Adaptive Morphological Reconstruction for Seeded Image Segmentation

Morphological reconstruction (MR) is often employed by seeded image segmentation algorithms such as watershed transform and power watershed, as it is able to filter out seeds (regional minima) to reduce over-segmentation. However, the MR might mistakenly filter meaningful seeds that are required for...

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Veröffentlicht in:	IEEE transactions on image processing Jg. 28; H. 11; S. 5510 - 5523
Hauptverfasser:	Tao Lei, Xiaohong Jia, Tongliang Liu, Shigang Liu, Hongying Meng, Nandi, Asoke K.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.11.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Computer science Computing time Convergence Image edge detection Image reconstruction Image segmentation Mathematical morphology Morphology seeded segmentation Seeds spectral segmentation Transforms
ISSN:	1057-7149, 1941-0042, 1941-0042
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Abstract	Morphological reconstruction (MR) is often employed by seeded image segmentation algorithms such as watershed transform and power watershed, as it is able to filter out seeds (regional minima) to reduce over-segmentation. However, the MR might mistakenly filter meaningful seeds that are required for generating accurate segmentation and it is also sensitive to the scale because a single-scale structuring element is employed. In this paper, a novel adaptive morphological reconstruction (AMR) operation is proposed that has three advantages. First, AMR can adaptively filter out useless seeds while preserving meaningful ones. Second, AMR is insensitive to the scale of structuring elements because multiscale structuring elements are employed. Finally, the AMR has two attractive properties: monotonic increasingness and convergence that help seeded segmentation algorithms to achieve a hierarchical segmentation. Experiments clearly demonstrate that the AMR is useful for improving performance of algorithms of seeded image segmentation and seed-based spectral segmentation. Compared to several state-of-the-art algorithms, the proposed algorithms provide better segmentation results requiring less computing time.
AbstractList	Morphological reconstruction (MR) is often employed by seeded image segmentation algorithms such as watershed transform and power watershed, as it is able to filter out seeds (regional minima) to reduce over-segmentation. However, the MR might mistakenly filter meaningful seeds that are required for generating accurate segmentation and it is also sensitive to the scale because a single-scale structuring element is employed. In this paper, a novel adaptive morphological reconstruction (AMR) operation is proposed that has three advantages. First, AMR can adaptively filter out useless seeds while preserving meaningful ones. Second, AMR is insensitive to the scale of structuring elements because multiscale structuring elements are employed. Finally, the AMR has two attractive properties: monotonic increasingness and convergence that help seeded segmentation algorithms to achieve a hierarchical segmentation. Experiments clearly demonstrate that the AMR is useful for improving performance of algorithms of seeded image segmentation and seed-based spectral segmentation. Compared to several state-of-the-art algorithms, the proposed algorithms provide better segmentation results requiring less computing time. Morphological reconstruction (MR) is often employed by seeded image segmentation algorithms such as watershed transform and power watershed, as it is able to filter out seeds (regional minima) to reduce over-segmentation. However, the MR might mistakenly filter meaningful seeds that are required for generating accurate segmentation and it is also sensitive to the scale because a single-scale structuring element is employed. In this paper, a novel adaptive morphological reconstruction (AMR) operation is proposed that has three advantages. First, AMR can adaptively filter out useless seeds while preserving meaningful ones. Second, AMR is insensitive to the scale of structuring elements because multiscale structuring elements are employed. Finally, the AMR has two attractive properties: monotonic increasingness and convergence that help seeded segmentation algorithms to achieve a hierarchical segmentation. Experiments clearly demonstrate that the AMR is useful for improving performance of algorithms of seeded image segmentation and seed-based spectral segmentation. Compared to several state-of-the-art algorithms, the proposed algorithms provide better segmentation results requiring less computing time.Morphological reconstruction (MR) is often employed by seeded image segmentation algorithms such as watershed transform and power watershed, as it is able to filter out seeds (regional minima) to reduce over-segmentation. However, the MR might mistakenly filter meaningful seeds that are required for generating accurate segmentation and it is also sensitive to the scale because a single-scale structuring element is employed. In this paper, a novel adaptive morphological reconstruction (AMR) operation is proposed that has three advantages. First, AMR can adaptively filter out useless seeds while preserving meaningful ones. Second, AMR is insensitive to the scale of structuring elements because multiscale structuring elements are employed. Finally, the AMR has two attractive properties: monotonic increasingness and convergence that help seeded segmentation algorithms to achieve a hierarchical segmentation. Experiments clearly demonstrate that the AMR is useful for improving performance of algorithms of seeded image segmentation and seed-based spectral segmentation. Compared to several state-of-the-art algorithms, the proposed algorithms provide better segmentation results requiring less computing time. Morphological reconstruction (MR) is often employed by seeded image segmentation algorithms such as watershed transform and power watershed as it is able to filter seeds (regional minima) to reduce over-segmentation. However, MR might mistakenly filter meaningful seeds that are required for generating accurate segmentation and it is also sensitive to the scale because a single-scale structuring element is employed. In this paper, a novel adaptive morphological reconstruction (AMR) operation is proposed that has three advantages. Firstly, AMR can adaptively filter useless seeds while preserving meaningful ones. Secondly, AMR is insensitive to the scale of structuring elements because multiscale structuring elements are employed. Finally, AMR has two attractive properties: monotonic increasingness and convergence that help seeded segmentation algorithms to achieve a hierarchical segmentation. Experiments clearly demonstrate that AMR is useful for improving algorithms of seeded image segmentation and seed-based spectral segmentation. Compared to several state-of-the-art algorithms, the proposed algorithms provide better segmentation results requiring less computing time.
Author	Nandi, Asoke K. Tongliang Liu Shigang Liu Hongying Meng Xiaohong Jia Tao Lei
Author_xml	– sequence: 1 surname: Tao Lei fullname: Tao Lei email: leitao@sust.edu.cn organization: Sch. of Electron. Inf. & Artificial Intell., Shaanxi Univ. of Sci. & Technol., Xi'an, China – sequence: 2 surname: Xiaohong Jia fullname: Xiaohong Jia email: jiaxhsust@163.com organization: Sch. of Electr. & Control Eng., Shaanxi Univ. of Sci. & Technol., Xi'an, China – sequence: 3 surname: Tongliang Liu fullname: Tongliang Liu email: tongliang.liu@sydney.edu.au organization: Sch. of Comput. Sci., Univ. of Sydney, Sydney, NSW, Australia – sequence: 4 surname: Shigang Liu fullname: Shigang Liu email: shgliu@gmail.com organization: Sch. of Comput. Sci., Shaanxi Normal Univ., Xi'an, China – sequence: 5 surname: Hongying Meng fullname: Hongying Meng email: hongying.meng@brunel.ac.uk organization: Dept. of Electron. & Comput. Eng., Brunel Univ. London, Uxbridge, UK – sequence: 6 givenname: Asoke K. surname: Nandi fullname: Nandi, Asoke K. email: asoke.nandi@brunel.ac.uk organization: Dept. of Electron. & Comput. Eng., Brunel Univ. London, Uxbridge, UK
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Snippet	Morphological reconstruction (MR) is often employed by seeded image segmentation algorithms such as watershed transform and power watershed, as it is able to... Morphological reconstruction (MR) is often employed by seeded image segmentation algorithms such as watershed transform and power watershed as it is able to...
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SubjectTerms	Algorithms Computer science Computing time Convergence Image edge detection Image reconstruction Image segmentation Mathematical morphology Morphology seeded segmentation Seeds spectral segmentation Transforms
Title	Adaptive Morphological Reconstruction for Seeded Image Segmentation
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