Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering
The primary brain image segmentation goal is to partition a given brain image into different regions representing anatomical structures. Magnetic resonance image (MRI) segmentation is especially interesting because the accurate representation of white matter, grey matter and cerebrospinal fluid prov...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ortiz, A. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014transfer abstract |
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| Schlagwörter: |
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| Umfang: |
20 |
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| Übergeordnetes Werk: |
Enthalten in: Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study - Petrruzziello, Carmelina ELSEVIER, 2013, an international journal, New York, NY |
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| Übergeordnetes Werk: |
volume:262 ; year:2014 ; day:20 ; month:03 ; pages:117-136 ; extent:20 |
| Links: |
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| DOI / URN: |
10.1016/j.ins.2013.10.002 |
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ELV034224319 |
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| 520 | |a The primary brain image segmentation goal is to partition a given brain image into different regions representing anatomical structures. Magnetic resonance image (MRI) segmentation is especially interesting because the accurate representation of white matter, grey matter and cerebrospinal fluid provides a way to identify many brain disorders, such as dementia, schizophrenia or Alzheimer’s disease (AD). This paper presents a fully unsupervised method for MRI segmentation based on Self-Organising Maps (SOMs) and Genetic Algorithms (GAs). In particular, the proposed method is based on five stages consisting of image acquisition, feature extraction, feature selection using evolutionary computation, voxel classification using SOM, and sharp map clustering. Moreover, a novel SOM clustering mechanism is presented that defines cluster borders by considering the output space and the relationship with the input space. This clustering mechanism accommodates spatial relationships using an entropy-gradient (EG) function calculation to group the units on the output space (SOM). The complete procedure does not use any a priori knowledge regarding voxel class assignment, but reveals a fully unsupervised, automated method for MRI segmentation to directly identify different tissue classes. Our algorithm was tested using images from the Internet Brain Image Repository (IBSR), outperforming the CGMM method in WM and CSF delineation, and was found to be the most effective among other techniques. Furthermore, this method provided excellent results using high-resolution MR images provided by the Nuclear Medicine Service of the “Virgen de las Nieves” Hospital (Granada, Spain). | ||
| 520 | |a The primary brain image segmentation goal is to partition a given brain image into different regions representing anatomical structures. Magnetic resonance image (MRI) segmentation is especially interesting because the accurate representation of white matter, grey matter and cerebrospinal fluid provides a way to identify many brain disorders, such as dementia, schizophrenia or Alzheimer’s disease (AD). This paper presents a fully unsupervised method for MRI segmentation based on Self-Organising Maps (SOMs) and Genetic Algorithms (GAs). In particular, the proposed method is based on five stages consisting of image acquisition, feature extraction, feature selection using evolutionary computation, voxel classification using SOM, and sharp map clustering. Moreover, a novel SOM clustering mechanism is presented that defines cluster borders by considering the output space and the relationship with the input space. This clustering mechanism accommodates spatial relationships using an entropy-gradient (EG) function calculation to group the units on the output space (SOM). The complete procedure does not use any a priori knowledge regarding voxel class assignment, but reveals a fully unsupervised, automated method for MRI segmentation to directly identify different tissue classes. Our algorithm was tested using images from the Internet Brain Image Repository (IBSR), outperforming the CGMM method in WM and CSF delineation, and was found to be the most effective among other techniques. Furthermore, this method provided excellent results using high-resolution MR images provided by the Nuclear Medicine Service of the “Virgen de las Nieves” Hospital (Granada, Spain). | ||
| 650 | 7 | |a Image segmentation |2 Elsevier | |
| 650 | 7 | |a MRI |2 Elsevier | |
| 650 | 7 | |a Neural networks |2 Elsevier | |
| 650 | 7 | |a Feature extraction |2 Elsevier | |
| 650 | 7 | |a Self-organising maps |2 Elsevier | |
| 650 | 7 | |a Genetic algorithms |2 Elsevier | |
| 700 | 1 | |a Gorriz, J.M. |4 oth | |
| 700 | 1 | |a Ramirez, J. |4 oth | |
| 700 | 1 | |a Salas-Gonzalez, D. |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science Inc |a Petrruzziello, Carmelina ELSEVIER |t Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study |d 2013 |d an international journal |g New York, NY |w (DE-627)ELV011843691 |
