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All Journal International Journal of Applied Mathematics, Sciences, and Technology for National Defense
Ahmad Kadri bin Junoh
Universiti Malaysia Perlis
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Mathematics Physics

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White blood cell segmentation by using vector color approach Achmad Abdurrazzaq; Ahmad Kadri bin Junoh; Ilyasa Pahlevi Reza Yulianto
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol. 1 No. 1 (2023): International Journal of Applied Mathematics, Sciences, and Technology for Nati
Publisher : FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/app.sci.def.v1i1.158

Abstract

White blood cells play an important role in protecting the body from various types of diseases, therefore blood tests are done to determine a person’s disease. Diagnosis of blood diseases is done by analyzing and counting the white blood cells in the blood. Optimal calculations and analysis of white blood cell are important, so research on white blood cell detection is a rapidly growing topic. Many detection methods are proposed using the existing segmentation techniques. In this paper, the detection method is proposed using vector operation approach instead general segmentation technique. From the experiments performed, it is shown that the proposed method can detect white blood cells on smear images. Further, this method extracts the nucleus from white blood cells well.
Bibliometrix research of noise removal techniques in digital images for defense Fulkan Kafilah Al Husein; Muhammad Yusuf Al Habsy; Damaris Nugrahita Christi; Agnes Emanuela Hutagaol; Ahmad Kadri bin Junoh
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol. 3 No. 1 (2025): International Journal of Applied Mathematics, Sciences, and Technology for Nati
Publisher : FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/app.sci.def.v3i1.463

Abstract

In modern defense applications, the accuracy and clarity of digital images are crucial, especially for tasks like surveillance, reconnaissance, and intelligence gathering. However, noise introduced during image acquisition or transmission significantly degrades image quality. This paper presents a comprehensive review of various noise removal techniques employed in digital image processing for defense systems. The review focuses on both linear and non-linear methods, including matrix decomposition, hybrid deep learning, Generative Adversarial Networks (GANs), and trimming filters. Emphasis is placed on the effectiveness of each technique in enhancing image quality while preserving critical details. The use of linear and non-linear methods such as deep learning-based approaches is shown to outperform traditional linear filters in handling complex noise patterns, particularly in scenarios requiring precise object detection and image restoration. The paper highlights a comprehensive overview of the researched literature and shows the latest trends and developments in the field. Finally, recommendations for future research and the development of more robust noise reduction methods are provided, aiming to improve operational effectiveness in defense applications.
Co-Authors Achmad Abdurrazzaq Agnes Emanuela Hutagaol Damaris Nugrahita Christi Fulkan Kafilah Al Husein Ilyasa Pahlevi Reza Yulianto Muhammad Yusuf Al Habsy