Leukocyte Classification using Adaptive Neuro-Fuzzy Inference System in Microscopic Blood Images
Abstract Microscopic pathology is still a meticulous and biased task for hematologist, which leads to the misclassification of cells and vagueness prediction of abnormal cells due to variability in the morphological structure of leukocytes. Therefore, to enhance the detection precision and diminishi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Rawat, Jyoti [verfasserIn] Singh, Annapurna [verfasserIn] Bhadauria, H S [verfasserIn] Virmani, Jitendra [verfasserIn] Devgun, J S [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: The Arabian journal for science and engineering - Berlin : Springer, 2011, 43(2017), 12 vom: 21. Nov., Seite 7041-7058 |
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| Übergeordnetes Werk: |
volume:43 ; year:2017 ; number:12 ; day:21 ; month:11 ; pages:7041-7058 |
| Links: |
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| DOI / URN: |
10.1007/s13369-017-2959-3 |
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SPR03198102X |
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| 520 | |a Abstract Microscopic pathology is still a meticulous and biased task for hematologist, which leads to the misclassification of cells and vagueness prediction of abnormal cells due to variability in the morphological structure of leukocytes. Therefore, to enhance the detection precision and diminishing the time factor, an automatic classification system for leukocytes has been proposed. In routine clinical practice, expert hematologists observed that the nucleus plays a crucial role in the identification of the blood disorders. Accordingly, in this work, the localization of leukocyte nucleus is performed by using Chan–Vase level-set method for the design of a classification framework that differentiates between four classes of the leukocytes, i.e., eosinophils, polymorphs, monocytes and lymphocytes based on the nucleus. A dataset consisting of 162 leukocyte microscopic images is used. The images in the dataset are classified on the basis of texture, shape and color features. The feature selection method based on the linguistic hedge is applied on evaluated feature space of 92. The selected features are fed to an adaptive neuro-fuzzy classifier for the classification. The proposed framework obtained an accuracy of 98.7% after applying the adaptive neuro-fuzzy classification on selected 46 informative features. The correlation of best features and data extorted from the different microscopic images may yield a dramatic increase in diagnostic consistency in clinical pathology. The results obtained by utilization of selected optimal features and adaptive neuro-fuzzy classification system indicate that it can be routinely used in clinical environment for differential diagnosis between different classes of leukocytes. | ||
| 650 | 4 | |a Leukocyte segmentation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Chan–Vase method |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Texture features |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Shape features |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Color features |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Leukocyte classification |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Adaptive neuro-fuzzy classifier |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Singh, Annapurna |e verfasserin |4 aut | |
| 700 | 1 | |a Bhadauria, H S |e verfasserin |4 aut | |
| 700 | 1 | |a Virmani, Jitendra |e verfasserin |4 aut | |
| 700 | 1 | |a Devgun, J S |e verfasserin |4 aut | |
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10.1007/s13369-017-2959-3 doi (DE-627)SPR03198102X (SPR)s13369-017-2959-3-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Rawat, Jyoti verfasserin aut Leukocyte Classification using Adaptive Neuro-Fuzzy Inference System in Microscopic Blood Images 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Microscopic pathology is still a meticulous and biased task for hematologist, which leads to the misclassification of cells and vagueness prediction of abnormal cells due to variability in the morphological structure of leukocytes. Therefore, to enhance the detection precision and diminishing the time factor, an automatic classification system for leukocytes has been proposed. In routine clinical practice, expert hematologists observed that the nucleus plays a crucial role in the identification of the blood disorders. Accordingly, in this work, the localization of leukocyte nucleus is performed by using Chan–Vase level-set method for the design of a classification framework that differentiates between four classes of the leukocytes, i.e., eosinophils, polymorphs, monocytes and lymphocytes based on the nucleus. A dataset consisting of 162 leukocyte microscopic images is used. The images in the dataset are classified on the basis of texture, shape and color features. The feature selection method based on the linguistic hedge is applied on evaluated feature space of 92. The selected features are fed to an adaptive neuro-fuzzy classifier for the classification. The proposed framework obtained an accuracy of 98.7% after applying the adaptive neuro-fuzzy classification on selected 46 informative features. The correlation of best features and data extorted from the different microscopic images may yield a dramatic increase in diagnostic consistency in clinical pathology. The results