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Sitoloji preperatlarının görüntü işlenmesi için bir araç olarak dalgacık analizi metodolojisi

Year 2016, Volume: 41 Issue: 3, 453 - 463, 30.09.2016
https://doi.org/10.17826/cukmedj.237468

Abstract

Amaç: Bu çalışmanın amacı sitoloji preperatlarının görüntülerinin işlenmesinde dalgacık analizlerinin kullanılma olasılığını belirlemektir.

Gereç ve Yöntem: Sitoloji preparatlarının farklı bir görüntü seti, kontrastlarindaki değişimler ve dalgacık analizlerinin metolojik uygulamalarıyla analiz edildi.

Bulgular: Sitoloji preparat görüntülerinin işlenmesi prosedürü geliştirildi. Sitolojik preparat görüntü işlenmesi prosedürü kalitatif olarak (görüntüleme açısından) birçok yapının tanımlanmasına izin verir: hücre yapısının analizi, hücre görüntülerinin yapısal özellikleri, hücre sınırları, ve hücre nukleuslari. 

Sonuç: Sitolojik preparatların görüntüleme işlenmesi için dalgacık analizlerinin uygulanabilirligi ve yapılabilirliginin düşünülmesi. Bu işlem sitolojik preparatların görüntüleme analizi kalitesini iyileştirir dolayısıyla bu da daha uygun şekilde tanıya olanak tanıyacaktır.

References

  • Schlüter S, Sheppard A, Brown, K, Wildenschild D. Image processing of multiphase images obtained via X‐ray microtomography: a review. Water Resour Res. 2014;50:3615-39.
  • Gaemperli O, Shalhoub J, Owen D, Lamare F, Rimoldi OE, Davies AH et al. Imaging intraplaque inflammation in carotid atherosclerosis with 11C- PK11195 positron emission tomography/computed tomography. Eur Heart J. 2012;33:1902-10.
  • Sikdar S, Rangwala H, Eastlake EB, Hunt I, Nelson AJ, Devanathan J et al. Novel method for predicting dexterous individual finger movements by imaging muscle activity using a wearable ultrasonic system. IEEE Trans Neural Syst Rehabil Eng. 2014;22:69-76.
  • Eklund A, Dufort P, Forsberg D, LaConte SM. Medical image processing on the GPU–Past, present and future. Med Image Anal. 2013;17:1073-94.
  • Ciresan D, Giusti A, Gambardella LM, Schmidhuber J. Deep neural networks segment neuronal membranes in electron microscopy images. dv Neural Inf Process Syst. 2012:2843-51.
  • Saha M, Agarwal S, Arun I, Ahmed R, Chatterjee S, Mitra P et al Histogram based thresholding for automated nucleus segmentation using breast imprint cytology. Advancements of Medical Electronics. 2015:49-57.
  • Mahendran G, Babu R, Sivakumar D. Automatic segmentation and classification of pap smear cells. International Journal of Management, IT and Engineering. 2014;4:100-8.
  • Singh S, Gupta R. Identification of components of fibroadenoma in cytology preparations using texture analysis: a morphometric study. Cytopathology. 2012;23:187-191.
  • Ensink E, Sinha J, Sinha A, Tang H, Calderone HM, Hostetter G, Haab BB. Segment and fit thresholding: a new method for image analysis applied to microarray and immunofluorescence data. Anal Chem. 2015;87:9715-21.
  • George YM, Bagoury BM, Zayed HH, Roushdy MI. Automated cell nuclei segmentation for breast fine needle aspiration cytology. Signal Processing. 2013;93:2804-16.
  • Malviya R, Karri SPK, Chatterjee J, Manjunatha M, Ray AK. Computer assisted cervical cytological nucleus localization. TENCON 2012-2012 IEEE Region 10 Conference. IEEE, 2012:1-5.
  • van Ingen EM, Leyte-Veldstra L, Al I, Wielenga G, Ploem IS. Automated cytology using a quantitative staining method combined with a TV-based image analysis computer. cancer control: Proceedings of the 12th International Cancer Congress, Buenos Aires, 1978. Elsevier. 2013:45-67.
  • Dey N, Ashour AS, Ashour AS, Singh A. Digital analysis of microscopic images in medicine. Journal of Advanced Microscopy Research. 2015;10:1-13.
  • Kobylin O, Lyashenko V. Comparison of standard image edge detection techniques and of method based on wavelet transform. Int J Adv Res (Indore). 2014;2:572-80.
  • Lyashenko V, Deineko Z, Ahmad A. Properties of wavelet coefficients of self-similar time series. Int J Eng Sci Res. 2015;6:1492-9.
  • Lyashenko V, Kobylin O, Ahmad MA. General methodology for implementation of image normalization procedure using its wavelet transform. Int J Sci Res (Raipur). 2014;3:2870-7.
  • Kingsbury N. Image processing with complex wavelets. Philos Trans A Math Phys Eng Sci. 1999;357:2543-60.
  • Heil CE, Walnut DF. Continuous and discrete wavelet transforms. SIAM Rev Soc Ind Appl Math. 1989;31:628-66.
  • Tourneur Y, Espinosa L. Histochemical and Cytochemical Methods of Visualization. Boca Raton, CRC Press. 2013.
  • Pise AP, Longadge R, Malik LG. Segmentation of nuclei in cytological images of breast FNAC sample: case study. International Journal of Computer Science and Mobile Computing. 2014;3:226-32.
  • Al-Kofahi Y, Lassoued W, Lee W, Roysam B. Improved automatic detection and segmentation of cell nuclei in histopathology images. IEEE Trans Biomed Eng 2010;57:841-52.
  • Nedzved A, Ablameyko S, Pitas I. Morphological segmentation of histology cell images. In Pattern Recognition, 2000. Proceedings. 15th International Conference on. 2000;1:500-3.
  • Chaabane SB, Fnaiech F. Color edges extraction using statistical features and automatic threshold technique: application to the breast cancer cells. Biomed Eng. 2014;13:1-18.

