Zainab Yahya
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Preferred name
Zainab Yahya
Official Name
Zainab, Yahya
Alternative Name
Yahya, Zainab
Yahya, Z
Main Affiliation
Scopus Author ID
55669781700
Researcher ID
DYK-8082-2022

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  • Publication
    An overview of multi-filters for eliminating impulse noise for digital images
    ( 2020-02-01)
    Abdurrazzaq A.
    ;
    Ahmad Kadri Junoh
    ;
    Wan Zuki Azman Wan Muhamad
    ;
    Zainab Yahya
    ;
    Mohd I.
    An image through the digitization process is referred to as a digital image. The quality of the digital image may be degenerating due to interferences on the acquisition, transmission, extraction, etc. This attracted the attention of many researchers to study the causes of damage to the information in the image. In addition to finding cause of image damage, the researchers also looking for ways to overcome this problem. There are many filtering techniques that have been introduced to deal the damage to the information in the image. In addition to eliminating noise from the image, filtering techniques also aims to maintain the originality of the features in the image. Among the many research papers on image filtering there is a lack of review papers which are an important to facilitate researchers in understanding the differences in each filtering technique. Additionally, it helps researchers determine the direction of research conducted based on the results of previous research. Therefore, this paper presents a review of several filtering techniques that have been developed so far.
  • Publication
    Tropical algebra based adaptive filter for noise removal in digital image
    ( 2020-07-01)
    Abdurrazzaq A.
    ;
    Mohd I.
    ;
    Ahmad Kadri Junoh
    ;
    Zainab Yahya
    The concept of the tropical algebra was first introduced to solve problems in mathematical economy such as optimization and approximation problems. In this paper, the concept of tropical algebra is used to build an image filtering algorithm. By using this concept, the lowest and highest pixel values are considered in determining the new pixel value. In addition, adaptive window will also be implemented to help the filtering process become more effective at high density noise. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) are used to evaluate the output image quality produced by the filtering method. In this experiment, the performance of proposed method and existing methods such as switching median filter (SMF), adaptive fuzzy noise switching median filter (NAFSM), modified decision based on unsymmetric trimmed median filter (MDBUTMF), adaptive type-2 fuzzy filter (AT2FF)), based on pixel density filters (BPDF), different applied median filters (DAMF), and tropical SVD filters (TSVD) will be compared. PSNR and SSIM results show that the proposed method outperforms most existing methods: SMF (27.13/0.7954), NAFSM (29.11/0.8459), MDBTUMF (29.18/0.8462), AT2FF (28.10/0.8159), BPDF (25.65/0.7545), DAMF (31.20/0.8833), TSVD (28.39/0.7906), and proposed (31.26/0.8827).
  • Publication
    New white blood cell detection technique by using singular value decomposition concept: White blood cell detection technique
    ( 2021-01-01)
    Abdurrazzaq A.
    ;
    Ahmad Kadri Junoh
    ;
    Zainab Yahya
    ;
    Mohd I.
    Segmentation technique is a commonly used method to detect white blood cells. The segmentation technique aims to separate the blood image into several parts based on the similarity of features in the image. Therefore, the detection results do not completely contain white blood cells but also contain other parts with similar features to white blood cells. This study proposes a new detection technique that directly considers the features of white blood cells using singular value decomposition approach. The experimental results show that the proposed method works better in detecting white blood cell nuclei than the existing methods. The existing methods only work well for white blood cells with dense color intensities such as basophil and monocyte. Meanwhile, the proposed method works well overall as it directly compares the level of similarity in white blood cells.