Lymphocyte image segmentation using functional link neural architecture for acute leukemia detection
Purpose Microscopic examination of stained blood slides is an indispensable technique for hematological disease recognition. Diagnosis based on human visual interpretation is often subjected to inter and intra observer variations, slowness, tiredness and operator experience. Accurate and authentic d...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Mohapatra, Subrajeet [verfasserIn] Patra, Dipti [verfasserIn] Kumar, Sunil [verfasserIn] Satpathy, Sanghamitra [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2012 |
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| Schlagwörter: |
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Enthalten in: Biomedical Engineering Letters - Korean Society of Medical and Biological Engineering, 2011, 2(2012), 2 vom: Juni, Seite 100-110 |
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volume:2 ; year:2012 ; number:2 ; month:06 ; pages:100-110 |
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10.1007/s13534-012-0056-9 |
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| 520 | |a Purpose Microscopic examination of stained blood slides is an indispensable technique for hematological disease recognition. Diagnosis based on human visual interpretation is often subjected to inter and intra observer variations, slowness, tiredness and operator experience. Accurate and authentic diagnosis of hematological neoplasia is essential in the planning of suitable surgery and chemotherapy. This paper aims at proposing a fast and simple framework for lymphocyte image segmentation. Methods Accurate segmentation of lymphocyte is essential as it facilitates automated leukemia detection in blood microscopic images. In this paper image segmentation is considered as a pixel classification problem and a novel neural architecture is employed to classify each pixel into cytoplasm, nucleus or background region. The network tuned for a set of images works well for other similar stained blood images. Results Comparative analysis with other standard techniques reveals that the proposed scheme outperforms its counterparts in terms of nucleus and cytoplasm extraction. Conclusions In this work, a neural network based lymphocyte image segmentation scheme is designed for automated leukemia detection. Desired segmentation accuracy in terms of nucleus and cytoplasm extraction is always high in automated disease recognition system and is achieved through the proposed scheme. | ||
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10.1007/s13534-012-0056-9 doi (DE-627)SPR031735703 (SPR)s13534-012-0056-9-e DE-627 ger DE-627 rakwb eng Mohapatra, Subrajeet verfasserin aut Lymphocyte image segmentation using functional link neural architecture for acute leukemia detection 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose Microscopic examination of stained blood slides is an indispensable technique for hematological disease recognition. Diagnosis based on human visual interpretation is often subjected to inter and intra observer variations, slowness, tiredness and operator experience. Accurate and authentic diagnosis of hematological neoplasia is essential in the planning of suitable surgery and chemotherapy. This paper aims at proposing a fast and simple framework for lymphocyte image segmentation. Methods Accurate segmentation of lymphocyte is essential as it facilitates automated leukemia detection in blood microscopic images. In this paper image segmentation is considered as a pixel classification problem and a novel neural architecture is employed to classify each pixel into cytoplasm, nucleus or background region. The network tuned for a set of images works well for other similar stained blood images. Results Comparative analysis with other standard techniques reveals that the proposed scheme outperforms its counterparts in terms of nucleus and cytoplasm extraction. Conclusions In this work, a neural network based lymphocyte image segmentation scheme is designed for automated leukemia detection. Desired segmentation accuracy in terms of nucleus and cytoplasm extraction is always high in automated disease recognition system and is achieved through the proposed scheme. Leukocyte segmentation (dpeaa)DE-He213 Lab color model (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 Pixel classification (dpeaa)DE-He213 Functional expansion (dpeaa)DE-He213 Tanimoto index (dpeaa)DE-He213 Patra, Dipti verfasserin aut Kumar, Sunil verfasserin aut Satpathy, Sanghamitra verfasserin aut Enthalten in Biomedical Engineering Letters Korean Society of Medical and Biological Engineering, 2011 2(2012), 2 vom: Juni, Seite 100-110 (DE-627)SPR03173507X nnns volume:2 year:2012 number:2 month:06 pages:100-110 https://dx.doi.org/10.1007/s13534-012-0056-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_40 AR 2 2012 2 06 100-110 |
