Aimi Salihah Abdul Nasir
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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
    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.
  • Publication
    An intelligent diagnostic system for malaria
    ( 2015)
    Aimi Salihah Abdul Nasir
    Malaria is a serious health problem, causing many deaths and morbidity cases throughout the world particularly in Africa and south Asia. In 2013, there were about 198 million cases of malaria and an estimation of 584,000 deaths recorded globally, mostly among African children. Malaria is caused by infection of red blood cells with protozoan parasite of the genus Plasmodium. Plasmodium Falciparum and Plasmodium Vivax are the two main species that have caused the most malaria infections worldwide. Malaria can become lifethreatening if it is not treated immediately. Until now, microscopy-based diagnosis still remains the most widely used approaches for malaria diagnosis. However, this subjective evaluation procedure is time consuming, labour intensive and requires special training. Thus, this research has developed an intelligent diagnostic system for malaria which consists of image processing and intelligent classifier for the purpose of malaria diagnosis. A 3-stage classification of intelligent diagnostic system can be used as an early detection for malaria based on the classification of blood samples between normal and malaria on the first stage, and further classify the malaria sample as either P. falciparum or P. vivax species on the second stage, along with its four different life-cycle stages which are young trophozoite, mature trophozoite, schizont and gametocyte on the final stage. In order to perform the diagnosis process, the blood images were processed with various image processing techniques such as contrast enhancement and image segmentation for obtaining a fully segmented malaria parasite. As for contrast enhancement, this study proposed modified global and modified linear contrast stretching based on total pixel approach, as well as modified global and modified linear contrast stretching based on pixel level approach. After the image has been enhanced, the malaria image was segmented using different types of clustering algorithms. This included the used of the proposed enhanced kmeans clustering. The combination between contrast enhancement and image segmentation have provided good segmented malaria parasite. Later, various features such as size, shape and colour based features were extracted from the segmented malaria parasite. These features were fed as inputs to the three different classifiers which are multilayered perceptron (MLP) neural network trained by Levenberg-Marquardt (LM) algorithm, singlehidden layer feed forward neural network (SLFN) trained by online sequential extreme learning machine (OS-ELM) algorithm and random forest. The MLP network trained by LM algorithm has been proven to be the best with the highest classification performance as compared to others. Overall, the intelligent diagnostic system for malaria that has been developed using MLP network trained by LM algorithm is capable to perform the detection process by classifying a total of 1800 images consisting of malaria and normal blood images with testing accuracy, sensitivity and specificity of 95.28%, 96.06% and 86.00%, respectively. As for the diagnosis process, the system has classified a total of 1453 malaria images (accuracy of 90.81%) correctly into P. falciparum and P. vivax species, along with their four life-cycle stages. Thus, the proposed intelligent diagnostic system for malaria parasites is capable to perform the detection of malaria parasites, and then further diagnose the detected malaria parasites into its species and life-cycle stages.
  • 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.
  • Publication
    Comparative analysis of conventional and modern high-rise hotels in Penang based on hourly simulation of cooling load performance using DesignBuilder
    (Semarak Ilmu Publishing, 2023)
    Muhammad Hafeez Abdul Nasir
    ;
    Ahmad Sanusi Hassan
    ;
    Aimi Salihah Abdul Nasir
    ;
    Mohd Suhaimi Mohd-Danuri
    ;
    Mohd Nasrun Mohd Nawi
    ;
    Rafikullah Deraman
    The study examines the energy efficiency performance of hotel façades in relation to the annual cooling load simulation. In achieving the objective, two case studies of high-rise city hotels are selected within the locality of Penang, Malaysia. The case studies are selected based on the year of construction coupled with the architectural styles encompassing conventional and modern design of hotel facades. In traditional hotel facades, passive design elements, including proper window and wall materials selection alongside window-to-wall ratio (WWR), are less significant. Comparatively, elements of passive design in modern hotel facades are notable. In assessing the thermal performance of the hotel façade, a case study of the conventional and modern high-rise city hotels in Penang are selected to undergo hourly cooling load simulation in the hotel guestroom using the DesignBuilder simulation program in establishing the hotel’s energy efficiency performance. The findings revealed the average annual cooling energy of the conventional and modern high-rise city hotel guestrooms is 553 kWh/m2 and 538 kWh/m2, respectively. The study concludes the elements of passive design, including WWR, window material selection, and external wall colour are crucial in determining energy-efficient hotel operations.