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10.1016/j.ins.2013.10.002 doi GBVA2014021000006.pica (DE-627)ELV034224319 (ELSEVIER)S0020-0255(13)00711-1 DE-627 ger DE-627 rakwb eng 070 004 070 DNB 004 DNB 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Ortiz, A. verfasserin aut Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering 2014transfer abstract 20 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The primary brain image segmentation goal is to partition a given brain image into different regions representing anatomical structures. Magnetic resonance image (MRI) segmentation is especially interesting because the accurate representation of white matter, grey matter and cerebrospinal fluid provides a way to identify many brain disorders, such as dementia, schizophrenia or Alzheimer’s disease (AD). This paper presents a fully unsupervised method for MRI segmentation based on Self-Organising Maps (SOMs) and Genetic Algorithms (GAs). In particular, the proposed method is based on five stages consisting of image acquisition, feature extraction, feature selection using evolutionary computation, voxel classification using SOM, and sharp map clustering. Moreover, a novel SOM clustering mechanism is presented that defines cluster borders by considering the output space and the relationship with the input space. This clustering mechanism accommodates spatial relationships using an entropy-gradient (EG) function calculation to group the units on the output space (SOM). The complete procedure does not use any a priori knowledge regarding voxel class assignment, but reveals a fully unsupervised, automated method for MRI segmentation to directly identify different tissue classes. Our algorithm was tested using images from the Internet Brain Image Repository (IBSR), outperforming the CGMM method in WM and CSF delineation, and was found to be the most effective among other techniques. Furthermore, this method provided excellent results using high-resolution MR images provided by the Nuclear Medicine Service of the “Virgen de las Nieves” Hospital (Granada, Spain). The primary brain image segmentation goal is to partition a given brain image into different regions representing anatomical structures. Magnetic resonance image (MRI) segmentation is especially interesting because the accurate representation of white matter, grey matter and cerebrospinal fluid provides a way to identify many brain disorders, such as dementia, schizophrenia or Alzheimer’s disease (AD). This paper presents a fully unsupervised method for MRI segmentation based on Self-Organising Maps (SOMs) and Genetic Algorithms (GAs). In particular, the proposed method is based on five stages consisting of image acquisition, feature extraction, feature selection using evolutionary computation, voxel classification using SOM, and sharp map clustering. Moreover, a novel SOM clustering mechanism is presented that defines cluster borders by considering the output space and the relationship with the input space. This clustering mechanism accommodates spatial relationships using an entropy-gradient (EG) function calculation to group the units on the output space (SOM). The complete procedure does not use any a priori knowledge regarding voxel class assignment, but reveals a fully unsupervised, automated method for MRI segmentation to directly identify different tissue classes. Our algorithm was tested using images from the Internet Brain Image Repository (IBSR), outperforming the CGMM method in WM and CSF delineation, and was found to be the most effective among other techniques. Furthermore, this method provided excellent results using high-resolution MR images provided by the Nuclear Medicine Service of the “Virgen de las Nieves” Hospital (Granada, Spain). Image segmentation Elsevier MRI Elsevier Neural networks Elsevier Feature extraction Elsevier Self-organising maps Elsevier Genetic algorithms Elsevier Gorriz, J.M. oth Ramirez, J. oth Salas-Gonzalez, D. oth Enthalten in Elsevier Science Inc Petrruzziello, Carmelina ELSEVIER Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study 2013 an international journal New York, NY (DE-627)ELV011843691 volume:262 year:2014 day:20 month:03 pages:117-136 extent:20 https://doi.org/10.1016/j.ins.2013.10.002 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 262 2014 20 0320 117-136 20 045F 070 |
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10.1016/j.ins.2013.10.002 doi GBVA2014021000006.pica (DE-627)ELV034224319 (ELSEVIER)S0020-0255(13)00711-1 DE-627 ger DE-627 rakwb eng 070 004 070 DNB 004 DNB 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Ortiz, A. verfasserin aut Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering 2014transfer abstract 20 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The primary brain image segmentation goal is to partition a given brain image into different regions representing anatomical structures. Magnetic resonance image (MRI) segmentation is especially interesting because the accurate representation of white matter, grey matter and cerebrospinal fluid provides a way to identify many brain disorders, such as