An Approach to Cell Nuclei Counting in Histological Image Analysis
The paper describes a technique for automated cell nuclei counting. In this study, the primary goal is to provide simple and effective automated scheme of cell nuclei counting. The experiments on public data set of histology images have demonstrated accep
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Abstract. The paper describes a technique for automated cell nuclei counting. In this study, the primary goal is to provide simple and effective automated scheme of cell nuclei counting. The experiments on public data set of histology images have demonstrated acceptable level of calculation results. Keywords: Histological analysis counting
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Machine analysis
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Cell nuclei
Introduction and Motivation
The most important and rapidly developing areas in the field of visualization and control of microscopic objects is development of automated computer vision systems. Computer analysis of microscopy cell images has many real-life applications in a wide range of areas including diagnosis of a disease, morphological cell analysis and statistics [1]. It plays an important role in biomedical research and bioinformatics. Microscopic analysis approach is particularly important in solving the following problems: malignant transformation of normal cells and cancer cell detection, morphological changes in cells, dynamic changes in the cells during therapeutic procedures [2]. For that reason, automatic counting of cell nuclei is a key block in systems for microscopic analysis of cell images. Since 1970s automated methods of histological analysis have been developed [1,2]. Detection of cellular structures and cell counting are common tasks for many investigations. Some researchers have applied thresholding for cell counting in the investigated images. It is a simple way for separating objects of interest from the background. Another approach is to segment images using different edge detection (LoG filter, Laplacian filtering, etc.). However, detection and segmentation of cell nuclei is a challenging task, since the cells have a complex and a nonuniform structure. Another feature of histological image is non-uniform illumination, which also applies limitations on the use of standard approaches. In recent years many methods have been proposed for cell nuclei segmentation, separation and classification in histological analysis. More sophisticated c IFIP International Federation for Information Processing 2016 Published by Springer International Publishing Switzerland 2016. All Rights Reserved K. Saeed and W. Homenda (Eds.): CISIM 2016, LNCS 9842, pp. 139–147, 2016. DOI: 10.1007/978-3-319-45378-1 13
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M. Lukashevich and V. Starovoitov
approaches consist of several image processing stages. These methods are based on traditional image processing algorithms like adaptive contour model, watershed, morphological operations, k-means, Support vector machine, etc. [3–12]. Recent works suggest combining different approaches for increasing performance. Several excellent reviews about methods for nuclei detection, segmentation and classification can be found in [1,2]. The main methods for cell nuclei segmentation, separation and classification are presented in Table 1. Table 1. Short summary of state-of-the-art cell nuclei segmentation, separation and classification Solvable task Methods Segmentation Adaptive contour model, adaptive thresh
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