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10.1007/s13534-012-0056-9 doi (DE-627)SPR031735703 (SPR)s13534-012-0056-9-e DE-627 ger DE-627 rakwb eng Mohapatra, Subrajeet verfasserin aut Lymphocyte image segmentation using functional link neural architecture for acute leukemia detection 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose Microscopic examination of stained blood slides is an indispensable technique for hematological disease recognition. Diagnosis based on human visual interpretation is often subjected to inter and intra observer variations, slowness, tiredness and operator experience. Accurate and authentic diagnosis of hematological neoplasia is essential in the planning of suitable surgery and chemotherapy. This paper aims at proposing a fast and simple framework for lymphocyte image segmentation. Methods Accurate segmentation of lymphocyte is essential as it facilitates automated leukemia detection in blood microscopic images. In this paper image segmentation is considered as a pixel classification problem and a novel neural architecture is employed to classify each pixel into cytoplasm, nucleus or background region. The network tuned for a set of images works well for other similar stained blood images. Results Comparative analysis with other standard techniques reveals that the proposed scheme outperforms its counterparts in terms of nucleus and cytoplasm extraction. Conclusions In this work, a neural network based lymphocyte image segmentation scheme is designed for automated leukemia detection. Desired segmentation accuracy in terms of nucleus and cytoplasm extraction is always high in automated disease recognition system and is achieved through the proposed scheme. Leukocyte segmentation (dpeaa)DE-He213 Lab color model (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 Pixel classification (dpeaa)DE-He213 Functional expansion (dpeaa)DE-He213 Tanimoto index (dpeaa)DE-He213 Patra, Dipti verfasserin aut Kumar, Sunil verfasserin aut Satpathy, Sanghamitra verfasserin aut Enthalten in Biomedical Engineering Letters Korean Society of Medical and Biological Engineering, 2011 2(2012), 2 vom: Juni, Seite 100-110 (DE-627)SPR03173507X nnns volume:2 year:2012 number:2 month:06 pages:100-110 https://dx.doi.org/10.1007/s13534-012-0056-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_40 AR 2 2012 2 06 100-110 |
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10.1007/s13534-012-0056-9 doi (DE-627)SPR031735703 (SPR)s13534-012-0056-9-e DE-627 ger DE-627 rakwb eng Mohapatra, Subrajeet verfasserin aut Lymphocyte image segmentation using functional link neural architecture for acute leukemia detection 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose Microscopic examination of stained blood slides is an indispensable technique for hematological disease recognition. Diagnosis based on human visual interpretation is often subjected to inter and intra observer variations, slowness, tiredness and operator experience. Accurate and authentic diagnosis of hematological neoplasia is essential in the planning of suitable surgery and chemotherapy. This paper aims at proposing a fast and simple framework for lymphocyte image segmentation. Methods Accurate segmentation of lymphocyte is essential as it facilitates automated leukemia detection in blood microscopic images. In this paper image segmentation is considered as a pixel classification problem and a novel neural architecture is employed to classify each pixel into cytoplasm, nucleus or background region. The network tuned for a set of images works well for other similar stained blood images. Results Comparative analysis with other standard techniques reveals that the proposed scheme outperforms its counterparts in terms of nucleus and cytoplasm extraction. Conclusions In this work, a neural network based lymphocyte image segmentation scheme is designed for automated leukemia detection. Desired segmentation accuracy in terms of nucleus and cytoplasm extraction is always high in automated disease recognition system and is achieved through the proposed scheme. Leukocyte segmentation (dpeaa)DE-He213 Lab color model (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 Pixel classification (dpeaa)DE-He213 Functional expansion (dpeaa)DE-He213 Tanimoto index (dpeaa)DE-He213 Patra, Dipti verfasserin aut Kumar, Sunil verfasserin aut Satpathy, Sanghamitra verfasserin aut Enthalten in Biomedical Engineering Letters Korean Society of Medical and Biological Engineering, 2011 2(2012), 2 vom: Juni, Seite 100-110 (DE-627)SPR03173507X nnns volume:2 year:2012 number:2 month:06 pages:100-110 https://dx.doi.org/10.1007/s13534-012-0056-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_40 AR 2 2012 2 06 100-110 |
