Automated Segmentation of Cervical Cells Using MSER Algorithm and Gradient Embedded Cost Function-Based Level-Set Method

Author(s):  
Kaushiki Roy ◽  
Debotosh Bhattacharjee ◽  
Mita Nasipuri
INMIC ◽  
2013 ◽  
Author(s):  
Ammara Masood ◽  
Adel Ali Al Jumaily ◽  
Azadeh Noori Hoshyar ◽  
Omama Masood

2021 ◽  
Vol 11 (1) ◽  
pp. 443
Author(s):  
Guangqi Liu ◽  
Qinghai Ding ◽  
Haibo Luo ◽  
Moran Ju ◽  
Tianming Jin ◽  
...  

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.


2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yiming Huang ◽  
Hongqing Zhu

Accuracy segmentation of the nuclei and cytoplasm in Pap smear images is challenging in cervix cytological analysis. In this paper, a new fusion algorithm combining the asymmetric generalized Gaussian and Cauchy mixture model (GGCMM) with a shape constraint level set method to segment overlapping cervical smear cells is put forward. The proposed approach starts by separating nuclei and cytoplasm cluster through asymmetric GGCMM, where each component is a mixture of generalized Gaussian distribution and Cauchy distribution. The proposed asymmetric GGCMM takes into account the asymmetry of generalized Gaussian distribution and the heavier tail of Cauchy distribution. New probability distribution fits different shapes of observed data more flexibly. Then, we apply the morphological operation to remove fake nuclei which is usually much smaller than real nuclei. After that, the improved level set energy function with a distance map and a new shape prior term are applied to extract the contours of overlapping cervical cells. Due to this new level set energy function, the segmentation of every individual cell worked well, especially in overlapping areas. We evaluate the proposed method by using the ISBI 2014 Challenge Dataset. The results demonstrate that our approach outperforms existing methods in extracting overlapping cervical cells and obtains accurate cell contours.


Author(s):  
Luis Fernando Segalla ◽  
Alexandre Zabot ◽  
Diogo Nardelli Siebert ◽  
Fabiano Wolf

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