Abstract
Chest X-ray (CXR) is the most popular imaging modality used for preliminary diagnosis of pulmonary diseases. In automatic computer-aided diagnosis (CAD), the number of false-positive cases can be reduced by segmenting out the normal anatomical structures. Demarcating lung parenchyma on CXR image is challenging due to complex anatomical structures of the human thoracic cavity. The extracted lungs boundary suffers from undesirable artifacts such as ridges and pits. This paper presents an algorithm to adaptively scan the inner lung boundary to correct the undesirable artifacts. Further, the algorithm is context-aware, it takes care of normal cavity due to the aortic knuckle and diaphragm borders. The algorithm is tested on 138 binary lung mask extracted from digital CXR images from Montgomery dataset. The quantitative, qualitative, statistical results reveal that the proposed algorithm outperforms the existing method. The average increase in segmentation accuracy is 2.561%.
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Chandra, T.B., Verma, K., Jain, D., Netam, S.S. (2021). Segmented Lung Boundary Correction in Chest Radiograph Using Context-Aware Adaptive Scan Algorithm. In: Rizvanov, A.A., Singh, B.K., Ganasala, P. (eds) Advances in Biomedical Engineering and Technology. Lecture Notes in Bioengineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-6329-4_23
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