Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart Cities
Smart cities are a future reality for municipalities around the world. Healthcare services play a vital role in the transformation of traditional cities into smart cities. In this paper, we present a ubiquitous and quality computer-aided blood analysis service for the detection and counting of white...
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| Format: | Article |
| Language: | English |
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IEEE
2017-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/7782368/ |
| _version_ | 1818927899753840640 |
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| author | Muhammad Sajjad Siraj Khan Zahoor Jan Khan Muhammad Hyeonjoon Moon Jin Tae Kwak Seungmin Rho Sung Wook Baik Irfan Mehmood |
| author_facet | Muhammad Sajjad Siraj Khan Zahoor Jan Khan Muhammad Hyeonjoon Moon Jin Tae Kwak Seungmin Rho Sung Wook Baik Irfan Mehmood |
| author_sort | Muhammad Sajjad |
| collection | DOAJ |
| description | Smart cities are a future reality for municipalities around the world. Healthcare services play a vital role in the transformation of traditional cities into smart cities. In this paper, we present a ubiquitous and quality computer-aided blood analysis service for the detection and counting of white blood cells (WBCs) in blood samples. WBCs also called leukocytes or leucocytes are the cells of the immune system that are involved in protecting the body against both infectious disease and foreign invaders. Analysis of leukocytes provides valuable information to medical specialists, helping them in diagnosing different important hematic diseases, such as AIDS and blood cancer (Leukaemia). However, this task is prone to errors and can be time-consuming. A mobile-cloud-assisted detection and classification of leukocytes from blood smear images can enhance accuracy and speed up the detection of WBCs. In this paper, we propose a smartphone-based cloud-assisted resource aware framework for localization of WBCs within microscopic blood smear images using a trained multi-class ensemble classification mechanism in the cloud. In the proposed framework, nucleus is first segmented, followed by extraction of texture, statistical, and wavelet features. Finally, the detected WBCs are categorized into five classes: basophil, eosinophil, neutrophil, lymphocyte, and monocyte. Experimental results on numerous benchmark databases validate the effectiveness and efficiency of the proposed system in comparison to the other state-of-the-art schemes. |
| first_indexed | 2024-12-20T03:20:21Z |
| format | Article |
| id | doaj.art-b870707865794afa8ecd93c1aa4b2c5b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T03:20:21Z |
| publishDate | 2017-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b870707865794afa8ecd93c1aa4b2c5b2022-12-21T19:55:14ZengIEEEIEEE Access2169-35362017-01-0153475348910.1109/ACCESS.2016.26362187782368Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart CitiesMuhammad Sajjad0Siraj Khan1Zahoor Jan2Khan Muhammad3https://orcid.org/0000-0002-5302-1150Hyeonjoon Moon4Jin Tae Kwak5Seungmin Rho6https://orcid.org/0000-0003-1936-6785Sung Wook Baik7Irfan Mehmood8https://orcid.org/0000-0001-7864-957XDepartment of Computer Science, Digital Image Processing Laboratory, Islamia College Peshawar, PakistanDepartment of Computer Science, Digital Image Processing Laboratory, Islamia College Peshawar, PakistanDepartment of Computer Science, Digital Image Processing Laboratory, Islamia College Peshawar, PakistanDepartment of Software, Intelligent Media Laboratory, College of Software Convergence, Sejong University, Seoul, South KoreaDepartment of Computer Science and Engineering, Sejong University, Seoul, South KoreaDepartment of Computer Science and Engineering, Sejong University, Seoul, South KoreaDepartment of Media Software, Sungkyul University, Anyang, South KoreaDepartment of Software, Intelligent Media Laboratory, College of Software Convergence, Sejong University, Seoul, South KoreaDepartment of Computer Science and Engineering, Sejong University, Seoul, South KoreaSmart cities are a future reality for municipalities around the world. Healthcare services play a vital role in the transformation of traditional cities into smart cities. In this paper, we present a ubiquitous and quality computer-aided blood analysis service for the detection and counting of white blood cells (WBCs) in blood samples. WBCs also called leukocytes or leucocytes are the cells of the immune system that are involved in protecting the body against both infectious disease and foreign invaders. Analysis of leukocytes provides valuable information to medical specialists, helping them in diagnosing different important hematic diseases, such as AIDS and blood cancer (Leukaemia). However, this task is prone to errors and can be time-consuming. A mobile-cloud-assisted detection and classification of leukocytes from blood smear images can enhance accuracy and speed up the detection of WBCs. In this paper, we propose a smartphone-based cloud-assisted resource aware framework for localization of WBCs within microscopic blood smear images using a trained multi-class ensemble classification mechanism in the cloud. In the proposed framework, nucleus is first segmented, followed by extraction of texture, statistical, and wavelet features. Finally, the detected WBCs are categorized into five classes: basophil, eosinophil, neutrophil, lymphocyte, and monocyte. Experimental results on numerous benchmark databases validate the effectiveness and efficiency of the proposed system in comparison to the other state-of-the-art schemes.https://ieeexplore.ieee.org/document/7782368/Healthcare in smart citieshaematologyimage classificationimage segmentationleukocytes classificationmobile-cloud computing |
| spellingShingle | Muhammad Sajjad Siraj Khan Zahoor Jan Khan Muhammad Hyeonjoon Moon Jin Tae Kwak Seungmin Rho Sung Wook Baik Irfan Mehmood Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart Cities IEEE Access Healthcare in smart cities haematology image classification image segmentation leukocytes classification mobile-cloud computing |
| title | Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart Cities |
| title_full | Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart Cities |
| title_fullStr | Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart Cities |
| title_full_unstemmed | Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart Cities |
| title_short | Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart Cities |
| title_sort | leukocytes classification and segmentation in microscopic blood smear a resource aware healthcare service in smart cities |
| topic | Healthcare in smart cities haematology image classification image segmentation leukocytes classification mobile-cloud computing |
| url | https://ieeexplore.ieee.org/document/7782368/ |
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