dementia, schizophrenia or Alzheimer’s disease (AD). This paper presents a fully unsupervised method for MRI segmentation based on Self-Organising Maps (SOMs) and Genetic Algorithms (GAs). In particular, the proposed method is based on five stages consisting of image acquisition, feature extraction, feature selection using evolutionary computation, voxel classification using SOM, and sharp map clustering. Moreover, a novel SOM clustering mechanism is presented that defines cluster borders by considering the output space and the relationship with the input space. This clustering mechanism accommodates spatial relationships using an entropy-gradient (EG) function calculation to group the units on the output space (SOM). The complete procedure does not use any a priori knowledge regarding voxel class assignment, but reveals a fully unsupervised, automated method for MRI segmentation to directly identify different tissue classes. Our algorithm was tested using images from the Internet Brain Image Repository (IBSR), outperforming the CGMM method in WM and CSF delineation, and was found to be the most effective among other techniques. Furthermore, this method provided excellent results using high-resolution MR images provided by the Nuclear Medicine Service of the “Virgen de las Nieves” Hospital (Granada, Spain). The primary brain image segmentation goal is to partition a given brain image into different regions representing anatomical structures. Magnetic resonance image (MRI) segmentation is especially interesting because the accurate representation of white matter, grey matter and cerebrospinal fluid provides a way to identify many brain disorders, such as dementia, schizophrenia or Alzheimer’s disease (AD). This paper presents a fully unsupervised method for MRI segmentation based on Self-Organising Maps (SOMs) and Genetic Algorithms (GAs). In particular, the proposed method is based on five stages consisting of image acquisition, feature extraction, feature selection using evolutionary computation, voxel classification using SOM, and sharp map clustering. Moreover, a novel SOM clustering mechanism is presented that defines cluster borders by considering the output space and the relationship with the input space. This clustering mechanism accommodates spatial relationships using an entropy-gradient (EG) function calculation to group the units on the output space (SOM). The complete procedure does not use any a priori knowledge regarding voxel class assignment, but reveals a fully unsupervised, automated method for MRI segmentation to directly identify different tissue classes. Our algorithm was tested using images from the Internet Brain Image Repository (IBSR), outperforming the CGMM method in WM and CSF delineation, and was found to be the most effective among other techniques. Furthermore, this method provided excellent results using high-resolution MR images provided by the Nuclear Medicine Service of the “Virgen de las Nieves” Hospital (Granada, Spain). Image segmentation Elsevier MRI Elsevier Neural networks Elsevier Feature extraction Elsevier Self-organising maps Elsevier Genetic algorithms Elsevier Gorriz, J.M. oth Ramirez, J. oth Salas-Gonzalez, D. oth Enthalten in Elsevier Science Inc Petrruzziello, Carmelina ELSEVIER Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study 2013 an international journal New York, NY (DE-627)ELV011843691 volume:262 year:2014 day:20 month:03 pages:117-136 extent:20 https://doi.org/10.1016/j.ins.2013.10.002 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 262 2014 20 0320 117-136 20 045F 070 |
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10.1016/j.ins.2013.10.002 doi GBVA2014021000006.pica (DE-627)ELV034224319 (ELSEVIER)S0020-0255(13)00711-1 DE-627 ger DE-627 rakwb eng 070 004 070 DNB 004 DNB 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Ortiz, A. verfasserin aut Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering 2014transfer abstract 20 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The primary brain image segmentation goal is to partition a given brain image into different regions representing anatomical structures. Magnetic resonance image (MRI) segmentation is especially interesting because the accurate representation of white matter, grey matter and cerebrospinal fluid provides a way to identify many brain disorders, such as dementia, schizophrenia or Alzheimer’s disease (AD). This paper presents a fully unsupervised method for MRI segmentation based on Self-Organising Maps (SOMs) and Genetic Algorithms (GAs). In particular, the proposed method is based on five stages consisting of image acquisition, feature extraction, feature selection using evolutionary computation, voxel classification using SOM, and sharp map clustering. Moreover, a novel SOM clustering mechanism is presented that defines cluster borders by considering the output space and the relationship with the input space. This clustering mechanism accommodates spatial relationships using an entropy-gradient (EG) function calculation to group the units on the output space (SOM). The complete procedure does not use any a priori knowledge regarding voxel class assignment, but reveals a fully