A Novel Boundary Preserving Adaptive Filter for Improving Superpixel’s Compactness in Dental X-Ray Images

  • Maxwell A. Teixeira UECE
  • Domingos B. S. Santos UECE
  • Thelmo de Araujo UECE


Superpixels techniques are commonly used on image segmentation because the objects’s boundaries tend to be captured as a subset of the superpixels’s boundaries. Superpixel’s compactness is an important parameter for the quality of the final segmentation and it may be affected by image noise, which are very common in medical images. X-ray dental images are known to be particularly noisy. This paper analyzes the effects of smoothing and sharpening filters—frequently used in medical image preprocessing—on the compactness of the superpixels, measured by the isoperimetric quotient. Also, some experiments were conducted to validate two hypotheses about the causes of the filtering effects on the compactness of the superpixels. Based on the results, a boundary preserving adaptive filter (with several variants) is proposed, and its performance is compared to eight known filters: mean, median, Gaussian, gradient, Laplacian, morphological top hat, Kuwahara, and Kuwahara-Tomita filters. The proposed filter outperformed the filters tested here in increasing the overall compactness of the superpixels.

Palavras-chave: Boundary preserving filters, Adaptive filters, Superpixel compactness, Dental X-ray image filters


Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., and Süsstrunk, S. (2012). SLIC superpixels compared to state-of-the-art superpixel methods.

Al-Dhabyani, W., Gomaa, M., Khaled, H., and Fahmy, A. (2020). Dataset of breast ultrasound images. Data in Brief, 28(104863).

Chen, Z., Guo, B., Li, C., and Liu, H. (2020). Review on superpixel generation algorithms based on clustering. In 2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE), pages 532–537.

Das, T. P., Praharaj, S., Swain, S., Agarwal, S., and Kumar, K. (2022). Application of top-hat transformation for enhanced blood vessel extraction.

Gonzalez, R. C. and Woods, R. E. (2017). Digital Image Processing. Pearson Education, 4th edition.

Haralick, R. M., Shanmugam, K., and Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6):610–621.

Izumi, T. (2022). Fast GLCM. [link].

Kuwahara, M., Hachimura, K., Eiho, S., and Kinoshita, M. (1976). Processing of RIAngiocardiographic Images, pages 187–202. Springer US, Boston, MA.

Michelson, A. A. (1927). Studies in Optics. University of Chicago Press, Chicago, IL.

Nagao, M. and Matsuyama, T. (1979). Edge preserving smoothing. Computer Graphics and Image Processing, 9(4):394–407.

Neubert, P. (2015). Superpixels and their Application for Visual Place Recognition in Changing Environments. PhD thesis, Technische Universität Chemnitz, Sachsen, Germany.

Perumal, C. and Manuel, J. (2017). Sharpening of edges in radiographic images using edge preserving filter. International Conference on Inventive Systems and Control (ICISC).

Rad, A. E., Amin, I., Rahim, M., and Kolivand, H. (2015). Computer-aided dental caries detection system from x-ray images. Advances in Intelligent Systems and Computing, 331:233–243.

Rad, A. E., Rahim, M., Rehman, A., Altameem, A., and Saba, T. (2013). Evaluation of current dental radiographs segmentation approaches in computer-aided applications. IETE Technical Review, 30:210–222.

Rad, A. E., Rahim, M., Shafry, M., Kolivand, H., and Amin, I. B. M. (2017). Morphological region-based initial contour algorithm for level set methods in image segmentation. Multimedia Tools and Applications, 76.

Rad, A. E., Rahim, M. S. M., Rehman, A., and Saba, T. (2016). Digital Dental X-ray Database for Caries Screening. 3D Research, 7(2):18.

Rafati, M., Farnia, F., Taghvaei, M. E., and Nickfarjam, A. M. (2018). Fuzzy genetic-based noise removal filter for digital panoramic x-ray images. Biocybernetics and Biomedical Engineering, 38(4):941–965.

Schick, A., Fischer, M., and Stiefelhagen, R. (2012). Measuring and evaluating the compactness of superpixels. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), pages 930–934.

Shih, F. H. (2009). Image PProcessing and Mathematical Morphology: Fundamentals and Applications. Taylor and Francis, Boca Raton, FL.

Silva, B., Pinheiro, L., Oliveira, L., and Pithon, M. (2020). A study on tooth segmentation and numbering using end-to-end deep neural networks. 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI).

Stutz, D., Hermans, A., and Leibe, B. (2018). Superpixels: An evaluation of the state-of-the-art. Computer Vision and Image Understanding, 166:1–27.

Tomita, F. and Tsuji, S. (1977). Extraction of multiple regions by smoothing in selected neighborhoods. IEEE Transactions on Systems, Man, and Cybernetics, 7(2):107–109.
TEIXEIRA, Maxwell A.; SANTOS, Domingos B. S.; ARAUJO, Thelmo de. A Novel Boundary Preserving Adaptive Filter for Improving Superpixel’s Compactness in Dental X-Ray Images. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 24. , 2024, Goiânia/GO. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2024 . p. 37-48. ISSN 2763-8952. DOI: https://doi.org/10.5753/sbcas.2024.1832.