1. Introduction
Worldwide, especially in middle and low income countries, cervical cancer is the second most common cancer in women, and the third most frequent cause of cancer death, accounting for nearly 300,000 deaths annually. But cervical cancer is more preventable than others because it has a very long time precancerous stage and can be easily detected by a routine screening test. Cervical smear screening is the most popular method to detect the cervical pre-cancers and cancer from the cell abnormalities. However, the conventional manual screening methods are costly and mainly rely on the pathologist subjective experiences, which always result in inaccurate diagnosis. Therefore, it is necessary to develop the automated cervical smear screening analysis system to assist the diagnosis of cervical cancer.
While because of the slice-making, staining techniques and image collection means differences, the overlapping and adhesion phenomenon often appears in cervical cell images. The clustered cell will affect the following quantitative analysis and automatic recognition of cervical cell image. Separating the adherent cells into single ones is a great important and difficult task in cervical cell image processing. According to different characteristics of image, some researchers proposed some methods to process the overlapping cell [1-6], which contained gray scale threshold, region growing method, mathematical morphology, watershed algorithm, and edge detection method and so on. But cervical smear images are frequently con-taminated and the contrast between cell nucleus and cytoplasm is lower, which makes the contours of nuclei and cytoplasm very vague especially for the abnormal cells. So these methods can’t separate the overlapping cervical cell images effectively. In this paper, we propose a separating algorithm according to the concavity and convexity of overlap cell and limit erosion. Linking the separating dotted pair which constructed by concave points we can separate single cells from overlapped cells. The experiment result shows that the algorithm can separate the cell cluster successfully. The method we proposed in this paper is aim to the overlapping cells on a single plane, so the up and down overlapping is not considered in this paper.
2. Overlapping Judgment
2.1. Overlapping Cell Images Category
Before the separation of cell images into cell nuclei and cell cytoplasm, we should judge firstly whether the cells are overlapping or not. For cell image, the overlapping judgment is focus on cell body or cell nucleus, so we set the extracted cell body’s or cell nucleus’s pixel value with 1 and the background value with 0 and we can get a binary image. To simplify the procedure, we first investigate the analog cell images and then separate the actual overlapping cell images.
For most cell images, the overlapping cell may be classified into 3 categories: series cell, parallel cell and series parallel cell. Series cell represent the cells connected head and tail and didn’t form a closed region, while parallel cell represent the cells lapped with other two cells, in most cases the overlapping part is a closed region, in some special cases there is a distinct hole in the overlapping part. Series and parallel cell include series and parallel overlapping. The overlapping cell images are showing in Figure 1. In this paper, we are mainly discussing the series cell and parallel cell separation algorithm.
2.2. Overlapping Cell Image Judgment
Seeing from the overlapping cell images shown in Figure 1, we can find that no matter what kind of overlap there are concave phenomena in the overlapped zone and there exists at least two concave points. We can judge the overlap by the number of concave points which can be described by convexity closure easily. Define concave regions as the domains that subtract the original cell from its convexity closure. For a single cell, its convexity closure is itself and there is no concave region. For overlapping cell there are more than two concave regions. So we can judge the cell overlap by the numbers of concave regions, which shows in Figure 2.
Figure 3 shows the concave regions of series cell and parallel cell. Figure 3(a) is a 2-series cell which composed by two cells, two concave regions are obtained by convexity closure and are show in Figure 3(b). Figure 3(c) is a 3-series cell which composed by 3 cells, while four concave regions are obtained by subtracting Figure 3(c) from its convexity closure. So we can get a conclusion that for series cell, if the number of overlapping cells is n, then the number of concave regions is 2n − 2. Figures 3(e) and (g) are 3-parallel and 4-parallel cells which composed by 3 and 4 overlapping cells respectively, the numbers of concave regions are 3 and 4. We can estimate that for parallel cell which has closed region, if the number of overlapping cells is n, then the number of concave regions is n too. While for the parallel cell with a hole inside in the procedure of concave regions extraction we didn’t take the inner blank region into account, so we can get the additional inner blank region except for the concave regions. In this condition if the number of overlapping cells is n, then the number of ex-