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Color disease leaf image segmentation using NAMS superpixel algorithm

BACKGROUND: Disease leaf segmentation in color image is used to explore the disease shape and lesion regions. It is of great significance for pathological diagnosis and pathological research. OBJECTIVE: This paper proposes a superpixel algorithm using Non-symmetry and Anti-packing Model with Squares...

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Detalles Bibliográficos
Autores principales: Li, Hua, Chen, Chuanbo, Zhao, Shengrong, Lyu, Zehua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IOS Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6004959/
https://www.ncbi.nlm.nih.gov/pubmed/29689757
http://dx.doi.org/10.3233/THC-174525
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author Li, Hua
Chen, Chuanbo
Zhao, Shengrong
Lyu, Zehua
author_facet Li, Hua
Chen, Chuanbo
Zhao, Shengrong
Lyu, Zehua
author_sort Li, Hua
collection PubMed
description BACKGROUND: Disease leaf segmentation in color image is used to explore the disease shape and lesion regions. It is of great significance for pathological diagnosis and pathological research. OBJECTIVE: This paper proposes a superpixel algorithm using Non-symmetry and Anti-packing Model with Squares (NAMS) for color image segmentation of leaf disease. METHODS: First of all, the NAMS model is presented for color leaf disease image representation. The model can segment images asymmetrically and preserve the characteristics of image context. Second, NAMS based superpixel (NAMS superpixel) algorithm is proposed for clustering pixels, which can represent large homogeneous areas by super squares. By this way, the impact of complex background and the data redundancy in image segmentation can be reduced. RESULTS: Experimental results indicate that compared with segmenting the original image directly and manipulating by Simple Linear Iterative Clustering (SLIC) superpixel, the proposed NAMS superpixel performs more excellently in not only saving storage but also adhering to the lesion region edge. CONCLUSIONS: The outcome of NAMS superpixel can be regarded as a preprocess procedure for leaf disease region detection since the method can segment the image into superpixel blocks and preserve the lesion area.
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spelling pubmed-60049592018-06-25 Color disease leaf image segmentation using NAMS superpixel algorithm Li, Hua Chen, Chuanbo Zhao, Shengrong Lyu, Zehua Technol Health Care Research Article BACKGROUND: Disease leaf segmentation in color image is used to explore the disease shape and lesion regions. It is of great significance for pathological diagnosis and pathological research. OBJECTIVE: This paper proposes a superpixel algorithm using Non-symmetry and Anti-packing Model with Squares (NAMS) for color image segmentation of leaf disease. METHODS: First of all, the NAMS model is presented for color leaf disease image representation. The model can segment images asymmetrically and preserve the characteristics of image context. Second, NAMS based superpixel (NAMS superpixel) algorithm is proposed for clustering pixels, which can represent large homogeneous areas by super squares. By this way, the impact of complex background and the data redundancy in image segmentation can be reduced. RESULTS: Experimental results indicate that compared with segmenting the original image directly and manipulating by Simple Linear Iterative Clustering (SLIC) superpixel, the proposed NAMS superpixel performs more excellently in not only saving storage but also adhering to the lesion region edge. CONCLUSIONS: The outcome of NAMS superpixel can be regarded as a preprocess procedure for leaf disease region detection since the method can segment the image into superpixel blocks and preserve the lesion area. IOS Press 2018-05-29 /pmc/articles/PMC6004959/ /pubmed/29689757 http://dx.doi.org/10.3233/THC-174525 Text en © 2018 – IOS Press and the authors. All rights reserved https://creativecommons.org/licenses/by-nc/4.0/ This article is published online with Open Access and distributed under the terms of the Creative Commons Attribution Non-Commercial License (CC BY-NC 4.0).
spellingShingle Research Article
Li, Hua
Chen, Chuanbo
Zhao, Shengrong
Lyu, Zehua
Color disease leaf image segmentation using NAMS superpixel algorithm
title Color disease leaf image segmentation using NAMS superpixel algorithm
title_full Color disease leaf image segmentation using NAMS superpixel algorithm
title_fullStr Color disease leaf image segmentation using NAMS superpixel algorithm
title_full_unstemmed Color disease leaf image segmentation using NAMS superpixel algorithm
title_short Color disease leaf image segmentation using NAMS superpixel algorithm
title_sort color disease leaf image segmentation using nams superpixel algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6004959/
https://www.ncbi.nlm.nih.gov/pubmed/29689757
http://dx.doi.org/10.3233/THC-174525
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