unsupervised, automated method for MRI segmentation to directly identify different tissue classes. Our algorithm was tested using images from the Internet Brain Image Repository (IBSR), outperforming the CGMM method in WM and CSF delineation, and was found to be the most effective among other techniques. Furthermore, this method provided excellent results using high-resolution MR images provided by the Nuclear Medicine Service of the “Virgen de las Nieves” Hospital (Granada, Spain). The primary brain image segmentation goal is to partition a given brain image into different regions representing anatomical structures. Magnetic resonance image (MRI) segmentation is especially interesting because the accurate representation of white matter, grey matter and cerebrospinal fluid provides a way to identify many brain disorders, such as dementia, schizophrenia or Alzheimer’s disease (AD). This paper presents a fully unsupervised method for MRI segmentation based on Self-Organising Maps (SOMs) and Genetic Algorithms (GAs). In particular, the proposed method is based on five stages consisting of image acquisition, feature extraction, feature selection using evolutionary computation, voxel classification using SOM, and sharp map clustering. Moreover, a novel SOM clustering mechanism is presented that defines cluster borders by considering the output space and the relationship with the input space. This clustering mechanism accommodates spatial relationships using an entropy-gradient (EG) function calculation to group the units on the output space (SOM). The complete procedure does not use any a priori knowledge regarding voxel class assignment, but reveals a fully unsupervised, automated method for MRI segmentation to directly identify different tissue classes. Our algorithm was tested using images from the Internet Brain Image Repository (IBSR), outperforming the CGMM method in WM and CSF delineation, and was found to be the most effective among other techniques. Furthermore, this method provided excellent results using high-resolution MR images provided by the Nuclear Medicine Service of the “Virgen de las Nieves” Hospital (Granada, Spain). Image segmentation Elsevier MRI Elsevier Neural networks Elsevier Feature extraction Elsevier Self-organising maps Elsevier Genetic algorithms Elsevier Gorriz, J.M. oth Ramirez, J. oth Salas-Gonzalez, D. oth Enthalten in Elsevier Science Inc Petrruzziello, Carmelina ELSEVIER Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study 2013 an international journal New York, NY (DE-627)ELV011843691 volume:262 year:2014 day:20 month:03 pages:117-136 extent:20 https://doi.org/10.1016/j.ins.2013.10.002 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 262 2014 20 0320 117-136 20 045F 070 |
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10.1016/j.ins.2013.10.002 doi GBVA2014021000006.pica (DE-627)ELV034224319 (ELSEVIER)S0020-0255(13)00711-1 DE-627 ger DE-627 rakwb eng 070 004 070 DNB 004 DNB 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Ortiz, A. verfasserin aut Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering 2014transfer abstract 20 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The primary brain image segmentation goal is to partition a given brain image into different regions representing anatomical structures. Magnetic resonance image (MRI) segmentation is especially interesting because the accurate representation of white matter, grey matter and cerebrospinal fluid provides a way to identify many brain disorders, such as dementia, schizophrenia or Alzheimer’s disease (AD). This paper presents a fully unsupervised method for MRI segmentation based on Self-Organising Maps (SOMs) and Genetic Algorithms (GAs). In particular, the proposed method is based on five stages consisting of image acquisition, feature extraction, feature selection using evolutionary computation, voxel classification using SOM, and sharp map clustering. Moreover, a novel SOM clustering mechanism is presented that defines cluster borders by considering the output space and the relationship with the input space. This clustering mechanism accommodates spatial relationships using an entropy-gradient (EG) function calculation to group the units on the output space (SOM). The complete procedure does not use any a priori knowledge regarding voxel class assignment, but reveals a fully unsupervised, automated method for MRI segmentation to directly identify different tissue classes. Our algorithm was tested using images from the Internet Brain Image Repository (IBSR), outperforming the CGMM method in WM and CSF delineation, and was found to be the most effective among other techniques. Furthermore, this method provided excellent results using high-resolution MR images provided by the Nuclear Medicine Service of the “Virgen de las Nieves” Hospital (Granada, Spain). The primary brain image segmentation goal is to partition a given brain image into different regions representing anatomical structures. Magnetic resonance image (MRI) segmentation is especially interesting because the accurate representation of white matter, grey matter and cerebrospinal fluid provides a way to identify many brain disorders, such as