Adaptive Local Thresholding

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  • Topic: Image processing, Color space, Thresholding
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  • Published : May 1, 2011
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Adaptive Local Thresholding for Detection of Nuclei in Diversity Stained Cytology Images Neerad Phansalkart Ashish Sabale Sumit More Department of Electronics and Telecommunication College of Engineering Pune, Pune, India. Madhuri Joshi

Abstract-Accurate cell nucleus segmentation is necessary for automated cytological image analysis. Thresholding is a crucial step in segmentation. The accuracy of segmentation depends on the accuracy of thresholding. In this paper we propose a new method for thresholding of photomicrographs of diversly stained cytology smears. To account for the different stains, we use different color spaces. A new local thresholding scheme is developed to solve the problem of nonuniform staining. Finally, the results obtained from the new method are compared with those of some of the existing thresholding methods, clearly showing the improvement achieved.

irrespective of the stain used. To account for the different stains, the images are first preprocessed using different color spaces. II. MATERIALS AND METHODS

A. Image Specifications and Acquisition
The images used in this study are cytology smears of cancerous as well as noncancerous tissues of various organs in the human body. Cancerous specimens were specifically taken because the malignant nuclei have some irregularities, and the developed method should work irrespective of these irregularities. The magnification of all the images is 400. The smears are stained in either H & E stain, Pap stain, or Giemsa stain. B.

Keywordscytology; thresholding; color spaces






Segmentation of cell nuclei is a fundamental subject of quantitative analysis of cytological and histological images. As many of the characteristics of a cell are contained in the nucleus, the separation of nuclei from the background is an important part of segmentation for this kind of cell images. This separation of the nuclei from the background is done through either global or local thresholding. In the analysis of cytology images, determination of threshold is a particularly difficult problem to solve, because of the diversity of structures contained in these images and the intensity variations of the background and foreground caused by uneven staining. Also, different stains are used for staining cytology smears, which makes the design of a generalized segmentation scheme difficult. In view of the above problems, some thresholding methods have been proposed in the past [1]. The optimum global thresholding scheme proposed by Otsu may well be applied to the case of cytology images [2]. However, being a global scheme, this method cannot account for the local variations in the intensity caused by nonuniform staining. Thus, due to nonuniform staining, a global thresholding scheme is inappropriate for the case of cytology images. The methods of local thresholding are quite well developed in the field of document analysis, unlike the field of cytological image analysis. Numerous methods about the former can be found in literature [3]. Thus, there is a need to develop a thresholding method for the later, for accurate segmentation. Starting from a local thresholding method proposed by Sauvola and Pietikainen in the field of document image analysis [4], we develop a method to suit the needs of our problem. Further, in view of a generalized system for analysis of cytology images, the developed method should work

Stains and Color Spaces

In this section, we discuss the preprocessing step of the algorithm. However, before going into the details of the preprocessing, the different stains used in cytology and their characteristics are briefly presented. Pathologists use different stains for staining different cytological smears (specimens). This is because some tissues are stained better in some stains than others. The three major stains used in cytology are Hematoxylin and Eosin (H & E)...
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