Abstract
Development of an accurate and automated algorithm to completely segment cervical cells in Pap images is still one of the most challenging tasks. The main reasons are the presence of overlapping cells and the lack of guiding mechanism for the convergence of ill-defined contours to the actual cytoplasm boundaries. In this paper, we propose a novel method to address these problems based on level set method (LSM). Firstly, we proposed a morphological scaling-based topology filter (MSTF) and derived a new mathematical toolbox about vector calculus for evolution of level set function (LSF). Secondly, we combine MSTF and the mathematical toolbox into a multifunctional filtering algorithm 2D codimension two-object level set method (DCTLSM) to split touching cells. The DCTLSM can morphologically scale up and down the contour while keeping part of the contour points fixed. Thirdly, we design a contour scanning strategy as the evolution method of LSF to segment overlapping cells. In this strategy, a cutting line can be detected by morphologically scaling the union LSF of the pairs of cells. Then, we used this cutting line to construct a velocity field with an effective guiding mechanism for attracting and repelling LSF. The performance of the proposed algorithm was evaluated quantitatively and qualitatively on the ISBI-2014 dataset. The experimental results demonstrated that the proposed method is capable of fully segmenting cervical cells with superior segmentation accuracy compared with recent peer works.
Highlights
Cervical cancer, which is primarily caused by the infection of some types of HumanPapilloma Virus (HPV), is one of the most common gynecological cancer in the world and has a very high probability of fatality if it is left untreated [1,2,3]
Instead of the conventional crossing time [68] and fast marching method (FMM) [69], we used a linear time Euclidean distance transform (LTEDT) algorithm [62] based on dimensionality reduction and partial Voronoi diagram construction to calculate the L2 norm distance, which was used to construct the signed distance function (SDF) as the level set function (LSF) of the initial contour
To make the fixed contour inactive and perform morphological scaling on the active contour, we proposed a novel evolution strategy DCTLSM incited by the idea of codimension twolevel set method (CTLSM) [45]
Summary
Cervical cancer, which is primarily caused by the infection of some types of Human. Papilloma Virus (HPV), is one of the most common gynecological cancer in the world and has a very high probability of fatality if it is left untreated [1,2,3]. It is essential to develop a new system that can commendably carry out automated segmentation of overlapping cytoplasm for cervical cells in the Pap images. With the rapid improvement of the graphic processing unit (GPU), CNNs (convolutional neural networks) have been widely used in the field of medical image segmentation These methods mainly focus on clinical parameters, such as the volume and shape obtained from organ and structure segmentation for quantitative analysis and organ substructure obtained from lesion segmentation for histopathological diagnosis [55]. The main goal of the proposed method is to alleviate nonconvergence of the level set function (LSF) in delineating the overlapping regions of cells caused by poor initialization of the cellular contours in [33]. By qualitive and quantitative comparisons, our method outperformed the other segmentation methods
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