Abstract
Conventional morphological operations are implemented with fixed shape and size structuring elements. However, under these conditions, image features may change due to the complexity and diversity of images, leading to target boundary shifting, pseudo-target appearance, loss of detail, etc. In this paper, an adaptive mathematical morphology structuring element based on local pixel density and symmetry is proposed. The proposed structuring element can adaptively change shape according to the local pixel density, symmetry, and boundary features of pixels of image targets. First, the image is locally smoothed with a neighbourhood grey difference changing vector field to produce homogeneous image targets. Second, a local pixel density function based on pixel Euclidean distance, a pixel symmetry function based on pixel relative density, and a variation coefficient based on local pixel density and symmetry are defined. Finally, whether the centre pixel of the local pixel neighbourhood is a border pixel is determined with the variation coefficient. The adaptive structuring element is composed of all the border pixels determined in the local region of the image. According to the constructed structuring element, morphological dilation and erosion as well as other derived morphological operations are presented. The experimental results show that, with morphological operations, the proposed structuring element has the ability to adaptively fit the shape of image targets, preserve the necessary information of image features completely, and avoid image target boundary shifting. In addition to weakening the shading details, other image information is preserved, and distortion of the image is reduced.
Similar content being viewed by others
References
Adamski M, Sarnacki K, Saeed K (2021) Binary handwriting image enhancement by directional field-guided morphology. Inf Sci 551:168–183. https://doi.org/10.1016/j.ins.2020.11.019
Bai R, Wang J, Liang G, Li Y (2017) A new color adaptive mathematical morphology operator based on distance and threshold. In: International Conference on Anti-counterfeiting, Security, and Identification, IEEE
Curic V, Luengo Hendriks C, Borgefors G (2012) Salience adaptive structuring elements. IEEE J Sel Top Signal Process 6(7):809–819. https://doi.org/10.1109/JSTSP.2012.2207371
Das P, Dasgupta T et al (2016) A novel scheme for Bengali handwriting recognition based on Morphologi-cal operations with adaptive auto-generated structuring elements, In: Conference on control, Instrumentation, Energy & Communication, pp 1–215
Ding L, Kuriyan A, Ramchandran R, Sharma G (2017) Multi-scale morphological analysis for retinal vessel detection in wide-field fluorescein angiography. In: Image and Signal Processing Workshop, IEEE
Fang C, Wang X, Wang Q, Liang J (2020) Construction algorithm for adaptive morphological structuring elements based on the neighborhood gray difference changing vector field and relative density. IEEE Access 1(8):54928–54939. https://doi.org/10.1109/ACCESS.2020.2980885
Fouladivanda M, Kazemi K et al (2016) Adaptive morphology active contour for image segmentation. In: ICEE Conf, pp 960–965
Khurshid H, Khan MF et al (2015) Supervised building extraction using morphological profiles with adaptive structures. In: 9th Asia Modelling Symposium, pp 96–100
Landström A, Thurley MJ (2013) Adaptive morphology using tensor-based elliptical structuring elements. Pattern Recogn Lett 34(12):1416–1422. https://doi.org/10.1016/j.patrec.2013.05.003
Legaz-Aparicio Á-G, Verdú-Monedero R (2018) Adaptive morphological filters based on a multiple orientation vector field dependent on image local features. J Comput Appl Math 8:965–981. https://doi.org/10.1016/j.cam.2017.05.001
Lei T, Jia X, Liu T, Liu S, Meng H, Nandi AK (2019) Adaptive morphological reconstruction for seeded image segmentation. IEEE Trans Image Process 28(11):5510–5523. https://doi.org/10.1109/TIP.2019.2920514
Lerallut R, Decenci’ere E, Meyer F (2007) Image filtering using morphological amoebas. Image Vis Comput 25(4):395–404. https://doi.org/10.1007/1-4020-3443-1_2
Makhlouf Y, Daamouche A (2019) Automatic generation of adaptive structuring elements for road identification in VHR images. J Comput Appl Math 4:342–349. https://doi.org/10.1016/j.eswa.2018.10.049
Mallat K, Youssef R (2016) Adaptive morphological closing based on inertia tensor for structuring element estimation. In: 8th international symposium on signal image video and communication. IEEE
Pal S, Chatterjee S, Dey D, Munshi S (2019) Morphological operations with iterative rotation of structuring elements for segmentation of retinal vessel structures. Multidim Syst Sign Process 30:373–389. https://doi.org/10.1007/s11045-018-0561-9
Pinoli J, Debayle J (2009) General adaptive neighborhood mathematical morphology. In: International Conference on Mathematical Morphology & Its Applications to Image & Signal Processing, pp 2249–2252
Rishikeshan CA, Ramesh H (2018) An automated mathematical morphology driven algorithm for water body extraction from remotely sensed images. ISPRS J Photogramm Remote Sens 146:11–21. https://doi.org/10.1016/j.isprsjprs.2018.08.014
Serra J (1982) Image analysis and mathematical morphology. Biometrics 39:2–536. https://doi.org/10.2307/2531038
Stawiaski J, Meyer F (2009) Minimum spanning tree adaptive image filtering. In: Proceedings of the IEEE International Conference on Image Processing, IEEE, pp 2245–2248
Ti C, Xu G, Guan Y, Teng Y (2017) Depth recovery for Kinect sensor using contour-guided adaptive morphology filter. IEEE Sensors J 17(14):4534–4543. https://doi.org/10.1109/JSEN.2017.2707522
Wang XP, Li J, Liu Y (2014) Watershed segmentation based on gradient relief modification using variant structuring elemen. Optoelectron Lett 10(2):152–156. https://doi.org/10.1007/s11801-014-3209-5
Zhang C, Wang K (2015) Removal of high-density impulse noise based on switching morphology-mean filter. AEU Int J Electr Commun 69(1):226–235. https://doi.org/10.1016/j.aeue.2014.09.006
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interests/competing interests
All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Fang, C., Wang, X. & Wang, Q. Adaptive morphology structural element construction algorithm based on local pixel density and symmetry. Multimed Tools Appl 82, 195–215 (2023). https://doi.org/10.1007/s11042-022-13259-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-022-13259-3