Literature DB >> 26922612

Curvelet initialized level set cell segmentation for touching cells in low contrast images.

Sarabpreet Kaur1, J S Sahambi2.   

Abstract

Cell segmentation is an important element of automatic cell analysis. This paper proposes a method to extract the cell nuclei and the cell boundaries of touching cells in low contrast images. First, the contrast of the low contrast cell images is improved by a combination of multiscale top hat filter and h-maxima. Then, a curvelet initialized level set method has been proposed to detect the cell nuclei and the boundaries. The image enhancement results have been verified using PSNR (Peak Signal to noise ratio) and the segmentation results have been verified using accuracy, sensitivity and precision metrics. The results show improved values of the performance metrics with the proposed method.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Cell segmentation; Curvelets; Level sets; Multiscale top hat transform; h-maxima

Mesh:

Year:  2016        PMID: 26922612     DOI: 10.1016/j.compmedimag.2016.01.002

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  1 in total

1.  Segmentation and classification of two-channel C. elegans nucleus-labeled fluorescence images.

Authors:  Mengdi Zhao; Jie An; Haiwen Li; Jiazhi Zhang; Shang-Tong Li; Xue-Mei Li; Meng-Qiu Dong; Heng Mao; Louis Tao
Journal:  BMC Bioinformatics       Date:  2017-09-15       Impact factor: 3.169

  1 in total

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