obtained by utilization of selected optimal features and adaptive neuro-fuzzy classification system indicate that it can be routinely used in clinical environment for differential diagnosis between different classes of leukocytes. Leukocyte segmentation (dpeaa)DE-He213 Chan–Vase method (dpeaa)DE-He213 Texture features (dpeaa)DE-He213 Shape features (dpeaa)DE-He213 Color features (dpeaa)DE-He213 Leukocyte classification (dpeaa)DE-He213 Adaptive neuro-fuzzy classifier (dpeaa)DE-He213 Singh, Annapurna verfasserin aut Bhadauria, H S verfasserin aut Virmani, Jitendra verfasserin aut Devgun, J S verfasserin aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 43(2017), 12 vom: 21. Nov., Seite 7041-7058 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:43 year:2017 number:12 day:21 month:11 pages:7041-7058 https://dx.doi.org/10.1007/s13369-017-2959-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 43 2017 12 21 11 7041-7058 |
| spelling |
10.1007/s13369-017-2959-3 doi (DE-627)SPR03198102X (SPR)s13369-017-2959-3-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Rawat, Jyoti verfasserin aut Leukocyte Classification using Adaptive Neuro-Fuzzy Inference System in Microscopic Blood Images 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Microscopic pathology is still a meticulous and biased task for hematologist, which leads to the misclassification of cells and vagueness prediction of abnormal cells due to variability in the morphological structure of leukocytes. Therefore, to enhance the detection precision and diminishing the time factor, an automatic classification system for leukocytes has been proposed. In routine clinical practice, expert hematologists observed that the nucleus plays a crucial role in the identification of the blood disorders. Accordingly, in this work, the localization of leukocyte nucleus is performed by using Chan–Vase level-set method for the design of a classification framework that differentiates between four classes of the leukocytes, i.e., eosinophils, polymorphs, monocytes and lymphocytes based on the nucleus. A dataset consisting of 162 leukocyte microscopic images is used. The images in the dataset are classified on the basis of texture, shape and color features. The feature selection method based on the linguistic hedge is applied on evaluated feature space of 92. The selected features are fed to an adaptive neuro-fuzzy classifier for the classification. The proposed framework obtained an accuracy of 98.7% after applying the adaptive neuro-fuzzy classification on selected 46 informative features. The correlation of best features and data extorted from the different microscopic images may yield a dramatic increase in diagnostic consistency in clinical pathology. The results obtained by utilization of selected optimal features and adaptive neuro-fuzzy classification system indicate that it can be routinely used in clinical environment for differential diagnosis between different classes of leukocytes. Leukocyte segmentation (dpeaa)DE-He213 Chan–Vase method (dpeaa)DE-He213 Texture features (dpeaa)DE-He213 Shape features (dpeaa)DE-He213 Color features (dpeaa)DE-He213 Leukocyte classification (dpeaa)DE-He213 Adaptive neuro-fuzzy classifier (dpeaa)DE-He213 Singh, Annapurna verfasserin aut Bhadauria, H S verfasserin aut Virmani, Jitendra verfasserin aut Devgun, J S verfasserin aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 43(2017), 12 vom: 21. Nov., Seite 7041-7058 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:43 year:2017 number:12 day:21 month:11 pages:7041-7058 https://dx.doi.org/10.1007/s13369-017-2959-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 43 2017 12 21 11 7041-7058 |
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10.1007/s13369-017-2959-3 doi (DE-627)SPR03198102X (SPR)s13369-017-2959-3-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Rawat, Jyoti verfasserin aut Leukocyte Classification using Adaptive Neuro-Fuzzy Inference System in Microscopic Blood Images 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Microscopic pathology is still a meticulous and biased task for hematologist, which leads to the misclassification of cells and vagueness prediction of abnormal cells due to variability in the morphological structure of leukocytes. Therefore, to enhance the detection precision and diminishing the time factor, an automatic classification system for leukocytes has been proposed. In routine clinical practice, expert hematologists observed that the nucleus plays a crucial role in the identification of the blood disorders. Accordingly, in this work, the localization of leukocyte nucleus is performed by using Chan–Vase level-set method for the design of a classification framework that differentiates between four classes of the leukocytes, i.e., eosinophils, polymorphs, monocytes and lymphocytes based on the nucleus. A dataset consisting of 162 leukocyte microscopic images is used. The images in the dataset are classified on the basis of texture, shape and color features. The feature selection method based on the linguistic hedge is applied on evaluated feature space of 92. The selected features are fed to an adaptive neuro-fuzzy classifier for the classification. The proposed framework obtained an accuracy of 98.7% after applying the adaptive neuro-fuzzy classification on selected 46 informative features. The correlation of best features and data extorted from the different microscopic images may yield a dramatic increase