  • Publication
    An Identification of Aspergillus Species: A Comparison on Supervised Classification Methods
    ( 2022-01-01)
    Nur Rodiatul Raudah Mohamed Radzuan
    ;
    Haryati Jaafar
    ;
    Aimi Salihah Abdul Nasir
    Aspergillus is one of the well-known existed saprophytic fungi that can withstand with various environments. Other can be beneficial in food industry, it also can be infectious to human and animals and normally, it attacks those with low immunity level. In order to keep the treatment in track with more accurate analysis, identification of Aspergillus plays an important role. Identification of Aspergillus is solely based on its characteristic and currently, there are two methods used which are microscopic and macroscopic examinations to observe its features. It handled by experienced microscopist and a few confirmations had to be done before presenting out the final result. Therefore, to prevent misidentification, an automated based identification is proposed. In this paper, different supervised classifiers are tested and compared to observe their ability to detect different 162 of Aspergillus images. The features have been extracted by using Principal component analysis (PCA) and several classifiers such as k- nearest neighbour (kNN), Sparse Representation Classifier (SRC), Support Vector Machine (SVM), Improved Fuzzy-Based k Nearest Centroid Neighbor (IFkNCN) and Kernal Sparse Representation Classifier (KSRC) are employed. Based on its accuracy, Aspergillus flavus recorded 80% of accuracy for all the classifiers.
      1
  • Publication
    A fast and efficient segmentation of soil-transmitted helminths through various color models and k-means clustering
    ( 2021-01-01)
    Norhanis Ayunie Ahmad Khairudin
    ;
    Aimi Salihah Abdul Nasir
    ;
    Lim Chee Chin
    ;
    Haryati Jaafar
    ;
    Mohamed Z.
    Soil-transmitted helminths (STH) are one of the causes of health problems in children and adults. Based on a large number of helminthiases cases that have been diagnosed, a productive system is required for the identification and classification of STH in ensuring the health of the people is guaranteed. This paper presents a fast and efficient method to segment two types of STH; Ascaris Lumbricoides Ova (ALO) and Trichuris Trichiura Ova (TTO) based on the analysis of various color models. Firstly, the ALO and TTO images are enhanced using modified global contrast stretching (MGCS) technique, followed by the extraction of color components from various color models. In this study, segmentation based on various color models such as RGB, HSV, L*a*b and NSTC have been used to identify, simplify and extract the particular color needed. Then, k-means clustering is used to segment the color component images into three clusters region which are target (helminth eggs), unwanted and background regions. Then, additional processing steps are applied on the segmented images to remove the unwanted region from the images and to restore the information of the images. The proposed techniques have been evaluated on 100 images of ALO and TTO. Results obtained show saturation component of HSV color model is the most suitable color component to be used with the k-means clustering technique on ALO and TTO images which achieve segmentation performance of 99.06% for accuracy, 99.31% for specificity and 95.06% for sensitivity.
      1  13
  • Publication
    Fast k-means clustering algorithm for malaria detection in thick blood smear
    ( 2020-11-09)
    Aris T.A.
    ;
    Aimi Salihah Abdul Nasir
    ;
    Lim Chee Chin
    ;
    Haryati Jaafar
    ;
    Mohamed Z.
    Lots of people all over the world is threaten by a popular blood infection illness that is called as malaria. According to this fact, immediate diagnosis tests are essential to avoid the malaria parasites from expanding in every part of the body. Malaria detection is based on parasitic count process on thick blood smear samples. Anyhow, this mechanism consist the chances of misinterpretation of parasites on behalf to human flaws. Thus, this research objective is to investigate the segmentation performance for improving malaria detection in thick blood smear images through fast k-means clustering algorithm on various color models. In this research, fast kmeans clustering is used because of its advantage which is no need to retrain cluster center that causes time taken to train the image cluster centers is reduce. Meanwhile, different color models have been utilized in order to identify the most relevant color model that obviously highlight the parasites. Five varied color models namely RGB, XYZ, HSV, YUV and CMY are selected and 15 color components namely R, G, B, X, Y, Z, H, S, V, Y, U, V, C, M and Y component have been derived with the aim to discover which color component is the topnotch for malaria parasites detection. In general, around 100 thick blood smear images have been tested in this study and the outcomes reveal that the best segmentation performance is segmentation through R component of RGB with 99.81% accuracy.
      10  3
  • Publication
    Character segmentation for automatic vehicle license plate recognition based on fast k-means clustering
    ( 2020-11-09)
    Ariff F.N.M.
