Automatic Method for Segmentation of Fluorescent Images Obtained in the Near-Infrared Region
https://doi.org/10.32603/1993-8985-2022-25-6-40-49
Abstract
Introduction. Near-infrared fluorescence imaging technology is widely used in laparoscopic surgery. Intraoperative fluorescence navigation is based on accurate segmentation of fluorescent regions in near-infrared images (NIR images), thus increasing the accuracy and safety of surgical intervention. Moreover, it is an important auxiliary technology for laparoscopic surgery. Therefore, the search for an automatic method that allows for accurate segmentation of fluorescent regions in NIR images can contribute to an improved efficiency of intraoperative navigation.
Aim. Development of a method for automatic segmentation of fluorescent images obtained in the near infrared range.
Materials and methods. The proposed method consists of two stages. At the first stage, a preliminary segmentation of the image is performed based on the adaptive threshold found by Otsu’s method. At the second stage, the segmented area is refined using Otsu’s weighted method. The main advantage of the proposed method consists in the automatic determination of parameter α, which determines the performance of Otsu’s weighted method. Experiments were carried out using 276 actual laparoscopic images. The metric misclassification error (ME) was used to assess the quality of segmentation.
Results. The average ME of the proposed method was found to be 10.4 %, compared to that obtained by the conventional Otsu’s method of 27.1 %.
Conclusion. In comparison with Otsu’s method, the developed method shows an increased efficiency and accuracy of fluorescent image segmentation. This allows for a higher diagnostic accuracy and a more efficient navigation during laparoscopic surgery.
Keywords
automatic segmentation,
threshold segmentation,
Otsu’s method,
fluorescent laparoscopic images,
digital image processing
Supporting agencies: The study was realized with the support of the China Scholarship Council (grant no. 202009010036).
About the Authors
N. A. Obukhova
Saint Petersburg Electrotechnical University
Russian Federation
Russian Federation
Nataliia A. Obukhova, Dr Sci. (Eng.) (2009), Head of Television and Video Equipment Department
5 F, Professor Popov St., St Petersburg 197022
Xin Yang
Saint Petersburg Electrotechnical University
Russian Federation
Russian Federation
Xin Yang, Master on Radio Engineering (2020), Postgraduate Student at Television and Video Equipment Department
5 F, Professor Popov St., St Petersburg 197022
References
1. Zhu B., Sevick-Muraca E. M. A Review of Performance of Near-Infrared Fluorescence Imaging Devices Used in Clinical Studies. The British J. of Radiology. 2015, vol. 88, no. 1045, p. 20140547. doi: 10.1259/bjr.20140547
2. Boni L., David G., Mangano A., Dionigi G., Rausei S., Spampatti S., Cassionotti E., Fingerhut A. Clinical Applications of Indocyanine Green (ICG) Enhanced Fluorescence in Laparoscopic Surgery. Surgical Endoscopy. 2015, vol. 29, no. 7, pp. 2046–2055. doi: 10.1007/s00464-014-3895-x
3. Schaafsma B. E., Mieog J. S. D., Hutteman M., Vorst J. R., Kuppen P. J. K., Löwik C. W. G. M., Frangioni J. V., Velde C. J. H., Vahrmeijer A. L. The Clinical Use of Indocyanine Green as a Near – Infrared Fluorescent Contrast Agent for Image – Guided Oncologic Surgery. J. of Surgical Oncology. 2011, vol. 104, no. 3, pp. 323–332. doi: 10.1002/jso.21943
4. Nishino H., Hatano E., Seo S., Nitta T., Saito T., Nakamura M., Hattori K., Takatani M., Fuji H., Taura K., Uemoto Sh. Real-Time Navigation for Liver Surgery Using Projection Mapping with Indocyanine Green Fluorescence: Development of the Novel Medical Imaging Projection System. Annals of Surgery. 2018, vol. 267, no. 6, pp. 1134–1140. doi: 10.1097/SLA.0000000000002172
5. Marshall M. V., Rasmussen J. C., Tan I.-Ch., Aldrich M. B., Adams K. E., Wang X., Fife C. E., Maus E. A., Smith L. A., Sevick-Muraca E. M. Near-Infrared Fluorescence Imaging in Humans with Indocyanine Green: a Review and Update. Open Surgical Oncology J. 2010, vol. 2, no. 2, pp. 12–25. doi: 10.2174/1876504101002010012
6. Bali A., Singh S. N. A Review on the Strategies and Techniques of Image Segmentation. 5th Intern. Conf. on Advanced Computing & Communication Technologies, Haryana, India, 21–22 Feb. 2015. Piscataway, IEEE, 2015, pp. 113–120. doi: 10.1109/ACCT.2015.63
7. Qiao W, Wu C. Weighting Otsu's Segmentation Method and Its Fuzzy Theory Explanation. Computer Engineering. 2009, vol. 10, pp. 211–213. doi: 10.3969/j.issn.1000-3428.2009.10.070 (in Chinese)
8. Yuan X., Wu L., Peng Q. An Improved Otsu Method Using the Weighted Object Variance for Defect Detection. Applied Surface Science. 2015, vol. 349, pp. 472–484. doi: 10.1016/j.apsusc.2015.05.033
9. Zhang J., Hu J. Image Segmentation Based on 2D Otsu Method with Histogram Analysis. Intern. Conf. on Computer Science and Software Engineering. Wuhan, China, 12–14 Sept. 2008. Piscataway, IEEE, 2008, vol. 6, pp. 105–108. doi: 10.1109/CSSE.2008.206
10. Feng Y., Zhao H., Li X., Zhang X., Li H. A Multi-Scale 3D Otsu Thresholding Algorithm for Medical Image Segmentation. Digital Signal Processing. 2017, vol. 60, pp. 186–199. doi: 10.1016/j.dsp.2016.08.003
11. Salem N., Malik H., Shams A. Medical Image Enhancement Based on Histogram Algorithms. Procedia Computer Science. 2019, vol. 163, pp. 300–311. doi: 10.1016/j.procs.2019.12.112
12. Otsu N. A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics. 1979, vol. 9, no. 1, pp. 62–66. doi: 10.1109/TSMC.1979.4310076
13. Sezgin M., Sankur B. Survey over Image Thresholding Techniques and Quantitative Performance Evaluation. J. of Electronic Imaging. 2004, vol. 13, no. 1, pp. 146–165. doi: 10.1117/1.1631315
14. Li X., Lovell J. F., Yoon J., Chen X. Clinical Development and Potential of Photothermal and Photo-dynamic Therapies for Cancer. Nature Reviews Clinical Oncology. 2020, vol. 17, no. 11, pp. 657–674. doi: 10.1038/s41571-020-0410-2
15. Choi M. C., Jung S. G., Park H., Lee S. Y., Lee C., Hwang Y. Y., Kim S. J. Photodynamic Therapy for Management Of Cervical Intraepithelial Neoplasia II and III in Young Patients and Obstetric Outcomes. Lasers in Surgery and Medicine. 2013, vol. 45, no. 9, pp. 564–572. doi: 10.1002/lsm.22187
16. Otdelnova O. B., Khashukoeva A. Z., Ibragimova M. I. Photodynamic therapy with photodytazin in treatment of gynecologic diseases. Russian J. of Bio-therapy. 2008, vol. 7, no. 4, pp. 47–52. (In Russ.)
Review
For citations:
Obukhova N.A., Yang X. Automatic Method for Segmentation of Fluorescent Images Obtained in the Near-Infrared Region. Journal of the Russian Universities. Radioelectronics. 2022;25(6):40-49. (In Russ.) https://doi.org/10.32603/1993-8985-2022-25-6-40-49
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