Nazahah Mustafa
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Preferred name
Nazahah Mustafa
Official Name
Nazahah, Mustafa
Alternative Name
Mustafa, Nazahah Binti
Mustafa, N.
Mustafa, Nazahah
Nazahah, Mustafa
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Scopus Author ID
36656186800
Researcher ID
T-6527-2019

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  • Publication
    Understanding Domain Knowledge in Initialization Method for K-Mean Clustering Algorithm in Medical Images
    ( 2022-01-01)
    Tan X.J.
    ;
    Nazahah Mustafa
    ;
    Mohd Yusoff Mashor
    ;
    Ab Rahman K.S.
    ;
    Wan Zuki Azman Wan Muhamad
    ;
    Leow Wai Zhe
    ;
    Cheor Wai Loon
    ;
    Oung Qi Wei
    This work serves as a preliminary study to investigate and identify the applicability of domain knowledge as an initialization method for K-Mean (KM), typically in medical images. For this purpose, 20 breast histopathology images were used as data set and the evaluations are focused on the clustering of the hyperchromatic nucleus. The iteration numbers and clustering results (i.e., accuracy, over-segmentation, and under-segmentation) are benchmarked with KM++ and the conventional random initialization method. The domain knowledge initialization method is found promising by achieving lower iteration numbers (<9), higher percentage in accuracy (85.5% (±2.27)), and lower percentages in over-segmentation (8.25% (±2.23)), and under-segmentation (7.00% (±2.14)). From this study, we hypothesize that the domain knowledge initialization method has the potential to be implemented as an initialization method and is posited to overperform some established initialization methods, typically for clustering tasks in medical images.
      1  37
  • Publication
    Feature Targeted Image Enhancement for Acute Myeloid Leukemia
    ( 2023)
    Rabi'Atul' Adawiyah Abdul Rahman
    ;
    Mohd Yusoff Mashor
    ;
    Rafikha Aliana A Raof
    ;
    Rosline Hassan
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Nazahah Mustafa
    ;
    Khairul Shakir Ab Rahman
    ;
    Razan Hayati Zulkeflee
    Image enhancement is one of the pre-processing steps in various computer vision applications. The current image enhancement algorithm typically applies uniform enhancements across the entire image where this approach often falls short of accurately highlighting or enhancing the specific features due to the influence of the background color. Therefore, this paper proposes a feature-targeted image enhancement technique. Feature-targeted image enhancement (FTIE) algorithm is the improvement over the conventional technique. This method will only enhance the targeted feature instead of the entire image. Therefore, the targeted feature will be enhanced accurately without the influence of the background image. The FTIE method was done by extracting the target feature from the original images and then applying the enhancement method to that region only. Based on the 80 acute myeloid leukemia images, the proposed method showed a promising result, where the comparative analysis shows that the image produced from the proposed method surpasses other conventional methods in terms of structural similarity index (0.995), universal image quality index (0.996), peak signal-to-noise ratio (30.803), mean absolute error (0.002), correlation coefficient (0.997) and contrast enhancement-based image quality (1.743) values.
      2  26