Multi-Pass Fast Watershed for Accurate Segmentation of Overlapping Cervical Cells

Abstract

The task of segmenting cell nuclei and cytoplasm in pap smear images is one of the most challenging tasks in automated cervix cytological analysis due to specifically the presence of overlapping cells. This paper introduces a multi-pass fast watershed-based method (MPFW) to segment both nucleus and cytoplasm from large cell masses of overlapping cervical cells in three watershed passes. The first pass locates the nuclei with barrier-based watershed on the gradient-based edge map of a pre-processed image. The next pass segments the isolated, touching, and partially overlapping cells with a watershed transform adapted to the cell shape and location. The final pass introduces mutual iterative watersheds separately applied to each nucleus in the largely overlapping clusters to estimate the cell shape. In MPFW, the line-shaped contours of the watershed cells are deformed with ellipse fitting and contour adjustment to give a better representation of cell shapes. The performance of the proposed method has been evaluated using synthetic, real extended depth-of-field, and multi-layers cervical cytology images provided by the first and second overlapping cervical cytology image segmentation challenges in ISBI 2014 and ISBI 2015. The experimental results demonstrate superior performance of the proposed MPFW in terms of segmentation accuracy, detection rate, and time complexity, compared with recent peer methods.

Segmentation of Overlapped Cervical Cells Using Asymmetric Mixture Model and Shape Constraint Level Set Method

“…Tareef et al [13] turned to incorporate SLIC [14] with support vector machine (SVM) to get nucleus and cytoplasm boundaries, and they applied sparse coding (SC) theory and some morphological processing to reconstruct every cell's boundary. Recently, multipass watershed method [15] was also used in overlapping cervical cell segmentation, which applied different thresholds to get different regions-of-interest (ROIs). In addition, Chang et al [16] introduced a method which incorporates morphology techniques with double threshold to detect cervical cells, and it was able to segment every cell in a short time.…”

Automated Detection of Cervical Cancer

“…Afaf Tareef et al(2018), projected a method for the segmentation of the overlapping cells [3]. This paper uses a three pass fast water shed based method for segmenting the overlapping cells.…”

Nucleus segmentation of cervical cytology images based on multi-scale fuzzy clustering algorithm

“…Many scholars have proposed different methods for nucleus segmentation based on the ISBI2014 and ISBI2015 public datasets. The algorithms for nucleus segmentation are mainly divided into simple linear iterative clustering (SLIC) method [5][6][7][8], region-based segmentation method [7,[9][10][11][12][13], convolutional neural network (CNN) [14][15][16][17], and clustering method [18][19][20][21][22][23][24][25][26][27][28]. SLIC superpixel algorithm is one of the most popular nucleus segmentation methods currently.…”

“…Afterward, the boundary map was utilized to extract cell clumps and candidate nuclei by the Gaussian mixture model and maximally stable extremal region (MSER) algorithm. Tareef et al [8] applied a triangular transformation algorithm to identify cell clumps, and combined the SLIC algorithm with a marker-based watershed algorithm to extract candidate nuclei. The SLIC algorithm can accurately segment consistent size targets.…”