    ;
    Aimi Salihah Abdul Nasir
    ;
    Haryati Jaafar
    ;
    Zulkifli A.N.
    Automatic vehicle license plate recognition (AVLPR) system is one of application for transportation area under intelligent transport system. This system helps in monitor and identify the vehicle by reading the vehicles license plate numbers and recognize the plate characters automatically. However, various factors such as diversity of plate character viewpoint, shape, format and unstable light conditions at the time of image acquisition were obtained, have challenged the system to segment and recognize the characters. Therefore, this paper, presents an effective procedure approached based on fast k-mean (FKM) clustering. FKM approached have an ability to shortening the time of the image cluster centers process consumed. In addition, the FKM algorithm also able to overcomes the cluster center re-processing problem when constantly added the image in huge quantities. The proposed procedure begins with enhancing the input image by using modified white patch and converted into grayscale image. A total of 100 of images has been tested for the segmentation process with clustering techniques approach used. Template matching is used to standardize the recognition results obtained. The highest achieved was 88.57% of average accuracy for FKM clustering technique compared to k-means clustering where it was only able to achieve an average accuracy of 85.78% and 86.14% for fuzzy c-means. Thus, this show that the most efficient, quicker and more useful algorithm goes to FKM rather than the algorithm for fuzzy c-means (FCM) and k-means (KM). Therefore, it is possible toward consider the proposed FKM clustering as an image segmentation method for segmenting license plate images.
      4  6
  • Publication
    Image segmentation using k-means clustering and otsu's thresholding with classification method for human intestinal parasites
    ( 2020-07-09)
    Khairudin Norhanis Ayunie Ahmad
    ;
    Rohaizad Nurfatin Shamimi
    ;
    Aimi Salihah Abdul Nasir
    ;
    Lim Chee Chin
    ;
    Haryati Jaafar
    ;
    Mohamed Z.
    Helminth is one of the intestinal parasites that may cause harm and death to human. It is very important to have a system that is capable of assisting the technologist in investigating of fecal samples. In this paper, an automatic classification process is proposed to detect the different types of helminth eggs from fecal samples by using image processing technique. 50 samples of Ascaris Lumbricoides Ova (ALO) and Trichuris Trichiura Ova (TTO) are tested. First, these images undergo partial contrast stretching (PCS) technique to enhance the target images. Next, RGB and HSV color model have been compared in order to identify which color component is able to ease the segmentation process. S component shows a good results with high contrast between the target and the unwanted region. Then, Otsu's thresholding and k-means clustering are compared in order to to select the most suitable image processing method to be used in classification procedure. k-means clustering shows a better results compared to Otsu's thresholding. In classification process, area and size have been chosen as the feature to extract for the classification. The ratio for successfully detected ALO species is 84% while TTO is 76%.
      3  9
  • Publication
    Comparability of edge detection techniques for automatic vehicle license plate detection and recognition
    ( 2021-01-01)
    Fatin Norazima Mohamad Ariff
    ;
    Aimi Salihah Abdul Nasir
    ;
    Haryati Jaafar
    ;
    Zulkifli A.N.
    License plate recognition system is one of the famous topics in image processing to identify the vehicle registration number. This system has been given a lot of beneficial toward transportation system, especially for security system. However, to get the perfect segmentation on alphabet shape for recognition purpose is quite challenging due to the non-uniform condition of image acquisition. Hence this paper proposes a methodology for segmentation of license plate number by using edge-based segmentation. In this study, image segmentation based on edge detection has been chosen due to the sharpness and detail in detecting the shape of an object. Since there are various types of edge detection techniques have been proposed by the previous researchers, several edge detection techniques from the most commonly used techniques have been chosen to be compared and analyze the results of various edge detection for license plate recognition. In this paper, several types of edge detection techniques such as Approxcanny, Canny, Chan-Vese, Kirsch, Prewitt, Robert, Sobel, Quadtree and Zero Crossing edge detector have been compared through greyscale images. Grayscale image has been enhancing before by modified white patch. Then, the holes area of the segmented license plate image are filled to obtain the characters, followed by step for removing the unwanted objects from the segmented license plate images. Later, the characters of the license plate are recognized based on template matching approach. This recognition analysis consists of two stages. First stage is all edge detector techniques have been used same standard values in removing the noise. Five edge detectors with best performance have been selected for next stage. In the second stage, the unwanted objects have been removed with appropriate values which are suitable for each of the edge detection techniques. The final result shows that Chan-Vese conquers the analysis with highest accuracy of edge detection obtained in license plate recognition.
      1