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10.1007/s13534-012-0056-9 doi (DE-627)SPR031735703 (SPR)s13534-012-0056-9-e DE-627 ger DE-627 rakwb eng Mohapatra, Subrajeet verfasserin aut Lymphocyte image segmentation using functional link neural architecture for acute leukemia detection 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose Microscopic examination of stained blood slides is an indispensable technique for hematological disease recognition. Diagnosis based on human visual interpretation is often subjected to inter and intra observer variations, slowness, tiredness and operator experience. Accurate and authentic diagnosis of hematological neoplasia is essential in the planning of suitable surgery and chemotherapy. This paper aims at proposing a fast and simple framework for lymphocyte image segmentation. Methods Accurate segmentation of lymphocyte is essential as it facilitates automated leukemia detection in blood microscopic images. In this paper image segmentation is considered as a pixel classification problem and a novel neural architecture is employed to classify each pixel into cytoplasm, nucleus or background region. The network tuned for a set of images works well for other similar stained blood images. Results Comparative analysis with other standard techniques reveals that the proposed scheme outperforms its counterparts in terms of nucleus and cytoplasm extraction. Conclusions In this work, a neural network based lymphocyte image segmentation scheme is designed for automated leukemia detection. Desired segmentation accuracy in terms of nucleus and cytoplasm extraction is always high in automated disease recognition system and is achieved through the proposed scheme. Leukocyte segmentation (dpeaa)DE-He213 Lab color model (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 Pixel classification (dpeaa)DE-He213 Functional expansion (dpeaa)DE-He213 Tanimoto index (dpeaa)DE-He213 Patra, Dipti verfasserin aut Kumar, Sunil verfasserin aut Satpathy, Sanghamitra verfasserin aut Enthalten in Biomedical Engineering Letters Korean Society of Medical and Biological Engineering, 2011 2(2012), 2 vom: Juni, Seite 100-110 (DE-627)SPR03173507X nnns volume:2 year:2012 number:2 month:06 pages:100-110 https://dx.doi.org/10.1007/s13534-012-0056-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_40 AR 2 2012 2 06 100-110 |
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10.1007/s13534-012-0056-9 doi (DE-627)SPR031735703 (SPR)s13534-012-0056-9-e DE-627 ger DE-627 rakwb eng Mohapatra, Subrajeet verfasserin aut Lymphocyte image segmentation using functional link neural architecture for acute leukemia detection 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose Microscopic examination of stained blood slides is an indispensable technique for hematological disease recognition. Diagnosis based on human visual interpretation is often subjected to inter and intra observer variations, slowness, tiredness and operator experience. Accurate and authentic diagnosis of hematological neoplasia is essential in the planning of suitable surgery and chemotherapy. This paper aims at proposing a fast and simple framework for lymphocyte image segmentation. Methods Accurate segmentation of lymphocyte is essential as it facilitates automated leukemia detection in blood microscopic images. In this paper image segmentation is considered as a pixel classification problem and a novel neural architecture is employed to classify each pixel into cytoplasm, nucleus or background region. The network tuned for a set of images works well for other similar stained blood images. Results Comparative analysis with other standard techniques reveals that the proposed scheme outperforms its counterparts in terms of nucleus and cytoplasm extraction. Conclusions In this work, a neural network based lymphocyte image segmentation scheme is designed for automated leukemia detection. Desired segmentation accuracy in terms of nucleus and cytoplasm extraction is always high in automated disease recognition system and is achieved through the proposed scheme. Leukocyte segmentation (dpeaa)DE-He213 Lab color model (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 Pixel classification (dpeaa)DE-He213 Functional expansion (dpeaa)DE-He213 Tanimoto index (dpeaa)DE-He213 Patra, Dipti verfasserin aut Kumar, Sunil verfasserin aut Satpathy, Sanghamitra verfasserin aut Enthalten in Biomedical Engineering Letters Korean Society of Medical and Biological Engineering, 2011 2(2012), 2 vom: Juni, Seite 100-110 (DE-627)SPR03173507X nnns volume:2 year:2012 number:2 month:06 pages:100-110 https://dx.doi.org/10.1007/s13534-012-0056-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_40 AR 2 2012 2 06 100-110 |