dementia, schizophrenia or Alzheimer’s disease (AD). This paper presents a fully unsupervised method for MRI segmentation based on Self-Organising Maps (SOMs) and Genetic Algorithms (GAs). In particular, the proposed method is based on five stages consisting of image acquisition, feature extraction, feature selection using evolutionary computation, voxel classification using SOM, and sharp map clustering. Moreover, a novel SOM clustering mechanism is presented that defines cluster borders by considering the output space and the relationship with the input space. This clustering mechanism accommodates spatial relationships using an entropy-gradient (EG) function calculation to group the units on the output space (SOM). The complete procedure does not use any a priori knowledge regarding voxel class assignment, but reveals a fully unsupervised, automated method for MRI segmentation to directly identify different tissue classes. Our algorithm was tested using images from the Internet Brain Image Repository (IBSR), outperforming the CGMM method in WM and CSF delineation, and was found to be the most effective among other techniques. Furthermore, this method provided excellent results using high-resolution MR images provided by the Nuclear Medicine Service of the “Virgen de las Nieves” Hospital (Granada, Spain). Image segmentation Elsevier MRI Elsevier Neural networks Elsevier Feature extraction Elsevier Self-organising maps Elsevier Genetic algorithms Elsevier Gorriz, J.M. oth Ramirez, J. oth Salas-Gonzalez, D. oth Enthalten in Elsevier Science Inc Petrruzziello, Carmelina ELSEVIER Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study 2013 an international journal New York, NY (DE-627)ELV011843691 volume:262 year:2014 day:20 month:03 pages:117-136 extent:20 https://doi.org/10.1016/j.ins.2013.10.002 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 262 2014 20 0320 117-136 20 045F 070 |
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10.1016/j.ins.2013.10.002 doi GBVA2014021000006.pica (DE-627)ELV034224319 (ELSEVIER)S0020-0255(13)00711-1 DE-627 ger DE-627 rakwb eng 070 004 070 DNB 004 DNB 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Ortiz, A. verfasserin aut Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering 2014transfer abstract 20 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The primary brain image segmentation goal is to partition a given brain image into different regions representing anatomical structures. Magnetic resonance image (MRI) segmentation is especially interesting because the accurate representation of white matter, grey matter and cerebrospinal fluid provides a way to identify many brain disorders, such as dementia, schizophrenia or Alzheimer’s disease (AD). This paper presents a fully unsupervised method for MRI segmentation based on Self-Organising Maps (SOMs) and Genetic Algorithms (GAs). In particular, the proposed method is based on five stages consisting of image acquisition, feature extraction, feature selection using evolutionary computation, voxel classification using SOM, and sharp map clustering. Moreover, a novel SOM clustering mechanism is presented that defines cluster borders by considering the output space and the relationship with the input space. This clustering mechanism accommodates spatial relationships using an entropy-gradient (EG) function calculation to group the units on the output space (SOM). The complete procedure does not use any a priori knowledge regarding voxel class assignment, but reveals a fully unsupervised, automated method for MRI segmentation to directly identify different tissue classes. Our algorithm was tested using images from the Internet Brain Image Repository (IBSR), outperforming the CGMM method in WM and CSF delineation, and was found to be the most effective among other techniques. Furthermore, this method provided excellent results using high-resolution MR images provided by the Nuclear Medicine Service of the “Virgen de las Nieves” Hospital (Granada, Spain). The primary brain image segmentation goal is to partition a given brain image into different regions representing anatomical structures. Magnetic resonance image (MRI) segmentation is especially interesting because the accurate representation of white matter, grey matter and cerebrospinal fluid provides a way to identify many brain disorders, such as dementia, schizophrenia or Alzheimer’s disease (AD). This paper presents a fully unsupervised method for MRI segmentation based on Self-Organising Maps (SOMs) and Genetic Algorithms (GAs). In particular, the proposed method is based on five stages consisting of image acquisition, feature extraction, feature selection using evolutionary computation, voxel classification using SOM, and sharp map clustering. Moreover, a novel SOM clustering mechanism is presented that defines cluster borders by considering the output space and the relationship with the input space. This clustering mechanism accommodates spatial relationships using an entropy-gradient (EG) function calculation to group the units on the output space (SOM). The complete procedure does not use any a priori knowledge regarding voxel class assignment, but reveals a fully unsupervised, automated