Mathematical morphology based lung segmentation using multiscale dense pyramid network architecture

Kumar, Pradeep; Raja, Linesh; Soni, Pramod Kumar; Gaur, Kuntal

doi:10.47974/JDMSC-1969

Open Access Research Article

Mathematical morphology based lung segmentation using multiscale dense pyramid network architecture

^*Pradeep KumarCorresponding authorpradeep.kumar@jaipur.manipal.eduDepartment of Computer ApplicationsManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile → , Linesh Rajalinesh.raja@jaipur.manipal.eduDepartment of Computer ApplicationsManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile → , Pramod Kumar Sonipramod.soni@jaipur.manipal.eduDepartment of Computer ApplicationsManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile → , Kuntal Gaurkuntal.gaur@jaipur.manipal.eduDepartment of Computer ApplicationsManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile →

* Corresponding author · click or hover a name for details

pp. 1139–1149Vol. 27Issue 4June 2024DOI: 10.47974/JDMSC-1969 Crossmark XML

Published Online:: 26 Jun 2024
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-1969
Pages:: 1139–1149

Abstract

Chest radiographs (CXR) are widely used technique in healthcare for the diagnosis and treatment of patients. The recognition of visual irregularities from CXR is most difficult task for expert radiologists as the chest area composed of many sensitive anatomical structures. In this paper, a reliable and secure fully automated mathematical morphology-based architecture (M2LS-Net) is designed for extracting lungs region from CXRs. This method uses dense and multiscale features to segment lungs from chest followed by the use of morphological features to eliminate the non-lung regions to enhance the accuracy of the segmentation. The designed method has attained better performance in comparison to established architectures when evaluated against different performance metrics.

Keywords

Mathematical modeling Computer aided detection Mathematical morphology Segmentation X-Ray Pyramid dilated network

Subject Classifications

Primary 93A30Secondary 49K15

References

[1] J. John and M. G. Mini, “Multilevel Thresholding Based Segmentation and Feature Extraction for Pulmonary Nodule Detection,” Procedia Technology, vol. 24, pp. 957–963 (2016), doi: https://doi. org/10.1016/j.protcy.2016.05.209.
[2] A. Maity, T. R. Nair, S. Mehta, and P. Prakasam, “Automatic lung parenchyma segmentation using a deep convolutional neural network from chest X-rays,” Biomed Signal Process Control, vol. 73, p. 103398 (2022), doi: https://doi.org/10.1016/j.bspc.2021.103398.
[3] G. S. Lodwick, T. E. Keats, and J. P. Dorst, “The Coding of Roentgen Images for Computer Analysis as Applied to Lung Cancer’,” 1963.
[4] P. Kumar Soni, N. Rajpal, and R. Mehta, “Road network extraction using multi-layered filtering and tensor voting from aerial images,” The Egyptian Journal of Remote Sensing and Space Science, vol. 24, no. 2, pp. 211–219 (2021), doi: https://doi.org/10.1016/j.ejrs.2021.01.004.
[5] J. Shiraishi et al., “Development of a Digital Image Database for Chest Radiographs With and Without a Lung Nodule,” American Journal of Roentgenology, vol. 174, no. 1, pp. 71–74, Jan. (2000), doi: 10.2214/ajr.174.1.1740071.
[6] D. Demner-Fushman et al., “Preparing a collection of radiology examinations for distribution and retrieval,” Journal of the American Medical Informatics Association, vol. 23, no. 2, pp. 304–310, Mar. (2016), doi: 10.1093/jamia/ocv080.
[7] Kumar, A., Dadheech, P., Singh, V., Poonia, R.C. and Raja, L., An improved quantum key distribution protocol for verification. Journal of Discrete Mathematical Sciences and Cryptography, 22(4), pp.491-498 (2019).
[8] X. Lian, Y. Pang, J. Han, and J. Pan, “Cascaded hierarchical atrous spatial pyramid pooling module for semantic segmentation,” Pattern Recognit, vol. 110, p. 107622 (2021), doi: https://doi.org/10.1016/j.patcog.2020.107622.
[9] S. R. Lavanya and R. Mallika, “AMCGWO : An enhanced feature selection based on swarm optimization for effective disease prediction,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 25, no. 3, pp. 635–647, Apr. (2022), doi: 10.1080/09720529.2021.2019451.
[10] Y. Zhang, X. Sun, J. Dong, C. Chen, and Q. Lv, “GPNet: Gated pyramid network for semantic segmentation,” Pattern Recognit, vol. 115, p. 107940 (2021), doi: https://doi.org/10.1016/j.patcog.2021.107940.

Views: 279Downloads: 73Citations: 0