Abstract
In cervical cancer screening, accurate segmentation of cervical nucleus is a key part in the early diagnosis of cervical cancer. However, the cervical nucleus segmentation faces many great challenges owing to the overlapping cervical cells, uneven staining and poor contrast of cervical cytology smear images. In this paper, a tree domain structure and screening algorithm based on depth-first searching strategy are proposed to obtain candidate nucleus regions according to the annular clustering characteristics of nucleus depth information in cervical cytology images. Then, the candidate nucleus regions are finely segmented with an iterative level set algorithm based on adaptive radius morphological dilation. Experimental results are evaluated on the ISBI2015 public dataset. The performance of the proposed nucleus segmentation algorithm is higher than that of the state-of-the-art methods in terms of positive predictive value, negative predictive value, precision, recall of the cervical nucleus segmentation.
Highlights
Cervical cancer is one of the four common cancers in the world
The main contributions of our method presented in this paper are as follows: 1) compared with other nucleus segmentation algorithms, the proposed algorithm directly uses the depth information of the cervical cytology samples rather than the extended depth of field (EDF) images; 2) through constructing the tree domain structure of annular features after clustering of depth information, the nuclei can be accurately identified; 3) a novel iterative level set algorithm based on adaptive radius morphological dilation is proposed, which can find the optimal dilation radius adaptively and carry out the nucleus segmentation results accurately
NUCLEUS APPROXIMATE SEGMENTATION According to the annular structure generated in the nucleus defocusing process, we propose a nucleus segmentation algorithm based on the clustering of nucleus depth information
Summary
Cervical cancer is one of the four common cancers in the world. 266,000 people die of cervical cancer each year [1], [2]. The early canceration of cervical cells has no visible symptoms of physical malaise or physiological reaction that are perceptible. The PAP test is an effective means of detecting early cervical carcinogenesis through manual screening. Screening of cervical cancer cell is a time-consuming task and highly repetitive work that requires the utmost attention of a pathologist. Even an experienced pathologist may have screening errors due to fatigue and decreased attention after long-term working. A computer-aided diagnosis (CAD) system is needed to assist pathologists in the segmentation and identification of cervical cells
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