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Automatic phytoplankton image smoothing through integrated dual image histogram specification and enhanced background removal method

Diatom is a dominant phytoplankton and commonly found in oceans or waterways. The captured phytoplankton microscopic images suffer from low contrast and surrounding debris. These images are not appropriated for identification. Integrated dual image contrast adaptive histogram specification with enha...

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Main Authors: Kamarul Baharin, Mohd Aiman Syahmi, Abdul Ghani, Ahmad Shahrizan, Mohammad Noor, Normawaty, Ismail, Hasnun Nita, Syamsul Amri, Syafiq Qhushairy
Format: Article
Language:English
Published: Taylor and Francis 2022
Subjects:
Q Science (General)
Online Access:http://irep.iium.edu.my/101506/7/101506_Automatic%20phytoplankton%20image%20smoothing.pdf
http://irep.iium.edu.my/101506/
https://www.tandfonline.com/loi/yims20
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spelling my.iium.irep.1015062022-12-01T08:40:32Z http://irep.iium.edu.my/101506/ Automatic phytoplankton image smoothing through integrated dual image histogram specification and enhanced background removal method Kamarul Baharin, Mohd Aiman Syahmi Abdul Ghani, Ahmad Shahrizan Mohammad Noor, Normawaty Ismail, Hasnun Nita Syamsul Amri, Syafiq Qhushairy Q Science (General) Diatom is a dominant phytoplankton and commonly found in oceans or waterways. The captured phytoplankton microscopic images suffer from low contrast and surrounding debris. These images are not appropriated for identification. Integrated dual image contrast adaptive histogram specification with enhanced background removal (DIHS-BR) is proposed to address these issues by automatically removes the background of the phytoplankton image and improves the image quality while cropping phytoplankton cell. DIHS-BR will automatically remove the background and noises. DIHS-BR consists of two major steps, namely, contrast adaptive histogram specification and background removal by means of edge mask cropping. Results demonstrated that DIHS-BR filtered out the image background and left only the required phytoplankton cell image. Noises are minimized, while the contrast and colour of phytoplankton cells are improved. The average edge-based contrast measure (EBCM) of 83.065 demonstrates the best contrast improvement of the proposed methods compared with the other state-of-the-art methods Taylor and Francis 2022-11-14 Article PeerReviewed application/pdf en http://irep.iium.edu.my/101506/7/101506_Automatic%20phytoplankton%20image%20smoothing.pdf Kamarul Baharin, Mohd Aiman Syahmi and Abdul Ghani, Ahmad Shahrizan and Mohammad Noor, Normawaty and Ismail, Hasnun Nita and Syamsul Amri, Syafiq Qhushairy (2022) Automatic phytoplankton image smoothing through integrated dual image histogram specification and enhanced background removal method. The Imaging Science Journal. pp. 1-27. ISSN 1368-2199 E-ISSN 1743-131X https://www.tandfonline.com/loi/yims20 10.1080/13682199.2022.2149067
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic Q Science (General)
spellingShingle Q Science (General)
Kamarul Baharin, Mohd Aiman Syahmi
Abdul Ghani, Ahmad Shahrizan
Mohammad Noor, Normawaty
Ismail, Hasnun Nita
Syamsul Amri, Syafiq Qhushairy
Automatic phytoplankton image smoothing through integrated dual image histogram specification and enhanced background removal method
description Diatom is a dominant phytoplankton and commonly found in oceans or waterways. The captured phytoplankton microscopic images suffer from low contrast and surrounding debris. These images are not appropriated for identification. Integrated dual image contrast adaptive histogram specification with enhanced background removal (DIHS-BR) is proposed to address these issues by automatically removes the background of the phytoplankton image and improves the image quality while cropping phytoplankton cell. DIHS-BR will automatically remove the background and noises. DIHS-BR consists of two major steps, namely, contrast adaptive histogram specification and background removal by means of edge mask cropping. Results demonstrated that DIHS-BR filtered out the image background and left only the required phytoplankton cell image. Noises are minimized, while the contrast and colour of phytoplankton cells are improved. The average edge-based contrast measure (EBCM) of 83.065 demonstrates the best contrast improvement of the proposed methods compared with the other state-of-the-art methods
format Article
author Kamarul Baharin, Mohd Aiman Syahmi
Abdul Ghani, Ahmad Shahrizan
Mohammad Noor, Normawaty
Ismail, Hasnun Nita
Syamsul Amri, Syafiq Qhushairy
author_facet Kamarul Baharin, Mohd Aiman Syahmi
Abdul Ghani, Ahmad Shahrizan
Mohammad Noor, Normawaty
Ismail, Hasnun Nita
Syamsul Amri, Syafiq Qhushairy
author_sort Kamarul Baharin, Mohd Aiman Syahmi
title Automatic phytoplankton image smoothing through integrated dual image histogram specification and enhanced background removal method
title_short Automatic phytoplankton image smoothing through integrated dual image histogram specification and enhanced background removal method
title_full Automatic phytoplankton image smoothing through integrated dual image histogram specification and enhanced background removal method
title_fullStr Automatic phytoplankton image smoothing through integrated dual image histogram specification and enhanced background removal method
title_full_unstemmed Automatic phytoplankton image smoothing through integrated dual image histogram specification and enhanced background removal method
title_sort automatic phytoplankton image smoothing through integrated dual image histogram specification and enhanced background removal method
publisher Taylor and Francis
publishDate 2022
url http://irep.iium.edu.my/101506/7/101506_Automatic%20phytoplankton%20image%20smoothing.pdf
http://irep.iium.edu.my/101506/
https://www.tandfonline.com/loi/yims20
_version_ 1751535940448288768
score 13.211869

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