2015/05/21 by S L Happy, Happy, S L, Swarnadip Chatterjee +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · #Artificial intelligence #Biology #Cell #Cell Image Analysis Techniques #Cell biology #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #Cytoplasm #Digital Imaging for Blood Diseases #FOS: Computer and information sciences #Genetics #Image Processing Techniques and Applications #Image segmentation #Nucleus #Pattern recognition (psychology) #Scale-space segmentation #Segmentation #cs.CV
paper · pdf · doi:10.48550/arxiv.1505.05601
2 pages, 2 figures
arxiv created 2015/05/21 · openalex publication_date 2015/05/21 · arxiv updated 2015/05/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Overlapping of cervical cells and poor contrast of cell cytoplasm are the major issues in accurate detection and segmentation of cervical cells. An unsupervised cell segmentation approach is presented here. Cell clump segmentation was carried out using the extended depth of field (EDF) image created from the images of different focal planes. A modified Otsu method with prior class weights is proposed for accurate segmentation of nuclei from the cell clumps. The cell cytoplasm was further segmented from cell clump depending upon the number of nucleus detected in that cell clump. Level set model was used for cytoplasm segmentation.