An iterative possibilistic knowledge diffusion approach for blind medical image segmentation.
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| Title: | An iterative possibilistic knowledge diffusion approach for blind medical image segmentation. |
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| Authors: | Khanfir Kallel, I.1,2 imen.khanfir.kallel@gmail.com, Almouahed, S.2, Solaiman, B.2 basel.solaiman@imt-atlantique.fr, Bossé, É.2,3 |
| Source: | Pattern Recognition. Jun2018, Vol. 78, p182-197. 16p. |
| Subjects: | Diagnostic imaging, Image segmentation, Diffusion, Pattern recognition systems, Fuzzy logic, Probability theory |
| Abstract: | This paper presents an image segmentation method imitating human focusing visual attention in image interpretation using possibilistic knowledge modeling concepts. The proposed pixel level method consists on the Iterative Possibilistic Knowledge Diffusion (IPKD) on immediate neighbourhood pixels. The advantage of this mechanism is to provide iterative diffusion of per-pixel certain knowledge to surrounding pixels in order to progressively refine the segmentation process. The diffusion process is achieved using image smoothing techniques such as Nagao and Gabor filtering, mean filtering and anisotropic diffusion. Those diffusion techniques are then compared in the possibilistic knowledge representation space. The merit of a possibilistic knowledge representation, rather than a grey-level sensor based representation, is demonstrated by both experimental and synthetic data. Producing the lowest error rates, possibilistic knowledge diffusion using Nagao filter is adopted for the approach assessment. Experimental results using synthetic images as well as mammographic images from MIAS (Mammographic Image Analysis Society) data-base, are performed in order to assess the efficiency of the proposed segmentation method according to the visual criterion as well as some quantitative criteria. IPKD's performance (in terms of recognition rate, 94.37% and global predictive rate, 92.18%) is compared with three relevant reference methods: level-set, Fuzzy C-Mean and region growing methods. The IPKD approach outperforms the other three methods, respectively, at the recognition rates of 89.77%, 84.43% and 88.11% and at the global predictive rates of 87.86%, 89.72% and 84.04%. Noise-sensitivity experiments have been conducted on synthetic as well as on real images. The proposed IPKD approach outperforms the three reference methods and in addition, exhibits a desired stability behaviour. [ABSTRACT FROM AUTHOR] |
| Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 128166516 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An iterative possibilistic knowledge diffusion approach for blind medical image segmentation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Khanfir+Kallel%2C+I%2E%22">Khanfir Kallel, I.</searchLink><relatesTo>1,2</relatesTo><i> imen.khanfir.kallel@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Almouahed%2C+S%2E%22">Almouahed, S.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Solaiman%2C+B%2E%22">Solaiman, B.</searchLink><relatesTo>2</relatesTo><i> basel.solaiman@imt-atlantique.fr</i><br /><searchLink fieldCode="AR" term="%22Bossé%2C+É%2E%22">Bossé, É.</searchLink><relatesTo>2,3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Pattern+Recognition%22">Pattern Recognition</searchLink>. Jun2018, Vol. 78, p182-197. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Diagnostic+imaging%22">Diagnostic imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Diffusion%22">Diffusion</searchLink><br /><searchLink fieldCode="DE" term="%22Pattern+recognition+systems%22">Pattern recognition systems</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+logic%22">Fuzzy logic</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+theory%22">Probability theory</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper presents an image segmentation method imitating human focusing visual attention in image interpretation using possibilistic knowledge modeling concepts. The proposed pixel level method consists on the Iterative Possibilistic Knowledge Diffusion (IPKD) on immediate neighbourhood pixels. The advantage of this mechanism is to provide iterative diffusion of per-pixel certain knowledge to surrounding pixels in order to progressively refine the segmentation process. The diffusion process is achieved using image smoothing techniques such as Nagao and Gabor filtering, mean filtering and anisotropic diffusion. Those diffusion techniques are then compared in the possibilistic knowledge representation space. The merit of a possibilistic knowledge representation, rather than a grey-level sensor based representation, is demonstrated by both experimental and synthetic data. Producing the lowest error rates, possibilistic knowledge diffusion using Nagao filter is adopted for the approach assessment. Experimental results using synthetic images as well as mammographic images from MIAS (Mammographic Image Analysis Society) data-base, are performed in order to assess the efficiency of the proposed segmentation method according to the visual criterion as well as some quantitative criteria. IPKD's performance (in terms of recognition rate, 94.37% and global predictive rate, 92.18%) is compared with three relevant reference methods: level-set, Fuzzy C-Mean and region growing methods. The IPKD approach outperforms the other three methods, respectively, at the recognition rates of 89.77%, 84.43% and 88.11% and at the global predictive rates of 87.86%, 89.72% and 84.04%. Noise-sensitivity experiments have been conducted on synthetic as well as on real images. The proposed IPKD approach outperforms the three reference methods and in addition, exhibits a desired stability behaviour. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.patcog.2018.01.024 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 182 Subjects: – SubjectFull: Diagnostic imaging Type: general – SubjectFull: Image segmentation Type: general – SubjectFull: Diffusion Type: general – SubjectFull: Pattern recognition systems Type: general – SubjectFull: Fuzzy logic Type: general – SubjectFull: Probability theory Type: general Titles: – TitleFull: An iterative possibilistic knowledge diffusion approach for blind medical image segmentation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Khanfir Kallel, I. – PersonEntity: Name: NameFull: Almouahed, S. – PersonEntity: Name: NameFull: Solaiman, B. – PersonEntity: Name: NameFull: Bossé, É. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 00313203 Numbering: – Type: volume Value: 78 Titles: – TitleFull: Pattern Recognition Type: main |
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