in diagnostic consistency in clinical pathology. The results obtained by utilization of selected optimal features and adaptive neuro-fuzzy classification system indicate that it can be routinely used in clinical environment for differential diagnosis between different classes of leukocytes. Leukocyte segmentation (dpeaa)DE-He213 Chan–Vase method (dpeaa)DE-He213 Texture features (dpeaa)DE-He213 Shape features (dpeaa)DE-He213 Color features (dpeaa)DE-He213 Leukocyte classification (dpeaa)DE-He213 Adaptive neuro-fuzzy classifier (dpeaa)DE-He213 Singh, Annapurna verfasserin aut Bhadauria, H S verfasserin aut Virmani, Jitendra verfasserin aut Devgun, J S verfasserin aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 43(2017), 12 vom: 21. Nov., Seite 7041-7058 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:43 year:2017 number:12 day:21 month:11 pages:7041-7058 https://dx.doi.org/10.1007/s13369-017-2959-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 43 2017 12 21 11 7041-7058 |
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10.1007/s13369-017-2959-3 doi (DE-627)SPR03198102X (SPR)s13369-017-2959-3-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Rawat, Jyoti verfasserin aut Leukocyte Classification using Adaptive Neuro-Fuzzy Inference System in Microscopic Blood Images 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Microscopic pathology is still a meticulous and biased task for hematologist, which leads to the misclassification of cells and vagueness prediction of abnormal cells due to variability in the morphological structure of leukocytes. Therefore, to enhance the detection precision and diminishing the time factor, an automatic classification system for leukocytes has been proposed. In routine clinical practice, expert hematologists observed that the nucleus plays a crucial role in the identification of the blood disorders. Accordingly, in this work, the localization of leukocyte nucleus is performed by using Chan–Vase level-set method for the design of a classification framework that differentiates between four classes of the leukocytes, i.e., eosinophils, polymorphs, monocytes and lymphocytes based on the nucleus. A dataset consisting of 162 leukocyte microscopic images is used. The images in the dataset are classified on the basis of texture, shape and color features. The feature selection method based on the linguistic hedge is applied on evaluated feature space of 92. The selected features are fed to an adaptive neuro-fuzzy classifier for the classification. The proposed framework obtained an accuracy of 98.7% after applying the adaptive neuro-fuzzy classification on selected 46 informative features. The correlation of best features and data extorted from the different microscopic images may yield a dramatic increase in diagnostic consistency in clinical pathology. The results obtained by utilization of selected optimal features and adaptive neuro-fuzzy classification system indicate that it can be routinely used in clinical environment for differential diagnosis between different classes of leukocytes. Leukocyte segmentation (dpeaa)DE-He213 Chan–Vase method (dpeaa)DE-He213 Texture features (dpeaa)DE-He213 Shape features (dpeaa)DE-He213 Color features (dpeaa)DE-He213 Leukocyte classification (dpeaa)DE-He213 Adaptive neuro-fuzzy classifier (dpeaa)DE-He213 Singh, Annapurna verfasserin aut Bhadauria, H S verfasserin aut Virmani, Jitendra verfasserin aut Devgun, J S verfasserin aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 43(2017), 12 vom: 21. Nov., Seite 7041-7058 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:43 year:2017 number:12 day:21 month:11 pages:7041-7058 https://dx.doi.org/10.1007/s13369-017-2959-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 43 2017 12 21 11 7041-7058 |
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10.1007/s13369-017-2959-3 doi (DE-627)SPR03198102X (SPR)s13369-017-2959-3-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Rawat, Jyoti verfasserin aut Leukocyte Classification using Adaptive Neuro-Fuzzy Inference System in Microscopic Blood Images 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Microscopic pathology is still a meticulous and biased task for hematologist, which leads to the misclassification of cells and vagueness prediction of abnormal cells due to variability in the morphological structure of leukocytes. Therefore, to enhance the detection precision and diminishing the time factor, an automatic classification system for leukocytes has been proposed. In routine clinical practice, expert hematologists observed that the nucleus plays a crucial role in the identification of the blood disorders. Accordingly, in this work, the localization of leukocyte nucleus is performed by using Chan–Vase level-set method for the design of a classification framework that differentiates between four classes of the leukocytes, i.e., eosinophils, polymorphs, monocytes and lymphocytes based on the nucleus. A dataset consisting of 162 leukocyte microscopic images is used. The images in the dataset are classified on the basis of texture, shape and color features. The feature selection method based on the linguistic hedge is applied on evaluated feature space of 92. The selected features are fed to an adaptive neuro-fuzzy classifier for the classification. The proposed framework obtained an accuracy of 98.7% after applying the adaptive neuro-fuzzy classification on selected 46 informative features. The correlation