Abstract
Lung cancer is the most frequent cause of cancer mortality in the world. The diagnostic procedure usually begins with a chest X-ray; however, it is difficult to interpret due to the set of anatomical structures overlapped. Computer-aided detection (CAD) systems are a diagnostic aid tool for radiologists. In the present work a CAD system is proposed for the detection of lung nodules on chest radiographs. Methods such as convolution, local normalization and homomorphic filters are used to pre-process images, using a multi-level threshold method supported by morphological operations for anatomical segmentation. This is followed by a candidate nodule detector using the local sliding-band convergence filter. The candidate nodules are segmented using an adaptive threshold based on distance. A set of characteristics for each candidate are calculated based on the segmentation. The system was tested by a free available database (DB) of 247 images, of which 154 are pulmonary nodules (100 malignant and 54 benign cases and 93 nodules). The results obtained indicate that the system is able of detecting 98.7% of the nodules of the DB with an average of 56.08 detections per image. Two false positive were obtained due to lung segmentation.
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Martinez-Machado, E., Perez-Diaz, M., Orozco-Morales, R. (2021). Automated System for the Detection of Lung Nodules. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2021. Lecture Notes in Computer Science(), vol 13055. Springer, Cham. https://doi.org/10.1007/978-3-030-89691-1_33
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DOI: https://doi.org/10.1007/978-3-030-89691-1_33
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