Aimi Salihah Abdul Nasir
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Preferred name
Aimi Salihah Abdul Nasir
Official Name
Aimi Salihah, Abdul Nasir
Alternative Name
Abdul Nasir, Aimi Salihah
Abdul-Nasir, Aimi Salihah
Nasir, Aimi Salihah Abdul
Main Affiliation
Scopus Author ID
57219027157
Researcher ID
AAG-1747-2021

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  • Publication
    Analysis of the performance of SLIC super-pixel toward pre-segmentation of soil-transmitted helminth
    (AIP Publishing, 2023)
    Loke Siew Wen
    ;
    Lim Chee Chin
    ;
    Aimi Salihah Abdul Nasir
    ;
    Norhanis Ayunie Ahmad Khairudin
    ;
    Chong Yen Fook
    ;
    Mohd Yusoff Mashor
    ;
    Zeehaida Mohamed
    Soil-Transmitted Helminth (STH) infections are one of the most severe health issues in the world including Malaysia and frequently happened in an unsanitary environment within the children group. The helminth infections are diagnosed by inspecting the faeces samples manually through light microscope. However, the manual inspection method to diagnose the helminth egg is a time-consuming and challenging process especially when are huge number of samples. To increase the efficiency and accuracy of the diagnosis, an analysis of super-pixel segmentation with different parameter adjustments on four different species was carried out. This work described a Simple Linear Iterative Clustering (SLIC) super-pixel algorithm that uses different parameter settings to explore more parasites image features for a better segmentation process in the future and to analyse the effect of different SLIC parameter settings towards the pre-segmentation process. There is total 80 images collected from the four helminth egg species which are Ascaris Lumbricoides Ova (ALO), Enterobius Vermicularis Ova (EVO), Hookworm Ova (HWO) and Trichuris Trichiura Ova (TTO). The proposed approach is divided into three steps. First, the images with various lighting conditions are enhanced by the partial contrast stretching (PCS) technique. The simple linear iterative clustering (SLIC) super-pixel algorithm was implemented to the enhanced images as a pre-segmentation algorithm to form super-pixel images. Lastly, image quality assessment will be performed on the SLIC images. The SLIC parameter compactness of super-pixel, m of 5 and number of super-pixels, k of 1000 was selected because they generate the greatest PSNR value, indicating that this combination of parameters could produce high-quality images. In future, a more in-depth analysis of the parameter k and m, which impacts the form of each super-pixel and the pre-segmentation process, might improve the recommended approach.
  • Publication
    Robust Image Processing Framework for Intelligent Multi-Stage Malaria Parasite Recognition of Thick and Thin Smear Images
    ( 2023-02-01)
    Aris T.A.
    ;
    Aimi Salihah Abdul Nasir
    ;
    Wan Azani Wan Mustafa
    ;
    Mohd Yusoff Mashor
    ;
    Haryanto E.V.
    ;
    Mohamed Z.
    Malaria is a pressing medical issue in tropical and subtropical regions. Currently, the manual microscopic examination remains the gold standard malaria diagnosis method. Nevertheless, this procedure required highly skilled lab technicians to prepare and examine the slides. Therefore, a framework encompassing image processing and machine learning is proposed due to inconsistencies in manual inspection, counting, and staging. Here, a standardized segmentation framework utilizing thresholding and clustering is developed to segment parasites’ stages of P. falciparum and P. vivax species. Moreover, a multi-stage classifier is designed for recognizing parasite species and staging in both species. Experimental results indicate the effectiveness of segmenting thick smear images based on Phansalkar thresholding garnered an accuracy of 99.86%. The employment of variance and new transferring process for the clustered members, enhanced k-means (EKM) clustering has successfully segmented all malaria stages with accuracy and an F1-score of 99.20% and 0.9033, respectively. In addition, the accuracies of parasite detection, species recognition, and staging obtained through a random forest (RF) accounted for 86.89%, 98.82%, and 90.78%, respectively, simultaneously. The proposed framework enables versatile malaria parasite detection and staging with an interactive result, paving the path for future improvements by utilizing the proposed framework on all others malaria species.
      20  2
  • Publication
    Modified global and modified linear contrast stretching algorithms: new colour contrast enhancement techniques for microscopic analysis of Malaria slide images
    ( 2012-10-03)
    Aimi Salihah Abdul Nasir
    ;
    Mohd Yusoff Mashor
    ;
    Zeehaida Mohamed
    Malaria is one of the serious global health problem, causing widespread sufferings and deaths in various parts of the world. With the large number of cases diagnosed over the year, early detection and accurate diagnosis which facilitates prompt treatment is an essential requirement to control malaria. For centuries now, manual microscopic examination of blood slide remains the gold standard for malaria diagnosis. However, low contrast of the malaria and variable smears quality are some factors that may influence the accuracy of interpretation by microbiologists. In order to reduce this problem, this paper aims to investigate the performance of the proposed contrast enhancement techniques namely, modified global and modified linear contrast stretching as well as the conventional global and linear contrast stretching that have been applied on malaria images ofP. vivaxspecies. The results show that the proposed modified global and modified linear contrast stretching techniques have successfully increased the contrast of the parasites and the infected red blood cells compared to the conventional global and linear contrast stretching. Hence, the resultant images would become useful to microbiologists for identification of various stages and species of malaria.
      1  16