Scalable system for classification of white blood cells from Leishman stained blood stain images
Introduction: The White Blood Cell (WBC) differential count yields clinically relevant information about health and disease. Currently, pathologists manually annotate the WBCs, which is time consuming and susceptible to error, due to the tedious nature of the process. This study aims at automation o...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2013-01-01
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| Series: | Journal of Pathology Informatics |
| Subjects: | |
| Online Access: | http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2013;volume=4;issue=2;spage=15;epage=15;aulast=Mathur |
| _version_ | 1811232760319180800 |
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| author | Atin Mathur Ardhendu S Tripathi Manohar Kuse |
| author_facet | Atin Mathur Ardhendu S Tripathi Manohar Kuse |
| author_sort | Atin Mathur |
| collection | DOAJ |
| description | Introduction: The White Blood Cell (WBC) differential count yields clinically relevant information about health and disease. Currently, pathologists manually annotate the WBCs, which is time consuming and susceptible to error, due to the tedious nature of the process. This study aims at automation of the Differential Blood Count (DBC) process, so as to increase productivity and eliminate human errors. Materials and Methods: The proposed system takes the peripheral Leishman blood stain images as the input and generates a count for each of the WBC subtypes. The digitized microscopic images are stain normalized for the segmentation, to be consistent over a diverse set of slide images. Active contours are employed for robust segmentation of the WBC nucleus and cytoplasm. The seed points are generated by processing the images in Hue-Saturation-Value (HSV) color space. An efficient method for computing a new feature, ′number of lobes,′ for discrimination of WBC subtypes, is introduced in this article. This method is based on the concept of minimization of the compactness of each lobe. The Naive Bayes classifier, with Laplacian correction, provides a fast, efficient, and robust solution to multiclass categorization problems. This classifier is characterized by incremental learning and can also be embedded within the database systems. Results: An overall accuracy of 92.45% and 92.72% over the training and testing sets has been obtained, respectively. Conclusion: Thus, incremental learning is inducted into the Naive Bayes Classifier, to facilitate fast, robust, and efficient classification, which is evident from the high sensitivity achieved for all the subtypes of WBCs. |
| first_indexed | 2024-04-12T11:08:33Z |
| format | Article |
| id | doaj.art-f3cb694c28a9418eb590b6e48a66bb4e |
| institution | Directory Open Access Journal |
| issn | 2153-3539 2153-3539 |
| language | English |
| last_indexed | 2024-04-12T11:08:33Z |
| publishDate | 2013-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pathology Informatics |
| spelling | doaj.art-f3cb694c28a9418eb590b6e48a66bb4e2022-12-22T03:35:40ZengElsevierJournal of Pathology Informatics2153-35392153-35392013-01-0142151510.4103/2153-3539.109883Scalable system for classification of white blood cells from Leishman stained blood stain imagesAtin MathurArdhendu S TripathiManohar KuseIntroduction: The White Blood Cell (WBC) differential count yields clinically relevant information about health and disease. Currently, pathologists manually annotate the WBCs, which is time consuming and susceptible to error, due to the tedious nature of the process. This study aims at automation of the Differential Blood Count (DBC) process, so as to increase productivity and eliminate human errors. Materials and Methods: The proposed system takes the peripheral Leishman blood stain images as the input and generates a count for each of the WBC subtypes. The digitized microscopic images are stain normalized for the segmentation, to be consistent over a diverse set of slide images. Active contours are employed for robust segmentation of the WBC nucleus and cytoplasm. The seed points are generated by processing the images in Hue-Saturation-Value (HSV) color space. An efficient method for computing a new feature, ′number of lobes,′ for discrimination of WBC subtypes, is introduced in this article. This method is based on the concept of minimization of the compactness of each lobe. The Naive Bayes classifier, with Laplacian correction, provides a fast, efficient, and robust solution to multiclass categorization problems. This classifier is characterized by incremental learning and can also be embedded within the database systems. Results: An overall accuracy of 92.45% and 92.72% over the training and testing sets has been obtained, respectively. Conclusion: Thus, incremental learning is inducted into the Naive Bayes Classifier, to facilitate fast, robust, and efficient classification, which is evident from the high sensitivity achieved for all the subtypes of WBCs.http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2013;volume=4;issue=2;spage=15;epage=15;aulast=MathurIncremental learningnaive bayes classifiernumber of lobeswhite blood cells classification |
| spellingShingle | Atin Mathur Ardhendu S Tripathi Manohar Kuse Scalable system for classification of white blood cells from Leishman stained blood stain images Journal of Pathology Informatics Incremental learning naive bayes classifier number of lobes white blood cells classification |
| title | Scalable system for classification of white blood cells from Leishman stained blood stain images |
| title_full | Scalable system for classification of white blood cells from Leishman stained blood stain images |
| title_fullStr | Scalable system for classification of white blood cells from Leishman stained blood stain images |
| title_full_unstemmed | Scalable system for classification of white blood cells from Leishman stained blood stain images |
| title_short | Scalable system for classification of white blood cells from Leishman stained blood stain images |
| title_sort | scalable system for classification of white blood cells from leishman stained blood stain images |
| topic | Incremental learning naive bayes classifier number of lobes white blood cells classification |
| url | http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2013;volume=4;issue=2;spage=15;epage=15;aulast=Mathur |
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