The cell overlapping and adhesion phenomenon often exists in cell image processing. Separating overlapped cell into single ones is of great important and difficult in cell image quantitative analysis and automatic recognition. In this paper, an algorithm based on concave region extraction and erosion limit has been proposed to judge and separate overlapping cell images. Experimental results show that the proposed algorithm has a good separation effects on analog cell images. Then the method is applying in actual cervical cell image and obtains good separation result.
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.
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
Seeing from the overlapping cell images shown in