Abstract
In immunohistochemistry (IHC), on the condition that the antigens to be tested exist in nuclei, the segmentation of the positive and negative stainings in nuclei is the base of the qualitative, quantitative, and positioning research for the function of specific protein. But the IHC image segmentation algorithm with fixed parameters will produce unstable segmentation results when IHC images have color cast or overall brightness variation. Though artificial interpretation can improve segmentation results, it is influenced by subjective factors and is inefficient. With the analysis of the S-P staining characteristics, this paper presents a simple and clear two-stage segmentation method for positive staining in nuclei. Firstly, the background and nuclei are separated from the image according to their luminance in YIQ space, and then, the positive and negative stainings in nuclei are distinguished according to their hue in HSV space. And it is proved by real experiments that this method can achieve adaptive processing for IHC images with color cast or overall brightness variation, etc.
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Acknowledgment
The authors would like to thank the National Natural Science Foundation of China (grant No.: 61075033, 61273248), the Natural Science Foundation of Guangdong Province (S2011010003348), and the Open Project Program of the National Laboratory of Pattern Recognition (NLPR) (201001060) for their support.
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© 2014 Springer-Verlag Berlin Heidelberg
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Yang, T., Peng, W., Li, X., Wang, Y. (2014). A Robust IHC Color Image Automatic Segmentation Algorithm. In: Wen, Z., Li, T. (eds) Foundations of Intelligent Systems. Advances in Intelligent Systems and Computing, vol 277. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54924-3_30
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DOI: https://doi.org/10.1007/978-3-642-54924-3_30
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