Interpolation Features in ContExt Software for Mammography Processing
DOI:
https://doi.org/10.5433/1679-0375.2025.v46.53582Keywords:
medical image processing, contour extraction, interpolation methods, computational efficiencyAbstract
This study proposes the expansion of the functionalities of the ContExt software, designed for contour extraction and mesh generation for numerical simulations in medical imaging. The main innovation is the incorporation of interpolation methods to enlarge contours after image resolution reduction, thereby enabling greater computational efficiency. Bilinear, bicubic, biquadratic, and cubic spline methods were evaluated, along with refinement techniques such as node removal and the Ramer–Douglas–Peucker algorithm. Reducing the image to half of its original resolution resulted in a significant decrease in processing time (over 95%) while maintaining satisfactory contour quality. However, a reduction to one-quarter resolution compromised the fidelity of the extracted structures. Bilinear method achieved the highest overlap rate, whereas the cubic spline proved to be the most accurate. Tests demonstrate that the new functionalities integrated into ContExt make the software more versatile and efficient for processing high-resolution images, with potential applications in various clinical and computational contexts.
Downloads
References
Azevedo, R. L., Gerótica, R. M. G., & Sanches, T. P. (2016). A importância da mamografia no diagnóstico precoce do câncer de mama. UNILUS Ensino e Pesquisa, 13(30). http://revista.lusiada.br/index.php/ruep/article/view/598
Canayaz, M. (2021). MH-COVIDNet: Diagnosis of COVID-19 using deep neural networks and metaheuristic-based feature selection on X-ray images. Biomedical Signal Processing and Control, 64, 102257. https://doi.org/10.1016/j.bspc.2020.102257 DOI: https://doi.org/10.1016/j.bspc.2020.102257
Casamaximo, R. F., Romeiro, N. M. L., da Silva, P. Z., Souza, I. P., Silva, J. T., Natti, P. L., & Cirilo, E. R. (2021). Algorithm for extracting points from images: irregular contours. XLII Ibero-Latin American Congress on Computational Methods in Engineering, 3(03), 1–7. https://www.researchgate.net/publication/354930765_Algorithm_for_extracting_points_from_images_irregular_contours
Chai, X., Chen, J., Mao, Z., & Zhu, Q. (2023). An Upscaling–Downscaling Optimal Seamline Detection Algorithm for Very Large Remote Sensing Image Mosaicking. Remote Sensing, 15, 89. https://doi.org/10.3390/rs15010089 DOI: https://doi.org/10.3390/rs15010089
Cirilo, E. R., Figliaggi, R. M., & Natti, P. L. (2023). Quality Parameter for Edge Representation of Images via Cubic Spline. Semina: Ciências Exatas e Tecnológicas, 44, e47720. https://doi.org/10.5433/1679-0375.2023.v44.47720 DOI: https://doi.org/10.5433/1679-0375.2023.v44.47720
ContExt. (2024). Software for contour extraction and mesh generation for numerical analysis. https://github.com/RafaelCasamaximo/ContExt
Dumic, E., Grgic, S., & Grgic, M. (2007). The Use of Wavelets in Image Interpolation: Possibilities and Limitations. Radioengineering, 16(4).
Fadnavis, S. (2014). Image Interpolation Techniques in Digital Image Processing: An Overview. International Journal of Engineering Research and Applications (IJERA), 4(10), 70–73. https://ijera.com/papers/Vol4_issue10/Part%20-%201/K41007073.pdf
Feng, Y., Spezia, M., Huang, S., Yuan, C., Zeng, Z., Zhang, L., Ji, X., Liu, W., Huang, B., Luo, W., Liu, B., Lei, Y., Du, S., Vuppalapati, A., Luu, H. H., Haydon, R. C., He, T.-C., & Ren, G. (2018). Breast cancer development and progression: Risk factors, cancer stem cells, signaling pathways, genomics, and molecular pathogenesis. Genes & Diseases, 5(2), 77–106. https://doi.org/10.1016/j.gendis.2018.05.001 DOI: https://doi.org/10.1016/j.gendis.2018.05.001
Instituto Nacional de Câncer José Alencar Gomes da Silva. (2004). Controle do câncer de mama - Documento de consenso. Revista Brasileira de Cancerologia, 50(2), 77–90. https://doi.org/10.32635/2176-9745.RBC.2004v50n2.2039 DOI: https://doi.org/10.32635/2176-9745.RBC.2004v50n2.2039
