Haryati Jaafar
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Haryati Jaafar
Official Name
Jaafar, Haryati
Alternative Name
Jaafar, Haryati
Jaafar, H.
Main Affiliation
Scopus Author ID
55357649900
Researcher ID
ILC-1943-2023

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  • 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
  • Publication
    Improvising non-uniform illumination and low contrast images of soil transmitted helminths image using contrast enhancement techniques
    ( 2021-01-01)
    Norhanis Ayunie Ahmad Khairudin
    ;
    Aimi Salihah Abdul Nasir
    ;
    Lim Chee Chin
    ;
    Haryati Jaafar
    ;
    Mohamed Z.
    Image enhancement plays an important role in image processing and computer vision. It is used to enhance the visual appearance in an image and also to convert the image suited to the requirement needed for image processing. In this paper, image enhancement is used to produce a better image by enhancing the image quality and highlighting the morphological features of the helminth eggs. Result obtained from enhancement is prepared for segmentation and classification process. The helminth eggs used in this paper are Ascaris Lumbricoides Ova (ALO) and Trichuris Trichiura Ova (TTO). In this study, several enhancement techniques have been performed on 100 images of ALO and TTO which have been captured under three different illuminations: normal, under-exposed and over-exposed images. The techniques used are global contrast stretching, limit contrast, linear contrast stretching, modified global contrast stretching, modified linear contrast stretching, partial contrast and reduce haze. Based on results obtained from these techniques, modified linear contrast stretching and modified global contrast stretching are able to equalize the lighting in the non-uniform illumination images of helminth eggs. Both techniques are suitable to be used on non-uniform illumination images and also able to improve the contrast in the image without affecting or removing the key features in ALO and TTO images as compared to the other techniques. Hence, the resultant images would become useful for parasitologist in analyzing helminth eggs.
      1
  • Publication
    Sauvola and Niblack Techniques Analysis for Segmentation of Vehicle License Plate
    ( 2020-07-09)
    Ariff Fatin Norazima Mohamad
    ;
    Aimi Salihah Abdul Nasir
    ;
    Haryati Jaafar
    ;
    Zulkifli A.
    License plate recognition system is functional to identify the vehicle registration number. This system is popular in image processing field. It's played important role in transportation system, especially for security system. However, variation condition of image acquisition causes the segmentation of license plate difficult to handle. This paper proposed a methodology for segmentation of license plate number by using thresholding segmentation group. In this study, image segmentation based on threshold has been chosen due to its ability in separating the foreground and the background. Hence, this technique is very useful for segmenting the characters which have tons of noise. Several threshold methods from the most commonly used techniques had been chosen to be compared and analyze the results for license plate detection and recognition. In this research, threshold techniques such as Savoula and Niblack have been select to compare. A total of 100 images captured by using a digital camera has been used the experimental analysis. After segmentation process, unwanted pixel has been removed with fixed value for each technique. Template matching has been used for classification of character recognition. The final result shows that Savoula conquers highest placed with great value in accuracy percentage of license plate recognition.
      13  4
  • Publication
    Color constancy analysis approach for color standardization on malaria thick and thin blood smear images
    ( 2021-01-01)
    Thaqifah Ahmad Aris
    ;
    Aimi Salihah Abdul Nasir
    ;
    Haryati Jaafar
    ;
    Lim Chee Chin
    ;
    Mohamed Z.
    Malaria is an extensively prevalent blood infection, the most severe and widespread parasitic disease that stirring millions of people in the world. Currently, microscopy diagnosis still the most widely used method for malaria diagnosis. However, this procedure contains the probability of miscalculation of parasites due to human error. Computerized system by using image processing is recognized as a quick and easy ways to analyze a lot of blood samples. However, because of the non-standard preparation of the blood slides which producing color varieties in different slides will result on low quality images. Hence, it is difficult to identify the existence of malaria parasites as well as observing its morphological characteristics to recognize malaria parasites. Therefore, this paper aims to analyze the standardization performance between six types of color constancy algorithms namely, gray world (GW), white patch (WP), modified white patch (MWP), progressive hybrid (PH), shades of gray (SoG) and gray edge (GE) on both thick and thin blood smear malaria images of P. falciparum and P. vivax species. Six types of color constancy algorithms standardization performance are analysed by using quantitative measure namely, peak signal to noise ratio (PSNR), normalized absolute error (NAE), mean square error (MSE) and root mean square error (RMSE). Based on the qualitative and quantitative findings, the results show that SoG algorithm is the best color constancy as compared to others proposed color constancy. SoG algorithm has achieved the highest PSNR and lowest NAE, MSE and RMSE values, thus proved that the quality of malaria images have been improved.
      2  12