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Purpose Microscopic examination of stained blood slides is an indispensable technique for hematological disease recognition. Diagnosis based on human visual interpretation is often subjected to inter and intra observer variations, slowness, tiredness and operator experience. Accurate and authentic diagnosis of hematological neoplasia is essential in the planning of suitable surgery and chemotherapy. This paper aims at proposing a fast and simple framework for lymphocyte image segmentation. Methods Accurate segmentation of lymphocyte is essential as it facilitates automated leukemia detection in blood microscopic images. In this paper image segmentation is considered as a pixel classification problem and a novel neural architecture is employed to classify each pixel into cytoplasm, nucleus or background region. The network tuned for a set of images works well for other similar stained blood images. Results Comparative analysis with other standard techniques reveals that the proposed scheme outperforms its counterparts in terms of nucleus and cytoplasm extraction. Conclusions In this work, a neural network based lymphocyte image segmentation scheme is designed for automated leukemia detection. Desired segmentation accuracy in terms of nucleus and cytoplasm extraction is always high in automated disease recognition system and is achieved through the proposed scheme. |
| abstractGer |
Purpose Microscopic examination of stained blood slides is an indispensable technique for hematological disease recognition. Diagnosis based on human visual interpretation is often subjected to inter and intra observer variations, slowness, tiredness and operator experience. Accurate and authentic diagnosis of hematological neoplasia is essential in the planning of suitable surgery and chemotherapy. This paper aims at proposing a fast and simple framework for lymphocyte image segmentation. Methods Accurate segmentation of lymphocyte is essential as it facilitates automated leukemia detection in blood microscopic images. In this paper image segmentation is considered as a pixel classification problem and a novel neural architecture is employed to classify each pixel into cytoplasm, nucleus or background region. The network tuned for a set of images works well for other similar stained blood images. Results Comparative analysis with other standard techniques reveals that the proposed scheme outperforms its counterparts in terms of nucleus and cytoplasm extraction. Conclusions In this work, a neural network based lymphocyte image segmentation scheme is designed for automated leukemia detection. Desired segmentation accuracy in terms of nucleus and cytoplasm extraction is always high in automated disease recognition system and is achieved through the proposed scheme. |
| abstract_unstemmed |
Purpose Microscopic examination of stained blood slides is an indispensable technique for hematological disease recognition. Diagnosis based on human visual interpretation is often subjected to inter and intra observer variations, slowness, tiredness and operator experience. Accurate and authentic diagnosis of hematological neoplasia is essential in the planning of suitable surgery and chemotherapy. This paper aims at proposing a fast and simple framework for lymphocyte image segmentation. Methods Accurate segmentation of lymphocyte is essential as it facilitates automated leukemia detection in blood microscopic images. In this paper image segmentation is considered as a pixel classification problem and a novel neural architecture is employed to classify each pixel into cytoplasm, nucleus or background region. The network tuned for a set of images works well for other similar stained blood images. Results Comparative analysis with other standard techniques reveals that the proposed scheme outperforms its counterparts in terms of nucleus and cytoplasm extraction. Conclusions In this work, a neural network based lymphocyte image segmentation scheme is designed for automated leukemia detection. Desired segmentation accuracy in terms of nucleus and cytoplasm extraction is always high in automated disease recognition system and is achieved through the proposed scheme. |
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| title_short |
Lymphocyte image segmentation using functional link neural architecture for acute leukemia detection |
| url |
https://dx.doi.org/10.1007/s13534-012-0056-9 |
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| author2 |
Patra, Dipti Kumar, Sunil Satpathy, Sanghamitra |
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Patra, Dipti Kumar, Sunil Satpathy, Sanghamitra |
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| doi_str |
10.1007/s13534-012-0056-9 |
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2024-07-04T01:02:31.832Z |
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7.400464 |