging. 33(2): 577–90. https://doi.org/10.1109/TMI.2013.2290491.
Nahiduzzaman, M., M. O. F. Goni, R. Hassan, M. R. Islam, M. K. Syfullah, S. M. Shahriar, et al. 2023. Parallel CNN-ELM: A Multiclass Classification of Chest X-Ray Images to Identify Seventeen Lung Diseases Including COVID-19. Expert Systems with Applications. 229: 120528. https://doi.org/10.1016/j.eswa.2023.120528.
Ieracitano, C., N. Mammone, M. Versaci, G. Varone, A. R. Ali, A. Armentano, and F. C. Morabito. 2022. A Fuzzy-Enhanced Deep Learning Approach for Early Detection of COVID-19 Pneumonia from Portable Chest X-Ray Images. Neurocomputing. 481: 202–15. https://doi.org/10.1016/j.neucom.2022.01.055.
Gupta, R. K., N. Kunhare, R. K. Pateriya, and N. Pathik. 2022. A Deep Neural Network for Detecting Coronavirus Disease Using Chest X-Ray Images. International Journal of Healthcare Information Systems and Informatics. 17: 1–27. https://doi.org/10.4018/IJHISI.20220401.oa1.
Marques, G., D. Agarwal, and I. de la Torre Díez. 2020. Automated Medical Diagnosis of COVID-19 through EfficientNet Convolutional Neural Network. Applied Soft Computing. 96: 106691. https://doi.org/10.1016/j.asoc.2020.106691.
Das, D., K. Santosh, and U. Pal. 2020. Truncated Inception Net: COVID-19 Outbreak Screening Using Chest X-Rays. Physical and Engineering Sciences in Medicine. 43: 915–25. https://doi.org/10.21203/rs.3.rs-20795/v1.
Rahman, T., M. E. Chowdhury, A. Khandakar, K. R. Islam, K. F. Islam, Z. B. Mahbub, et al. 2020. Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection Using Chest X-Ray. Applied Sciences. 10(9): 3233. https://doi.org/10.3390/app10093233.
Saleh, B. J., Z. Omar, V. Bhateja, and L. I. Izhar. 2023. Auto-Lesion Segmentation with a Novel Intensity Dark Channel Prior for COVID-19 Detection. Journal of Physics: Conference Series. 2622: 012002. https://doi.org/10.1088/1742-6596/2622/1/012002.
Saleh, B. J., Z. Omar, M. A. As'ari, and V. Bhateja. 2024. Semi-Supervised Novel Radiomics Approach for COVID-19 Severity Detection Based on Atmospheric Veil Correction. In 2024 IEEE 8th International Conference on Signal and Image Processing Applications (ICSIPA). 1–6. Kuala Lumpur, Malaysia. https://doi.org/10.1109/ICSIPA62061.2024.10687266.
Saad, Mohd Nizam, Zurina Muda, Noraidah Sahari Ashaari, and Hamzaini Abdul Hamid. 2014. Image Segmentation for Lung Region in Chest X-Ray Images Using Edge Detection and Morphology. In 2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE). 46–51. https://doi.org/10.1109/ICCSCE.2014.7072687.
Xu, Tao, Mrinal Mandal, Richard Long, Irene Cheng, and Anup Basu. 2012. An Edge-Region Force Guided Active Shape Approach for Automatic Lung Field Detection in Chest Radiographs. Computerized Medical Imaging and Graphics. 36(6): 452–63. https://doi.org/10.1016/j.compmedimag.2012.04.005.
Li, Xin, Leiting Chen, and Junyu Chen. 2017. A Visual Saliency-Based Method for Automatic Lung Regions Extraction in Chest Radiographs. In 2017 IEEE 14th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). 162–65. https://doi.org/10.1109/ICCWAMTIP.2017.8301470.
Jaeger, Stefan, Alexandros Karargyris, Sema Candemir, Les Folio, Jenifer Siegelman, Fiona Callaghan, Zhiyun Xue, Kannappan Palaniappan, Rahul K. Singh, Sameer Antani, George Thoma, Yi-Xiang Wang, Pu-Xuan Lu, and Clement J. McDonald. 2014. Automatic Tuberculosis Screening Using Chest Radiographs. IEEE Transactions on Medical Imaging. 33 (2): 233–45. https://doi.org/10.1109/TMI.2013.2284099.
Jaeger, Stefan, Alexandros Karargyris, Sameer Antani, and George Thoma. 2012. Detecting Tuberculosis in Radiographs Using Combined Lung Masks. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 4978–81. https://doi.org/10.1109/EMBC.2012.6347110.