Jales, R. M. (2022). Incidência mamográfica em clivagem em caso de câncer de mama bilateral [Dr.Pixel]. http://drpixel.fcm.unicamp.br/conteudo/incidencia-mamografica-em-clivagem-em-caso-de-cancer-de-mama-bilateral
Jarosch, A., Anslow, F., & Clarke, G. (2010). High-resolution precipitation and temperature downscaling for glacier models. Climate Dynamics, 38, 391–409. https://doi.org/10.1007/s00382-010-0949-1 DOI: https://doi.org/10.1007/s00382-010-0949-1
Jasti, V. D. P., Zamani, A. S., Arumugam, K., Naved, M., Pallathadka, H., Sammy, F., Raghuvanshi, A., & Kaliyaperumal, K. (2022). Computational technique based on machine learning and image processing for medical image analysis of breast cancer diagnosis. Security and Communication Networks, 2022(1), 1918379. https://doi.org/10.1155/2022/1918379 DOI: https://doi.org/10.1155/2022/1918379
Kösters, J., & Gøtzsche, P. (2003). Regular self-examination or clinical examination for early detection of breast cancer. Cochrane Database of Systematic Reviews, (2), CD003373. https://pubmed.ncbi.nlm.nih.gov/12804462/ DOI: https://doi.org/10.1002/14651858.CD003373
Kshatri, S. S., & Singh, D. (2023). Convolutional Neural Network in Medical Image Analysis: A Review. Archives of Computational Methods in Engineering, 30, 2793–2810. https://doi.org/10.1007/s11831-023-09898-w DOI: https://doi.org/10.1007/s11831-023-09898-w
Liu, S., Liu, X., Wang, S., & Khan, M. (2021). Fuzzy-aided solution for out-of-view challenge in visual tracking under IoT-assisted complex environment. Neural Computing and Applications, 33(4), 1055–1065. https://doi.org/10.1007/s00521-020-05021-3 DOI: https://doi.org/10.1007/s00521-020-05021-3
McKinney, W. (2011). pandas: a foundational Python library for data analysis and statistics. In Python for High Performance and Scientific Computing (Vol. 14, pp. 1–9). https://www.researchgate.net/publication/265194455_pandas_a_Foundational_Python_Library_for_Data_Analysis_and_Statistics
Nyström, L., Andersson, I., Bjurstam, N., Frisell, J., Nordenskjöld, B., & Rutqvist, L. E. (2002). Long-term effects of mammography screening: updated overview of the Swedish randomised trials. The Lancet, 359(9310), 909–919. https://doi.org/10.1016/S0140-6736(02)08020-0 DOI: https://doi.org/10.1016/S0140-6736(02)08020-0
Patel, V., Mistree, K., & Gopalbhai, C. (2013). A Review on Different Image Interpolation Techniques for Image Enhancement. International Journal of Emerging Technology and Advanced Engineering, 3(12), 129–133. https://iranarze.ir/wp-content/uploads/2017/10/7964-English-IranArze.pdf
Python Software Foundation. (2021). Python releases for Windows, version 24. https://www.python.org/downloads/windows/
Ramer, U. (1972). An iterative procedure for the polygonal approximation of plane curves. Computer Graphics and Image Processing, 1, 244–256. https://doi.org/10.1016/S0146-664X(72)80017-0 DOI: https://doi.org/10.1016/S0146-664X(72)80017-0
Romeiro, N. M. L., Panis, C., dos Santos, M. C. T., Rech, D., Natti, P. L., & Cirilo, E. R. (2022). Covariate clustering: women with breast cancer in southwestern Paraná, Brazil. Revista de Senología y Patología Mamaria, 35(3), 175–183. https://doi.org/10.1016/j.senol.2021.12.005 DOI: https://doi.org/10.1016/j.senol.2021.12.005
Scholl, I., Aach, T., Deserno, T. M., & Kuhlen, T. (2011). Challenges of medical image processing. Computing Science - Research and Development, 26, 5–13. https://doi.org/10.1007/s00450-010-0146-9 DOI: https://doi.org/10.1007/s00450-010-0146-9
Schulz-Wendtland, R., Fuchsjäger, M., Wacker, T., & Hermann, K.-P. (2009). Digital mammography: an update. European Journal of Radiology, 72(2), 258–265. https://doi.org/10.1016/j.ejrad.2009.05.052 DOI: https://doi.org/10.1016/j.ejrad.2009.05.052
Shannon, C. E. (1949). Communication in the presence of noise. Proceedings of the IRE, 37(1), 10–21. https://doi.org/10.1109/JRPROC.1949.232969 DOI: https://doi.org/10.1109/JRPROC.1949.232969
