Automated segmentation of the epidermis area in skin whole slide histopathological images

Automated segmentation of the epidermis area in skin whole slide histopathological images

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With the development of high-speed, high-resolution whole slide histology digital scanners, glass slides of tissue specimen can now be digitised at high magnification to create the whole slide image. Quantitative image analysis tools are then desirable to help the pathologist for their routine examination. Epidermis area is a very important observation area for the cancer diagnosis. Therefore, in order to build up a computer-aided diagnosis system, segmentation of the epidermis area is often the very first and crucial step. An improved computer-aided epidermis segmentation technique for the whole slide skin histopathological image is proposed in this study. The proposed technique first obtains an initial segmentation result with the help of global thresholding and shape analysis. A template matching method, with adaptive template intensity value, is then applied. Finally, a threshold is calculated based on the probability density function of the response value image. Experimental results show that the proposed technique overcomes the limitation of the existing technique and provides superior performance, with sensitivity of 95.68%, specificity of 99.41% and precision of 93.13%. The performance of the proposed technique is satisfactory for future clinical use.


    1. 1)
    2. 2)
      • 2. Mccarthy, S.W., Scolyer, R.A.: ‘Melanocytic lesions of the face: diagnostic pitfalls, Ann. Acad. Med., Singapore, 2004, 33, (4), pp. 314.
    3. 3)
    4. 4)
    5. 5)
    6. 6)
      • 6. Cruz-Roa, A., Basavanhally, A., González, F., et al: ‘Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks, March 2014, 9041, (216), p. 904103.
    7. 7)
    8. 8)
    9. 9)
      • 9. Weedon, D., Strutton, G.: ‘Skin pathology’ (Churchill Livingstone, New York, 2002), vol. 430.
    10. 10)
      • 10. Lu, C., Mandal, M.: ‘Automated segmentation and analysis of the epidermis area in skin histopathological images’. 2012 Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC), 2012, pp. 53555359.
    11. 11)
      • 11. Lu, C., Mahmood, M., Jha, N., Mandal, M.: ‘A robust automatic nuclei segmentation technique for quantitative histopathological image analysis’, Anal. Quant. Cytol. Histopathol., 2012, 12, pp. 296308.
    12. 12)
    13. 13)
    14. 14)
    15. 15)
    16. 16)
      • 16. Gonzalez, R., Woods, R.: ‘Digital image processing’, (Wiley, New York, 2002).
    17. 17)
    18. 18)
      • 18. Naik, S., Doyle, S., Agner, S., Madabhushi, A., Feldman, M., Tomaszewski, J.: ‘Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology’. Proc. Fifth IEEE Int. Symp. Biomedical Imaging: From Nano to Macro ISBI 2008, 2008, pp. 284287.
    19. 19)
    20. 20)
      • 20. Lewis, J.: ‘Fast normalized cross-correlation’. in Vision Interface (Citeseer, Quebec City, Canada, 1995), vol. 10, pp. 120123.
    21. 21)
      • 21. Niethammer, M., Borland, D., Marron, J.S., et al: ‘Appearance normalisation of histology slides’, Machine Learning in Medical Imaging, (Springer Berlin Heidelberg, 2010), pp. 5866.

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