method for MRI segmentation to directly identify different tissue classes. Our algorithm was tested using images from the Internet Brain Image Repository (IBSR), outperforming the CGMM method in WM and CSF delineation, and was found to be the most effective among other techniques. Furthermore, this method provided excellent results using high-resolution MR images provided by the Nuclear Medicine Service of the “Virgen de las Nieves” Hospital (Granada, Spain). Image segmentation Elsevier MRI Elsevier Neural networks Elsevier Feature extraction Elsevier Self-organising maps Elsevier Genetic algorithms Elsevier Gorriz, J.M. oth Ramirez, J. oth Salas-Gonzalez, D. oth Enthalten in Elsevier Science Inc Petrruzziello, Carmelina ELSEVIER Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study 2013 an international journal New York, NY (DE-627)ELV011843691 volume:262 year:2014 day:20 month:03 pages:117-136 extent:20 https://doi.org/10.1016/j.ins.2013.10.002 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 262 2014 20 0320 117-136 20 045F 070 |
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Enthalten in Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study New York, NY volume:262 year:2014 day:20 month:03 pages:117-136 extent:20 |
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Enthalten in Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study New York, NY volume:262 year:2014 day:20 month:03 pages:117-136 extent:20 |
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Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering |
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The primary brain image segmentation goal is to partition a given brain image into different regions representing anatomical structures. Magnetic resonance image (MRI) segmentation is especially interesting because the accurate representation of white matter, grey matter and cerebrospinal fluid provides a way to identify many brain disorders, such as dementia, schizophrenia or Alzheimer’s disease (AD). This paper presents a fully unsupervised method for MRI segmentation based on Self-Organising Maps (SOMs) and Genetic Algorithms (GAs). In particular, the proposed method is based on five stages consisting of image acquisition, feature extraction, feature selection using evolutionary computation, voxel classification using SOM, and sharp map clustering. Moreover, a novel SOM clustering mechanism is presented that defines cluster borders by considering the output space and the relationship with the input space. This clustering mechanism accommodates spatial relationships using an entropy-gradient (EG) function calculation to group the units on the output space (SOM). The complete procedure does not use any a priori knowledge regarding voxel class assignment, but reveals a fully unsupervised, automated method for MRI segmentation to directly identify different tissue classes. Our algorithm was tested using images from the Internet Brain Image Repository (IBSR), outperforming the CGMM method in WM and CSF delineation, and was found to be the most effective among other techniques. Furthermore, this method provided excellent results using high-resolution MR images provided by the Nuclear Medicine Service of the “Virgen de las Nieves” Hospital (Granada, Spain). |
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The primary brain image segmentation goal is to partition a given brain image into different regions representing anatomical structures. Magnetic resonance image (MRI) segmentation is especially interesting because the accurate representation of white matter, grey matter and cerebrospinal fluid provides a way to identify many brain disorders, such as dementia, schizophrenia or Alzheimer’s disease (AD). This paper presents a fully unsupervised method for MRI segmentation based on Self-Organising Maps (SOMs) and Genetic Algorithms (GAs). In particular, the proposed method is based on five stages consisting of image acquisition, feature extraction, feature selection using evolutionary computation, voxel classification using SOM, and sharp map clustering. Moreover, a novel SOM clustering mechanism is presented that defines cluster borders by considering the output space and the relationship with the input space. This clustering mechanism accommodates spatial relationships using an entropy-gradient (EG) function calculation to group the units on the output space (SOM). The complete procedure does not use any a priori knowledge regarding voxel class assignment, but reveals a fully unsupervised, automated method for MRI segmentation to directly identify different tissue classes. Our algorithm was tested using images from the Internet Brain Image Repository (IBSR), outperforming the CGMM method in WM and CSF delineation, and was found to be the most effective among other techniques. Furthermore, this method provided excellent results using high-resolution MR images provided by the Nuclear Medicine Service of the “Virgen de las Nieves” Hospital (Granada, Spain). |