of best features and data extorted from the different microscopic images may yield a dramatic increase in diagnostic consistency in clinical pathology. The results obtained by utilization of selected optimal features and adaptive neuro-fuzzy classification system indicate that it can be routinely used in clinical environment for differential diagnosis between different classes of leukocytes. Leukocyte segmentation (dpeaa)DE-He213 Chan–Vase method (dpeaa)DE-He213 Texture features (dpeaa)DE-He213 Shape features (dpeaa)DE-He213 Color features (dpeaa)DE-He213 Leukocyte classification (dpeaa)DE-He213 Adaptive neuro-fuzzy classifier (dpeaa)DE-He213 Singh, Annapurna verfasserin aut Bhadauria, H S verfasserin aut Virmani, Jitendra verfasserin aut Devgun, J S verfasserin aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 43(2017), 12 vom: 21. Nov., Seite 7041-7058 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:43 year:2017 number:12 day:21 month:11 pages:7041-7058 https://dx.doi.org/10.1007/s13369-017-2959-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 43 2017 12 21 11 7041-7058 |
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Rawat, Jyoti @@aut@@ Singh, Annapurna @@aut@@ Bhadauria, H S @@aut@@ Virmani, Jitendra @@aut@@ Devgun, J S @@aut@@ |
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Therefore, to enhance the detection precision and diminishing the time factor, an automatic classification system for leukocytes has been proposed. In routine clinical practice, expert hematologists observed that the nucleus plays a crucial role in the identification of the blood disorders. Accordingly, in this work, the localization of leukocyte nucleus is performed by using Chan–Vase level-set method for the design of a classification framework that differentiates between four classes of the leukocytes, i.e., eosinophils, polymorphs, monocytes and lymphocytes based on the nucleus. A dataset consisting of 162 leukocyte microscopic images is used. The images in the dataset are classified on the basis of texture, shape and color features. The feature selection method based on the linguistic hedge is applied on evaluated feature space of 92. The selected features are fed to an adaptive neuro-fuzzy classifier for the classification. The proposed framework obtained an accuracy of 98.7% after applying the adaptive neuro-fuzzy classification on selected 46 informative features. The correlation of best features and data extorted from the different microscopic images may yield a dramatic increase in diagnostic consistency in clinical pathology. 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|
| author |
Rawat, Jyoti |
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Rawat, Jyoti ddc 600 bkl 31.00 misc Leukocyte segmentation misc Chan–Vase method misc Texture features misc Shape features misc Color features misc Leukocyte classification misc Adaptive neuro-fuzzy classifier Leukocyte Classification using Adaptive Neuro-Fuzzy Inference System in Microscopic Blood Images |
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600 500 ASE 31.00 bkl Leukocyte Classification using Adaptive Neuro-Fuzzy Inference System in Microscopic Blood Images Leukocyte segmentation (dpeaa)DE-He213 Chan–Vase method (dpeaa)DE-He213 Texture features (dpeaa)DE-He213 Shape features (dpeaa)DE-He213 Color features (dpeaa)DE-He213 Leukocyte classification (dpeaa)DE-He213 Adaptive neuro-fuzzy classifier (dpeaa)DE-He213 |
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ddc 600 bkl 31.00 misc Leukocyte segmentation misc Chan–Vase method misc Texture features misc Shape features misc Color features misc Leukocyte classification misc Adaptive neuro-fuzzy classifier |
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ddc 600 bkl 31.00 misc Leukocyte segmentation misc Chan–Vase method misc Texture features misc Shape features misc Color features misc Leukocyte classification misc Adaptive neuro-fuzzy classifier |
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ddc 600 bkl 31.00 misc Leukocyte segmentation misc Chan–Vase method misc Texture features misc Shape features misc Color features misc Leukocyte classification misc Adaptive neuro-fuzzy classifier |
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Leukocyte Classification using Adaptive Neuro-Fuzzy Inference System in Microscopic Blood Images |
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Leukocyte Classification using Adaptive Neuro-Fuzzy Inference System in Microscopic Blood Images |
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Rawat, Jyoti |
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Rawat, Jyoti Singh, Annapurna Bhadauria, H S Virmani, Jitendra Devgun, J S |
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leukocyte classification using adaptive neuro-fuzzy inference system in microscopic blood images |
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Leukocyte Classification using Adaptive Neuro-Fuzzy Inference System in Microscopic Blood Images |
| abstract |