The methodology of wavelet analysis as a tool for cytology preparations image processing

Year 2016, Volume: 41 Issue: 3, 453 - 463, 30.09.2016
https://doi.org/10.17826/cukmedj.237468

Abstract


Abstract

Purpose: The aim of this study was to determine the possibility of using wavelet analysis for processing images of cytology preparations.

Material and Methods: A set of different images of cytology preparations were analyzed through changes in their contrast and application of the methodology of the wavelet analysis.

Results: Developed procedure of processing of cytology preparations images. Procedure of processing of cytology preparations images allows to qualitatively (in terms of their visualization) allocating: cells’ edges, cell nuclei, revealing in more detail textural features of cells’ images, which allows analyzing cell structure.

Conclusion: Consider the possibility and feasibility issues of applying wavelet analysis for processing cytology preparations images. This improves the quality of the analysis of cytology preparations images. This allows the to properly diagnose.

References

  • Schlüter S, Sheppard A, Brown, K, Wildenschild D. Image processing of multiphase images obtained via X‐ray microtomography: a review. Water Resour Res. 2014;50:3615-39.
  • Gaemperli O, Shalhoub J, Owen D, Lamare F, Rimoldi OE, Davies AH et al. Imaging intraplaque inflammation in carotid atherosclerosis with 11C- PK11195 positron emission tomography/computed tomography. Eur Heart J. 2012;33:1902-10.
  • Sikdar S, Rangwala H, Eastlake EB, Hunt I, Nelson AJ, Devanathan J et al. Novel method for predicting dexterous individual finger movements by imaging muscle activity using a wearable ultrasonic system. IEEE Trans Neural Syst Rehabil Eng. 2014;22:69-76.
  • Eklund A, Dufort P, Forsberg D, LaConte SM. Medical image processing on the GPU–Past, present and future. Med Image Anal. 2013;17:1073-94.
  • Ciresan D, Giusti A, Gambardella LM, Schmidhuber J. Deep neural networks segment neuronal membranes in electron microscopy images. dv Neural Inf Process Syst. 2012:2843-51.
  • Saha M, Agarwal S, Arun I, Ahmed R, Chatterjee S, Mitra P et al Histogram based thresholding for automated nucleus segmentation using breast imprint cytology. Advancements of Medical Electronics. 2015:49-57.
  • Mahendran G, Babu R, Sivakumar D. Automatic segmentation and classification of pap smear cells. International Journal of Management, IT and Engineering. 2014;4:100-8.
  • Singh S, Gupta R. Identification of components of fibroadenoma in cytology preparations using texture analysis: a morphometric study. Cytopathology. 2012;23:187-191.
  • Ensink E, Sinha J, Sinha A, Tang H, Calderone HM, Hostetter G, Haab BB. Segment and fit thresholding: a new method for image analysis applied to microarray and immunofluorescence data. Anal Chem. 2015;87:9715-21.
