An Automated Computer-aided Diagnosis System for Malignant Tumor Localization from Lung CT Images for Surgical Planning

Manikandan T.; Mazher Iqbal J.L.; Nandalal V.; Muruganandham A.; Joshua Kumaresan S.

doi:10.61841/fpqsw407

Authors

Manikandan T. Department of ECE, Rajalakshmi Engineering College, Chennai. Author
Mazher Iqbal J.L. Professor, Department of ECE, Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India. Author
Nandalal V. Professor, Department of ECE, Sri Krishna College of Engineering and Technology, Coimbatore Author
Muruganandham A. Professor, Department of ECE, Rajarajeswari College of Engineering, Bengaluru Author
Joshua Kumaresan S. Associate Professor, Department of ECE, R.M.K Engineering College, Chennai Author

DOI:

https://doi.org/10.61841/fpqsw407

Keywords:

Diagnosis System, CT Images,, Surgical Planning

Abstract

Locating the cancerous (malignant) tumor is the best way to treat the lung cancer. In-vivo assessment of tumor growth in lungs supports estimating the cancer threat. This study focused on developing a computer-aided detection (CAD) scheme for automatic segmentation of lung lobes and cancerous tumor regions from low-dose, isotropic computed tomography (CT) images, which may aid the surgical planning for lung cancer treatment. For this retrospective study, CT scan images of 18 cancerous South Indian subjects (confirmed through biopsy tests) aged between 22 and 81 years were analyzed. Initially, the original CT image was preprocessed, and lung lobes were segmented by adaptive fissure sweep and Dual Tree Complex Wavelet Transform (DTCWT). After processing through spatial fuzzy clustering with a level set approach, the malignant tumor was segmented. Lastly, the segmented malignant tumor was placed over the lobes to display its actual position. Two radiologists were appointed to manually segment the lobes and malignant tumor from the CT lung slices of all 18 cancerous subjects. To validate the result, cancerous tumors in those CT slices were marked manually by the independent radiologists and taken as ground truth images. The outcomes suggest that the developed CAD system can detect the cancerous tumor location and thereby may help the surgeons to plan for surgery.

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