Hassanien, A. E., L. N. Mahdy, K. A. Ezzat, H. H. Elmousalami, and H. A. Ella. 2020. Automatic X-Ray COVID-19 Lung Image Classification System Based on Multi-Level Thresholding and Support Vector Machine. medRxiv. https://doi.org/10.1101/2020.03.30.20047787.
Hammoudi, K., H. Benhabiles, M. Melkemi, F. Dornaika, I. Arganda-Carreras, D. Collard, and A. Scherpereel. 2021. Deep Learning on Chest X-Ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19. Journal of Medical Systems. 45(7): 75. https://doi.org/10.1007/s10916-021-01745-4.
Steve Eddins. 2013. "Homomorphic Filtering—Part 1."Steve on Image Processing with MATLAB. Accessed January 30, 2022. https://blogs.mathworks.com/steve/2013/06/25/homomorphic-filtering-part-1/.
Wang, Zhi-She, Feng-Bao Yang, Zhi-Hao Peng, Lei Chen, and Lie Jin. 2015. Multi-Sensor Image Enhanced Fusion Algorithm Based on NSST and Top-Hat Transformation. Optik. 126(23): 4184–90. https://doi.org/10.1016/j.ijleo.2015.08.118.
Jaeger, Stefan, Sema Candemir, Sameer Antani, Yì-Xiáng J. Wáng, Pu-Xuan Lu, and George Thoma. 2014. Two Public Chest X-Ray Datasets for Computer-Aided Screening of Pulmonary Diseases. Quantitative Imaging in Medicine and Surgery. 4(6): 475–77.
Kumar, B., and O. Dikshit. 2017. Hyperspectral Image Classification Based on Morphological Profiles and Decision Fusion. International Journal of Remote Sensing. 38(20): 5830–54. https://doi.org/10.1080/01431161.2017.1348636.
Yang, C., P. Liu, G. Yin, and L. Wang. 2017. Crack Detection in Magnetic Tile Images Using Nonsubsampled Shearlet Transform and Envelope Gray Level Gradient. Optics & Laser Technology. 90: 7–17. https://doi.org/10.1016/j.optlastec.2016.08.016.
Yousif, A. S., U. U. Sheikh, and Z. Omar. 2019. A Novel Enhancement Method for Medical Image Using Double Density Wavelet and Stationary Wavelet Transforms. In 2019 IEEE 9th International Conference on System Engineering and Technology (ICSET). 292–97. https://doi.org/10.1109/ICSEngT.2019.8906378.
Chavarín, Á., E. Cuevas, O. Avalos, J. Gálvez, and M. Pérez-Cisneros. 2023. Contrast Enhancement in Images by Homomorphic Filtering and Cluster-Chaotic Optimization. IEEE Access. 11: 73803–22. https://doi.org/10.1109/ACCESS.2023.3287559.
Thakur, N., N. U. Khan, and S. Datt Sharma. 2022. Cuckoo Search Optimized Histogram Equalization for Low Contrast Image Enhancement. In Proceedings of the 7th International Conference on Parallel, Distributed and Grid Computing (PDGC). 727–32. https://doi.org/10.1109/PDGC56933.2022.10053265.
Yousif, A. S., Z. Omar, U. U. Sheikh, and S. A. Khalid. 2021. A Novel Pathological Stroke Classification System Using NSST and WLEPCA. In 2020 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES). 565–69. https://doi.org/10.1109/IECBES48179.2021.9398808.
Khan, A. I., J. L. Shah, and M. M. Bhat. 2020. CoroNet: A Deep Neural Network for Detection and Diagnosis of COVID-19 from Chest X-Ray Images. Computer Methods and Programs in Biomedicine. 196: 105581. https://doi.org/10.1016/j.cmpb.2020.105581.
Rahman, T., M. E. Chowdhury, A. Khandakar, K. R. Islam, K. F. Islam, Z. B. Mahbub, et al. 2020. Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection Using Chest X-Ray. Applied Sciences. 10(9): 3233. https://doi.org/10.3390/app10093233.
Randieri, C., A. Perrotta, A. Puglisi, M. G. Bocci, and C. Napoli. 2025. CNN-Based Framework for Classifying COVID-19, Pneumonia, and Normal Chest X-Rays. Big Data and Cognitive Computing. 9(7): 186. https://doi.org/10.3390/bdcc9070186.
Oltu, B., S. Güney, S. E. Yuksel, and B. Dengiz. 2025. Automated Classification of Chest X-Rays: A Deep Learning Approach with Attention Mechanisms. BMC Medical Imaging. 25: 71. https://doi.org/10.1186/s12880-025-01604-5.
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.
