TY - GEN
T1 - Lung nodule segmentation using adaptive thresholding and watershed transform
AU - Navya, K. T.
AU - Pradeep, Gokul
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/5
Y1 - 2018/5
N2 - Lung cancer, according to the study conducted by World Health Organization in 2017 is the important reason for cancer-related death globally. It was determined that nearly one in every 6 deaths is due to cancer. The cancer nodules present in the lungs of the patient causes the disease. The detection of these nodules is very important for medical researchers. Early detection of these nodules increases the patient's survival rate significantly. Computed Tomography or CT scans of the lungs in axial view, from the publically available LIDC-IDRI database, are used for the study. The lung nodules vary in size, shape, and density. All these varying conditions make segmentation more challenging. Our approach combines several image processing techniques and efficiently detect the lung nodules of any shape and size. The proposed method uses adaptive thresholding and watershed segmentation for detection of the nodules. The techniques and algorithms were implemented using MATLAB. The method was tested for 50 cases and an accuracy of 96 percent was achieved.
AB - Lung cancer, according to the study conducted by World Health Organization in 2017 is the important reason for cancer-related death globally. It was determined that nearly one in every 6 deaths is due to cancer. The cancer nodules present in the lungs of the patient causes the disease. The detection of these nodules is very important for medical researchers. Early detection of these nodules increases the patient's survival rate significantly. Computed Tomography or CT scans of the lungs in axial view, from the publically available LIDC-IDRI database, are used for the study. The lung nodules vary in size, shape, and density. All these varying conditions make segmentation more challenging. Our approach combines several image processing techniques and efficiently detect the lung nodules of any shape and size. The proposed method uses adaptive thresholding and watershed segmentation for detection of the nodules. The techniques and algorithms were implemented using MATLAB. The method was tested for 50 cases and an accuracy of 96 percent was achieved.
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U2 - 10.1109/RTEICT42901.2018.9012577
DO - 10.1109/RTEICT42901.2018.9012577
M3 - Conference contribution
AN - SCOPUS:85081786566
T3 - 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2018 - Proceedings
SP - 630
EP - 633
BT - 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2018
Y2 - 18 May 2018 through 19 May 2018
ER -