Abstract Microscopic pathology is still a meticulous and biased task for hematologist, which leads to the misclassification of cells and vagueness prediction of abnormal cells due to variability in the morphological structure of leukocytes. Therefore, to enhance the detection precision and diminishing the time factor, an automatic classification system for leukocytes has been proposed. In routine clinical practice, expert hematologists observed that the nucleus plays a crucial role in the identification of the blood disorders. Accordingly, in this work, the localization of leukocyte nucleus is performed by using Chan–Vase level-set method for the design of a classification framework that differentiates between four classes of the leukocytes, i.e., eosinophils, polymorphs, monocytes and lymphocytes based on the nucleus. A dataset consisting of 162 leukocyte microscopic images is used. The images in the dataset are classified on the basis of texture, shape and color features. The feature selection method based on the linguistic hedge is applied on evaluated feature space of 92. The selected features are fed to an adaptive neuro-fuzzy classifier for the classification. The proposed framework obtained an accuracy of 98.7% after applying the adaptive neuro-fuzzy classification on selected 46 informative features. The correlation of best features and data extorted from the different microscopic images may yield a dramatic increase in diagnostic consistency in clinical pathology. The results obtained by utilization of selected optimal features and adaptive neuro-fuzzy classification system indicate that it can be routinely used in clinical environment for differential diagnosis between different classes of leukocytes. |
| abstractGer |
Abstract Microscopic pathology is still a meticulous and biased task for hematologist, which leads to the misclassification of cells and vagueness prediction of abnormal cells due to variability in the morphological structure of leukocytes. Therefore, to enhance the detection precision and diminishing the time factor, an automatic classification system for leukocytes has been proposed. In routine clinical practice, expert hematologists observed that the nucleus plays a crucial role in the identification of the blood disorders. Accordingly, in this work, the localization of leukocyte nucleus is performed by using Chan–Vase level-set method for the design of a classification framework that differentiates between four classes of the leukocytes, i.e., eosinophils, polymorphs, monocytes and lymphocytes based on the nucleus. A dataset consisting of 162 leukocyte microscopic images is used. The images in the dataset are classified on the basis of texture, shape and color features. The feature selection method based on the linguistic hedge is applied on evaluated feature space of 92. The selected features are fed to an adaptive neuro-fuzzy classifier for the classification. The proposed framework obtained an accuracy of 98.7% after applying the adaptive neuro-fuzzy classification on selected 46 informative features. The correlation of best features and data extorted from the different microscopic images may yield a dramatic increase in diagnostic consistency in clinical pathology. The results obtained by utilization of selected optimal features and adaptive neuro-fuzzy classification system indicate that it can be routinely used in clinical environment for differential diagnosis between different classes of leukocytes. |
| abstract_unstemmed |
Abstract Microscopic pathology is still a meticulous and biased task for hematologist, which leads to the misclassification of cells and vagueness prediction of abnormal cells due to variability in the morphological structure of leukocytes. Therefore, to enhance the detection precision and diminishing the time factor, an automatic classification system for leukocytes has been proposed. In routine clinical practice, expert hematologists observed that the nucleus plays a crucial role in the identification of the blood disorders. Accordingly, in this work, the localization of leukocyte nucleus is performed by using Chan–Vase level-set method for the design of a classification framework that differentiates between four classes of the leukocytes, i.e., eosinophils, polymorphs, monocytes and lymphocytes based on the nucleus. A dataset consisting of 162 leukocyte microscopic images is used. The images in the dataset are classified on the basis of texture, shape and color features. The feature selection method based on the linguistic hedge is applied on evaluated feature space of 92. The selected features are fed to an adaptive neuro-fuzzy classifier for the classification. The proposed framework obtained an accuracy of 98.7% after applying the adaptive neuro-fuzzy classification on selected 46 informative features. The correlation of best features and data extorted from the different microscopic images may yield a dramatic increase in diagnostic consistency in clinical pathology. The results obtained by utilization of selected optimal features and adaptive neuro-fuzzy classification system indicate that it can be routinely used in clinical environment for differential diagnosis between different classes of leukocytes. |
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| title_short |
Leukocyte Classification using Adaptive Neuro-Fuzzy Inference System in Microscopic Blood Images |
| url |
https://dx.doi.org/10.1007/s13369-017-2959-3 |
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| author2 |
Singh, Annapurna Bhadauria, H S Virmani, Jitendra Devgun, J S |
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Singh, Annapurna Bhadauria, H S Virmani, Jitendra Devgun, J S |
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10.1007/s13369-017-2959-3 |
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2024-07-04T02:02:58.589Z |
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| score |
7.400568 |