Lupsix: A Cascade Framework for Lung Parenchyma Segmentation in Axial CT Images

  • Hasan Koyuncu Konya Technical University Faculty of Engineering and Natural Sciences Electrical & Electronics Engineering Department
Keywords: Axial Analysis, Computed Tomography, Cascade Framework, Lung Parenchyma, Medical Image Segmentation


Lung imaging and computer aided diagnosis (CAD) play a critical role in detection of lung diseases. The most significant part of a lung based CAD is to fulfil the parenchyma segmentation, since disease information is kept in the parenchyma texture. For this purpose, parenchyma segmentation should be accurately performed to find the necessary diagnosis to be used in the treatment. Besides, lung parenchyma segmentation remains as a challenging task in computed tomography (CT) owing to the handicaps oriented with the imaging and nature of parenchyma. In this paper, a cascade framework involving histogram analysis, morphological operations, mean shift segmentation (MSS) and region growing (RG) is proposed to perform an accurate segmentation in thorax CT images. In training data, 20 axial CT images are utilized to define the optimum parameter values, and 150 images are considered as test data to objectively evaluate the performance of system. Five statistical metrics are handled to carry out the performance assessment, and a literature comparison is realized with the state-of-the-art techniques. As a result, parenchyma tissues are segmented with success rates as 98.07% (sensitivity), 99.72% (specificity), 99.3% (accuracy), 98.59% (Dice similarity coefficient) and 97.23% (Jaccard) on test dataset.


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Author Biography

Hasan Koyuncu, Konya Technical University Faculty of Engineering and Natural Sciences Electrical & Electronics Engineering Department
He graduated from Electrical and Electronics Engineering Department of Selcuk University with B.Sc. degree in 2011, with M.Sc. degree in 2013, and with Ph.D. degree in 2018. His bacholar, master and doctoral thesis are respectively related to medical signal analysis, medical pattern classification and medical image analysis. His current research interests are hybrid classifiers, ensemble learning, image segmentation, image denoising, image reconstruction, medical image analysis, pattern recognition and optimization.


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How to Cite
H. Koyuncu, “Lupsix: A Cascade Framework for Lung Parenchyma Segmentation in Axial CT Images”, IJISAE, vol. 6, no. 4, pp. 322-328, Dec. 2018.
Research Article