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

ABSTRACT

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.

KEYWORDS:

• Diatom
• phytoplankton
• image enhancement
• edge masking
• image smoothing
• image brightening
• dual-image specification
• background removal

Data Availability Statement

Some data are confidential including the code. Some data are made available upon request after the publication.

Acknowledgements

The authors would like to thank all reviewers for their encouraging comments in improving this work.

Disclosure statement

We would like to declare that we do not have any commercial or associative interest that represents a conflict of interest with the work submitted.

Additional information

Funding

This research has been supported by the collaborative work of the Universiti Malaysia Pahang (UMP), International Islamic University Malaysia (IIUM), and Universiti Teknologi MARA (UiTM) under University Research Grant (RDU200745) entitled ‘Formulation of Mathematical Model to Enhance Underwater Image Quality Using Optimized Dehazing Method for Fluid-Type Algae Identification’ and research grant of the Universiti Malaysia Pahang Postgraduate Research Scheme (PGRS210363).

Notes on contributors

Mohd Aiman Syahmi Kamarul Baharin

Mohd Aiman Syahmi Kamarul Baharin received B.Eng. degree in Mechatronics from Universiti Malaysia Pahang (UMP), Malaysia in 2021. Currently he is pursuing his Master degree at the Faculty of Manufacturing and Mechatronic Engineering Technology in image processing and computer vision. His research interests include image enhancement, object identification, detection, classification, and machine learning.

Ahmad Shahrizan Abdul Ghani

Ahmad Shahrizan Abdul Ghani received his Master degree in Mechatronics Engineering from University of Applied Sciences Augsburg, Germany in 2009. In 2015, he obtained his PhD. Degree from Universiti Sains Malaysia (USM) in the areas of image processing and computer vision. He is currently a member of Malaysian Board of Engineer as well as Malaysian Board of Technologist. Since 2016, he is actively participated in teaching and research activities in Mechatronics and Image Processing areas. He has published various research papers on top journals and conferences, reviews journal and conference papers, and involves in various international conference. He is actively engaged and collaborates with industries. His research interests include computer vision, image processing, machine learning, and their applications.

Normawaty Mohammad-Noor

Normawaty Mohammad Noor, PhD, is an Associate Professor of the Department of Marine Science, Kulliyyah of Science, International Islamic University Malaysia (IIUM), Malaysia. Her PhD was on Marine Biology from University of Copenhagen, Denmark. Her main research interest is on taxonomy and ecology of marine algae. She has been working on this field for more than 15 years. She has published numerous scientific publications and books.

Hasnun Nita Ismail

Hasnun Nita Ismail, PhD is a Senior Lecturer of Faculty of Science, University Technology MARA of Perak Branch Tapah Campus, Malaysia. Her research area focuses on water quality analysis and plankton ecology (phytoplankton and zooplankton) since the year of 2000. She has published her research findings in several book chapters, journal articles, and articles for scientific magazines at national and international levels. Presently, she has expanded her research interest to the biological study on the freshwater apple snail as a pest species in the rice field.

Syafiq Qhushairy Syamsul Amri

Syafiq Qhushairy Syamsul Amri received the B.Eng. degree in Mechatronics from Universiti Malaysia Pahang (UMP), Malaysia in 2021. He is a Master candidate at the Faculty of Manufacturing and Mechatronics Engineering Technology in the areas of image processing and computer vision. His current research interest includes underwater image enhancement, robotics, and deep learning. He is also actively participated in industrial attachment with a robotics company.