Automatic Detection of Proliferative Cells in Immunohistochemically Images of Meningioma Using Fuzzy C-Means Clustering and HSV Color Space

Authors: Vahid Anari, Mina Bakhshi

Abstract:

Visual search and identification of immunohistochemically stained tissue of meningioma was performed manually in pathologic laboratories to detect and diagnose the cancers type of meningioma. This task is very tedious and time-consuming. Moreover, because of cell's complex nature, it still remains a challenging task to segment cells from its background and analyze them automatically. In this paper, we develop and test a computerized scheme that can automatically identify cells in microscopic images of meningioma and classify them into positive (proliferative) and negative (normal) cells. Dataset including 150 images are used to test the scheme. The scheme uses Fuzzy C-means algorithm as a color clustering method based on perceptually uniform hue, saturation, value (HSV) color space. Since the cells are distinguishable by the human eye, the accuracy and stability of the algorithm are quantitatively compared through application to a wide variety of real images.

Keywords: Positive cell, color segmentation, HSV color space, immunohistochemistry, meningioma, thresholding, fuzzy c-means.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3566417

References:

[1] DN. Louis, H. Budka, and A. Von Demling, “Meningiomas” , in Pathology and geneticsy of tumors the nervous system, P. Kleihues and WK. Cavenee, Eds. World health organization, classifications of tumors. IARC press, 1993, pp. 134-141.
[2] WT. Longstreth, LK. Dennis, VM. McGuire, MT. Drangsholt, and TD. Koepsell, “Epidemiology of intracranial meningioma cancer”, vol.72, pp. 639-648, 1993.
[3] DN. Louis, BW. Scheithaver, H. Budka, A. Vondeimling, and JJ. Keppes, “Meningiomas” in Pathology and genetics of tumors the nervous system, Kleihues P, Cavenee WK, Eds. WHO classification of tumor. IARC press, 2000, pp.176-184.
[4] A, Perry. "Meningiomas." Practical surgical neuropathology: a diagnostic approach. Elsevier, 2018. 259-298.‏
[5] L, Lian Tao, et al. “Ki67 is a promising molecular target in the diagnosis of cancer” Molecular medicine reports, 2015, 11.3: 1566-1572.‏
[6] C, GRAEFE et al. “Optimized Ki-67 staining in murine cells: a tool to determine cell proliferation”. Molecular biology reports, 2019, 1-13.‏
[7] J, Oscanoa, et al. "Automated segmentation and classification of cell nuclei in immunohistochemical breast cancer images with estrogen receptor marker." 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2016.‏
[8] T. Wurflinger, J. Stocjkausen, D. Meyer-Ebrecht, and A. Bocking, “Robust automatic coregistration, segmentation, and classificationof cell nuclei in multimodal cytopathological microscopic images,” Compurterized Medical Imaging ang Graphics.
[9] P, Kennedy "Patched Completed Local Binary Pattern is an Effective Method for Neuroblastoma Histological Image Classification." Data Mining: 15th Australasian Conference, Aus DM 2017, Melbourne, VIC, Australia, August 19-20, 2017,
[10] A, Juan Eloy et al. "Digital image analysis for automatic enumeration of malaria parasites using morphological operations." Expert Systems with Applications 42.6 (2015): 3041-3047. ‏
[11] Pa, Yongsheng, et al. "Cell image segmentation using bacterial foraging optimization." Applied Soft Computing 58 (2017): 770-782. ‏
[12] A. Koschan and A. Abidi, Digital Image Processing, John Willey and Sons, 2008
[13] J.C. Bezdek, J. Keller, and N.R. Pal, “fuzzy models and algorithms for pattern recognition and image processing,”.Norwell,1999.