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  • An Integration Strategy based on Fuzzy Clustering and Level Set Method for Cell Image Segmentation

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    92119_1.pdf (190.6Kb)
    Author(s)
    Gharipour, A
    Liew, AWC
    Griffith University Author(s)
    Liew, Alan Wee-Chung
    Year published
    2013
    Metadata
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    Abstract
    In this study a new image segmentation framework which combines the Fuzzy c means clustering and the level set method is presented. Using this framework, the well-known Chan and Vese's level set technique and classical Bayes classifier are employed to obtain a prior membership value for each pixel based on region information. Next, a novel clustering model based on fuzzy c-mean clustering assisted by prior membership values is used to obtain the final segmentation. Experiments performed on high-throughput fluorescence microscopy colon cancer cell images, which are commonly utilized for the study of many normal and neoplastic ...
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    In this study a new image segmentation framework which combines the Fuzzy c means clustering and the level set method is presented. Using this framework, the well-known Chan and Vese's level set technique and classical Bayes classifier are employed to obtain a prior membership value for each pixel based on region information. Next, a novel clustering model based on fuzzy c-mean clustering assisted by prior membership values is used to obtain the final segmentation. Experiments performed on high-throughput fluorescence microscopy colon cancer cell images, which are commonly utilized for the study of many normal and neoplastic procedures, indicate a significant improvement in accuracy when compared to several existing techniques.
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    Conference Title
    2013 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2013
    Publisher URI
    http://www.ieee.org.hk/icspcc2013/
    DOI
    https://doi.org/10.1109/ICSPCC.2013.6664081
    Copyright Statement
    © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Computer vision
    Publication URI
    http://hdl.handle.net/10072/58728
    Collection
    • Conference outputs

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