  • George YM, Bagoury BM, Zayed HH, Roushdy MI. Automated cell nuclei segmentation for breast fine needle aspiration cytology. Signal Processing. 2013;93:2804-16.
  • Malviya R, Karri SPK, Chatterjee J, Manjunatha M, Ray AK. Computer assisted cervical cytological nucleus localization. TENCON 2012-2012 IEEE Region 10 Conference. IEEE, 2012:1-5.
  • van Ingen EM, Leyte-Veldstra L, Al I, Wielenga G, Ploem IS. Automated cytology using a quantitative staining method combined with a TV-based image analysis computer. cancer control: Proceedings of the 12th International Cancer Congress, Buenos Aires, 1978. Elsevier. 2013:45-67.
  • Dey N, Ashour AS, Ashour AS, Singh A. Digital analysis of microscopic images in medicine. Journal of Advanced Microscopy Research. 2015;10:1-13.
  • Kobylin O, Lyashenko V. Comparison of standard image edge detection techniques and of method based on wavelet transform. Int J Adv Res (Indore). 2014;2:572-80.
  • Lyashenko V, Deineko Z, Ahmad A. Properties of wavelet coefficients of self-similar time series. Int J Eng Sci Res. 2015;6:1492-9.
  • Lyashenko V, Kobylin O, Ahmad MA. General methodology for implementation of image normalization procedure using its wavelet transform. Int J Sci Res (Raipur). 2014;3:2870-7.
  • Kingsbury N. Image processing with complex wavelets. Philos Trans A Math Phys Eng Sci. 1999;357:2543-60.
  • Heil CE, Walnut DF. Continuous and discrete wavelet transforms. SIAM Rev Soc Ind Appl Math. 1989;31:628-66.
  • Tourneur Y, Espinosa L. Histochemical and Cytochemical Methods of Visualization. Boca Raton, CRC Press. 2013.
  • Pise AP, Longadge R, Malik LG. Segmentation of nuclei in cytological images of breast FNAC sample: case study. International Journal of Computer Science and Mobile Computing. 2014;3:226-32.
  • Al-Kofahi Y, Lassoued W, Lee W, Roysam B. Improved automatic detection and segmentation of cell nuclei in histopathology images. IEEE Trans Biomed Eng 2010;57:841-52.
  • Nedzved A, Ablameyko S, Pitas I. Morphological segmentation of histology cell images. In Pattern Recognition, 2000. Proceedings. 15th International Conference on. 2000;1:500-3.
  • Chaabane SB, Fnaiech F. Color edges extraction using statistical features and automatic threshold technique: application to the breast cancer cells. Biomed Eng. 2014;13:1-18.
There are 23 citations in total.

Details

Subjects Health Care Administration
Journal Section Research
Authors

Vyacheslav V. Lyashenko This is me

Asaad Mohammed Ahmed Abdallah Babker

Oleg A. Kobylin This is me

Publication Date September 30, 2016
Published in Issue Year 2016 Volume: 41 Issue: 3

Cite

MLA Lyashenko, Vyacheslav V. et al. “The Methodology of Wavelet Analysis As a Tool for Cytology Preparations Image Processing”. Cukurova Medical Journal, vol. 41, no. 3, 2016, pp. 453-6, doi:10.17826/cukmedj.237468.