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Multi-Pass Fast Watershed for Accurate Segmentation of Overlapping Cervical Cells
- Publication Year :
- 2018
- Publisher :
- IEEE, 2018.
-
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.
- Subjects :
- Watershed
Computer science
02 engineering and technology
Cervix Uteri
Ellipse
Accurate segmentation
030218 nuclear medicine & medical imaging
Image (mathematics)
03 medical and health sciences
0302 clinical medicine
Image Interpretation, Computer-Assisted
0202 electrical engineering, electronic engineering, information engineering
Humans
Segmentation
Electrical and Electronic Engineering
Cell Nucleus
Radiological and Ultrasound Technology
business.industry
Pattern recognition
Image segmentation
Cervical cells
Computer Science Applications
020201 artificial intelligence & image processing
Female
Artificial intelligence
business
Software
Algorithms
Papanicolaou Test
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....f56e5498f1a6afb297e07d806e37b769