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The primary brain image segmentation goal is to partition a given brain image into different regions representing anatomical structures. Magnetic resonance image (MRI) segmentation is especially interesting because the accurate representation of white matter, grey matter and cerebrospinal fluid provides a way to identify many brain disorders, such as dementia, schizophrenia or Alzheimer’s disease (AD). This paper presents a fully unsupervised method for MRI segmentation based on Self-Organising Maps (SOMs) and Genetic Algorithms (GAs). In particular, the proposed method is based on five stages consisting of image acquisition, feature extraction, feature selection using evolutionary computation, voxel classification using SOM, and sharp map clustering. Moreover, a novel SOM clustering mechanism is presented that defines cluster borders by considering the output space and the relationship with the input space. This clustering mechanism accommodates spatial relationships using an entropy-gradient (EG) function calculation to group the units on the output space (SOM). The complete procedure does not use any a priori knowledge regarding voxel class assignment, but reveals a fully unsupervised, automated method for MRI segmentation to directly identify different tissue classes. Our algorithm was tested using images from the Internet Brain Image Repository (IBSR), outperforming the CGMM method in WM and CSF delineation, and was found to be the most effective among other techniques. Furthermore, this method provided excellent results using high-resolution MR images provided by the Nuclear Medicine Service of the “Virgen de las Nieves” Hospital (Granada, Spain). |
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Magnetic resonance image (MRI) segmentation is especially interesting because the accurate representation of white matter, grey matter and cerebrospinal fluid provides a way to identify many brain disorders, such as dementia, schizophrenia or Alzheimer’s disease (AD). This paper presents a fully unsupervised method for MRI segmentation based on Self-Organising Maps (SOMs) and Genetic Algorithms (GAs). In particular, the proposed method is based on five stages consisting of image acquisition, feature extraction, feature selection using evolutionary computation, voxel classification using SOM, and sharp map clustering. Moreover, a novel SOM clustering mechanism is presented that defines cluster borders by considering the output space and the relationship with the input space. This clustering mechanism accommodates spatial relationships using an entropy-gradient (EG) function calculation to group the units on the output space (SOM). The complete procedure does not use any a priori knowledge regarding voxel class assignment, but reveals a fully unsupervised, automated method for MRI segmentation to directly identify different tissue classes. Our algorithm was tested using images from the Internet Brain Image Repository (IBSR), outperforming the CGMM method in WM and CSF delineation, and was found to be the most effective among other techniques. Furthermore, this method provided excellent results using high-resolution MR images provided by the Nuclear Medicine Service of the “Virgen de las Nieves” Hospital (Granada, Spain).</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Image segmentation</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">MRI</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Neural networks</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Feature extraction</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Self-organising maps</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Genetic algorithms</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gorriz, J.M.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ramirez, J.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Salas-Gonzalez, D.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science Inc</subfield><subfield code="a">Petrruzziello, Carmelina ELSEVIER</subfield><subfield code="t">Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study</subfield><subfield code="d">2013</subfield><subfield code="d">an international journal</subfield><subfield code="g">New York, NY</subfield><subfield code="w">(DE-627)ELV011843691</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:262</subfield><subfield code="g">year:2014</subfield><subfield code="g">day:20</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:117-136</subfield><subfield code="g">extent:20</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.ins.2013.10.002</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-BIODIV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.70</subfield><subfield code="j">Biochemie: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.12</subfield><subfield code="j">Biophysik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.15</subfield><subfield code="j">Zellbiologie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">262</subfield><subfield code="j">2014</subfield><subfield code="b">20</subfield><subfield code="c">0320</subfield><subfield code="h">117-136</subfield><subfield code="g">20</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">070</subfield></datafield></record></collection>
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