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Detection of cancer in breast thermograms using mathematical threshold based segmentation and morphology technique

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Abstract

The breast thermography measure is a physiological examination that gives information subject to the warmth varieties in the breast. Breast thermography is a physiological test that gives data dependent on the temperature changes in the breast. It accounts for the temperature spread of a human body in the exposure of the infrared radiation released through the outer side of that body. Precancerous tissue along with the surrounded zone around a risky tumor experiences higher temperature due to angiogenesis, and higher compound and vein activity than a standard breast thus breast thermography can identify early irregular variations in breast tissues. It may perceive the essential sign of building out sickness before mammography can distinguish. In this paper, the author derives the mathematical threshold-based methodology is reasonable, famously utilized in the segmentation strategy, and also Develop an algorithm that defines how well our technique is figuring out the hottest region and mark a ridgeline on hottest suspected regions.

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References

  • Alpar O, Krejcar O (2018) Detection of irregular thermoregulation in hand thermography by fuzzy C-means. In: International conference on bioinformatics and biomedical engineering. Springer, Cham, 10814, pp 255–265

  • Bouaynaya N, Schonfeld D (2008) Theoretical foundations of spatially-variant mathematical morphology part II: gray-level images. IEEE Trans Pattern Anal Mach Intell 30(5):837–850

    Article  Google Scholar 

  • Czech Society for Oncology. National Oncology Program. Available online: https://www.linkos.cz/narodnionkologicky-program/ Accessed 24 Aug 2018

  • Devarriya D, Gulati C, Mansharamani V, Sakalle A, Bhardwaj A (2020) Unbalanced breast cancer data classification using novel fitness functions in genetic programming. Expert Syst Appl 140:112866

    Article  Google Scholar 

  • Jaglan P, Dass R, Duhan M (2019) Breast cancer detection techniques: issues and challenges. J Institut Eng (India) Series B 100:379–386

    Article  Google Scholar 

  • Kandlikar SG, Perez-Raya I, Raghupathi PA, Gonzalez-Hernandez JL, Dabydeen D, Medeiros L, Phatak P (2017) Infrared imaging technology for breast cancer detection–current status protocols and new directions. Int J Heat Mass Transf 108:2303–2320

    Article  Google Scholar 

  • Khan AA, Arora AS (2018) Breast cancer detection through gabor filter based texture features using thermograms images. In: 2018 First international conference on secure cyber computing and communication (ICSCCC) IEEE, 412–417

  • Köşüş N, Köşüş A, Duran M, Simavlı S, Turhan N (2010) Comparison of standard mammography with digital mammography and digital infrared thermal imaging for breast cancer screening. J Turk German Gynecol Assoc 11(3):152

    Article  Google Scholar 

  • Li Y, Li Z, Zhu Y, Li B, Xiong W, Huang Y (2019) Thermal infrared small ship detection in sea clutter based on morphological reconstruction and multi-feature analysis. Appl Sci 9(18):3786

    Article  Google Scholar 

  • Mambou SJ, Maresova P, Krejcar O, Selamat A, Kuca K (2018a) Breast cancer detection using infrared thermal imaging and a deep learning model. Sensors 18(9):2799

    Article  Google Scholar 

  • Mambou S, Krejcar O, Maresova P, Selamat A, Kuca K (2019b) Novel hand gesture alert system. Appl Sci 9(16):3419

    Article  Google Scholar 

  • Mambou S, Maresova P, Krejcar O, Selamat A, Kuca K (2018) Breast cancer detection using modern visual IT techniques. In: Modern approaches for intelligent information and database systems. Springer, Cham, 769, 397–407

  • Mambou S, Krejcar O, Maresova P, Selamat A, Kuca K (2019) Novel four stages classification of breast cancer using infrared thermal imaging and a deep learning model. In: International work-conference on bioinformatics and biomedical engineering. Springer, Cham, 11466, 63–74

  • Mehdy MM, Ng PY, Shair EF, Saleh NI, Gomes C (2017) Artificial neural networks in image processing for early detection of breast cancer. Comput Math Methods Med 2017:1–15

    Article  Google Scholar 

  • Milosevic M, Jankovic D, Peulic A (2014) Thermography based breast cancer detection using texture features and minimum variance quantization. EXCLI J 13:1204

    Google Scholar 

  • Milosevic M, Jankovic D, Peulic A (2015) Comparative analysis of breast cancer detection in mammograms and thermograms. Biomed Eng\Biomedizinische Technik 60:49–56

    Google Scholar 

  • Milosevic M, Jankovic D, Milenkovic A, Stojanov D (2018) Early diagnosis and detection of breast cancer. Technol Health Care 26(4):729–759

    Article  Google Scholar 

  • Nobel TB, Dave N, Eljalby M, Xing X, Barbetta A, Hsu M, Jones DR (2020) Incidence and risk factors for isolated esophageal cancer recurrence to the brain. Ann Thorac Surg 109(2):329–336

    Article  Google Scholar 

  • Pandey D, Yin X, Wang H, Su MY, Chen JH, Wu J, Zhang Y (2018) Automatic and fast segmentation of breast region-of-interest (ROI) and density in MRIs. Heliyon 4(12):01042

    Article  Google Scholar 

  • Peter SC, Wenkel E, Weiland E, Dietzel M, Janka R, Hartmann A, Ellmann S (2020) Combination of an ultrafast TWIST-VIBE Dixon sequence protocol and diffusion-weighted imaging into an accurate easily applicable classification tool for masses in breast MRI. Euro Radiol 30:2761–2771

    Article  Google Scholar 

  • Singh J, Arora AS (2019) Automated approaches for ROIs extraction in medical thermography a review and future directions. Multimedia Tools Appl, 1–24

  • Unar-Munguía M, Meza R, Colchero MA, Torres-Mejía G, de Cosío TG (2017) Economic and disease burden of breast cancer associated with suboptimal breastfeeding practices in Mexico. Cancer Causes Control 28(12):1381–1391

    Article  Google Scholar 

  • Wu WJ, Lin SW, Moon WK (2012) Combining support vector machine with genetic algorithm to classify ultrasound breast tumor images. Comput Med Imaging Graph 36(8):627–633

    Article  Google Scholar 

  • Zhang X, He S, Ding B, Qu C, Zhang Q, Chen H, Lan X (2020) Cancer cell membrane-coated rare earth doped nanoparticles for tumor surgery navigation in NIR-II imaging window. Chem Eng J 385:123959

    Article  Google Scholar 

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Acknowledgements

The first author would like to thank Delhi Technical Campus, Greater Noida, Uttar Pradesh, and Dr.Shiradha Gupta, MBBS Kasturba medical college Mangalore (Manipal University) Karnataka, M.S Cristian Medical College Ludhiana, Punjab for his benevolent help to complete this work.

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Correspondence to Kumod Kumar Gupta.

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Gupta, K.K., Rituvijay, Pahadiya, P. et al. Detection of cancer in breast thermograms using mathematical threshold based segmentation and morphology technique. Int J Syst Assur Eng Manag 13, 421–428 (2022). https://doi.org/10.1007/s13198-021-01289-3

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