Sickles, E. A. (2007). The spectrum of breast asymmetries: imaging features, work-up, management. Radiologic Clinics of North America, 45(5), 765–771. https://doi.org/10.1016/j.rcl.2007.06.002 DOI: https://doi.org/10.1016/j.rcl.2007.06.002
Siegel, R. L., Miller, K. D., & Jemal, A. (2018). Cancer statistics, 2018. CA: A Cancer Journal for Clinicians, 68(1), 7–30. https://doi.org/10.3322/caac.21442 DOI: https://doi.org/10.3322/caac.21442
Silva, P. Z., Casamaximo, R. F., Romeiro, N. M. L., Izidoro, G. P., Tokairin, R. P., & Natti, P. L. (2025). Integrating breast cancer laboratory imaging with numerical analysis: ContExt software for contour extraction and mesh generation. Computers in Biology and Medicine, 196, 110591. https://doi.org/10.1016/j.compbiomed.2025.110591 DOI: https://doi.org/10.1016/j.compbiomed.2025.110591
Smith, D. (2020). Biquadratic interpolation. National Oceanic and Atmospheric Administration. https://repository.library.noaa.gov/view/noaa/26464
Suman, S., Hussin, F. A., Malik, A. S., Walter, N., Lee Goh, K., Hilmi, I., & Ho, S. H. (2014). Image Enhancement Using Geometric Mean Filter and Gamma Correction for WCE Images. In C. K. Loo et al. (Eds.), Neural Information Processing. ICONIP 2014, Springer. https://doi.org/10.1007/978-3-319-12643-2_34 DOI: https://doi.org/10.1007/978-3-319-12643-2_34
Sun, W., & Chen, Z. (2020). Learned image downscaling for upscaling using content adaptive resampler. IEEE Transactions on Image Processing, 29, 4027–4040. https://doi.org/10.1109/TIP.2020.2970248 DOI: https://doi.org/10.1109/TIP.2020.2970248
Thévenaz, P., Blu, T., & Unser, M. (2000). Image interpolation and resampling. In I. N. Bankman (Ed.), Handbook of Medical Imaging: Processing and Analysis (Vol. 1, pp. 393–420). Academic Press. https://doi.org/10.1016/B978-0-12-373904-9.X0001-4 DOI: https://doi.org/10.1016/B978-012077790-7/50030-8
Thornton, H. (2001). Screening for breast cancer with mammography. The Lancet, 358(9299), 2167–2168. https://doi.org/10.1016/S0140-6736(01)07193-8 DOI: https://doi.org/10.1016/S0140-6736(01)07198-7
Yang, W., Zhang, S., Chen, Y., Li, W., & Chen, Y. (2009). Shape symmetry analysis of breast tumors on ultrasound images. Computers in Biology and Medicine, 39, 231–238. https://doi.org/10.1016/j.compbiomed.2008.12.007 DOI: https://doi.org/10.1016/j.compbiomed.2008.12.007
Yu, W., Zhu, Q., Zheng, N., Huang, J., Zhou, M., & Zhao, F. (2023). Learning Non-Uniform-Sampling for Ultra-High-Definition Image Enhancement. MM ’23: Proceedings of the 31st ACM International Conference on Multimedia, 1412–1421. https://doi.org/10.1145/3581783.3611836 DOI: https://doi.org/10.1145/3581783.3611836
Zare, M. R., Seng, W. C., & Mueen, A. (2013). Automatic classification of medical X-ray images. Malaysian Journal of Computer Science, 26(1), 9–22. https://doi.org/10.22452/mjcs.vol26no1.2 DOI: https://doi.org/10.22452/mjcs.vol26no1.2
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Rafael Tokairin, Rafael Furlanetto Casamaximo, Neyva Maria Lopes Romeiro, Pedro Zaffalon da Silva, Eliandro Rodrigues Cirilo
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The Copyright Declaration for articles published in this journal is the author's right. Since manuscripts are published in an open access Journal, they are free to use, with their own attributions, in educational and non-commercial applications. The Journal has the right to make, in the original document, changes regarding linguistic norms, orthography, and grammar, with the purpose of ensuring the standard norms of the language and the credibility of the Journal. It will, however, respect the writing style of the authors. When necessary, conceptual changes, corrections, or suggestions will be forwarded to the authors. In such cases, the manuscript shall be subjected to a new evaluation after revision. Responsibility for the opinions expressed in the manuscripts lies entirely with the authors.
This journal is licensed with a license Creative Commons Attribution-NonCommercial 4.0 International.
