Abstract
Computer vision systems for automated breast cancer diagnosis using Fine Needle Aspiration Cytology (FNAC) images are under development for a while now. Accurate segmentation of the nuclei in microscopic images is crucial for functioning of these systems, as most quantify and analyze nuclear features for diagnosis. This paper presents a nucleus segmentation system (NSS) involving pre-processing, pre-segmentation and refined segmentation stages. The NSS includes a novel pixel transformation step to create a high contrast grayscale representation of the input color image. The grayscale image gives NSS the capability- to disregard elements that mimic nuclear morphological and luminescence characteristics, and to minimize effects of non-specific staining of cytoplasm by Hematoxylin. Experimental results illustrate generalizability of the NSS to use multiple refined segmentation techniques and particularly achieve accurate nucleus segmentation using active contours without edges(F-score > 0.92). The paper also presents the results of experiments conducted to study the impact of image pre-processing steps on the NSS performance. The pre-processing steps are observed to improve accuracy and consistency across tested refined segmentation techniques.
H. Garud—Special Thanks: Texas Instruments Inc. and Dr. Arindam Ghosh for their continued support.
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Notes
- 1.
In this paper symbols \(\text {f}\) and \(\mathbf f \) are used to represent single channel and multi-channel images.
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Garud, H. et al. (2017). Methods and System for Segmentation of Isolated Nuclei in Microscopic Breast Fine Needle Aspiration Cytology Images. In: Mukherjee, S., et al. Computer Vision, Graphics, and Image Processing. ICVGIP 2016. Lecture Notes in Computer Science(), vol 10481. Springer, Cham. https://doi.org/10.1007/978-3-319-68124-5_33
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