Haniza Yazid
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Preferred name
Haniza Yazid
Official Name
Haniza, Yazid
Alternative Name
Yazid, H.
Yazid, Haniza
Main Affiliation
Scopus Author ID
22137274300
Researcher ID
D-3830-2015

Research Output

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  • Publication
    Performance analysis of Otsu thresholding for sign language segmentation
    ( 2021-06-01)
    Tan Z.Y.
    ;
    Shafriza Nisha Basah
    ;
    Haniza Yazid
    ;
    Muhammad Juhairi Aziz Safar
    Sign language recognition system generally consists of three main processes, which are segmentation, modelling, and classification. Image segmentation plays a crucial role as the initial step in sign language recognition. Despite the many sign language recognition system algorithms proposed in the literature and their well-understood usage, their performance analyses are relatively limited. As such, the main motivation of this paper is to critically analyse the feasibility of successful sign language segmentation under variation of dynamic scene parameters such as noise, hand size, and intensity difference between hand and background. The focus is on image thresholding using Otsu technique, since it is the most commonly used in initial process of sign language segmentation. The analysis of this work was developed based on Monte Carlo statistical method, which showed that the success of sign language segmentation depends on hand size, hand background intensity difference, and noise measurement. The result showed that the sign alphabets with handheld shape like A, E, I, M, N, S, and T is easier to segment, while sign alphabets with finger-extend shape like C, D, F, G, H, K, L, P, R, U, V, W, and Y is harder to segment. Experiment using real images demonstrate the capability of the conditions to correctly predict the outcome of sign language segmentation using Otsu technique. In conclusion, the success of sign language segmentation could be predicted beforehand with obtainable scene parameters.
  • Publication
    Analysis of Optical Character Recognition using EasyOCR under Image Degradation
    ( 2023)
    Muhamad Aqil Mirza Salehudin
    ;
    Shafriza Nisha Basah
    ;
    Haniza Yazid
    ;
    Khairul Salleh Basaruddin
    ;
    Muhammad Juhairi Aziz Safar
    ;
    Mohd Hanafi Mat Som
    ;
    Khairul Azami Sidek
    This project explores EasyOCR's performance with Latin characters under image degradation. Variables like character-background intensity difference, Gaussian blur, and relative character size were tested. EasyOCR excels in distinguishing unique lowercase and uppercase characters but tends to favor uppercase for similar shapes like C, S, U, or Z. Results showed that high character-background intensity differences affected OCR output, with confidence scores ranging from 3 % to 80%. Higher differences caused confusion between characters like o and 0, or i and 1. Increased Gaussian blur hindered recognition but improved it for certain letters like v. Image size had a significant impact, with character detection failing as sizes decreased to 40% to 30% of the original. These findings provide insights into EasyOCR's capabilities and limitations with Latin characters under image degradation.
      7  29
  • Publication
    Performance analysis of multi-level thresholding for microaneurysm detection
    ( 2022-09-01)
    Choong K.H.
    ;
    Shafriza Nisha Basah
    ;
    Haniza Yazid
    ;
    Muhammad Juhairi Aziz Safar
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Lim Chee Chin
    Diabetic retinopathy (DR) – one of the diabetes complications – is the leading cause of blindness among the age group of 20–74 years old. Fortunately, 90% of these cases (blindness due to DR) could be prevented by early detection and treatment via manual and regular screening by qualified physicians. The screening of DR is tedious, which can be subjective, time-consuming, and sometimes prone to misclassification. In terms of accuracy and time, many automated screening systems based on image processing have been developed to improve diagnostic performance. However, the accuracy and consistency of the developed systems are largely unaddressed, where a manual screening process is still the most preferred option. The main contribution of this paper is to analyse the accuracy and consistency of microaneurysm (MA) detection via image processing by focusing on Otsu’s multi-thresholding as it has been shown to work very well in many applications. The analysis was based on Monte Carlo statistical analysis using synthetic retinal images of retinal images under variation of all stages of DR, retinal, and image parameters – intensity difference between MAs and blood vessels (BVs), MA size, and measurement noise. Then, the conditions – in terms of obtainable retinal and image parameters – that guarantee accurate and consistent MA detection via image processing were extracted. Finally, the validity of the conditions to guarantee accurate and consistent MA detection was verified using real retinal images. The results showed that MA detection via image processing is guaranteed to be accurate and consistent when the intensity difference between MAs and BVs is at least 50% and the sizes of MAs are from 5 to 20 pixels depending on measurement noise values. These conditions are very important as a guideline of MA detection for DR.
      5  44
  • Publication
    Comparison Between K-Nearest Neighbor (KNN) and Decision Tree (DT) Classifier for Glandular Components
    ( 2022-01-01)
    Hun C.C.
    ;
    Haniza Yazid
    ;
    Muhammad Juhairi Aziz Safar
    ;
    Ab Rahman K.S.
    Prostate cancer is one of the most common cancers in men, and the cases of this disease is increasing. Histopathological examination of prostate cancer is one of the main procedures for prostate cancer detection. The structural changes of the cytoplasm, stroma, lumen and nucleus in the glandular tissue will indicate the presence of cancerous or non-cancerous areas in the histopathology of prostate cancer. Therefore, a framework was developed to automatically segment and classify glandular tissue into cytoplasm, stroma, lumen, and nucleus, which can reduce the complexity of prostate cancer detection. The images underwent image enhancement using histogram equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). Then, in segmentation phase, K-means clustering (KMC) and multi-level thresholding (MT) methods were implemented to segment the enhanced image into cytoplasm and stroma, lumen and nuclei regions. A total of 8 feature vectors are extracted from each segmented image. All these features were introduced into the classification system namely K nearest neighbor (KNN) and decision tree (DT). The overall results showed that the performance of KNN is better than DT with an accuracy of 86.67%, sensitivity and specificity are both 100% (the features of the KMC category). With the features of MT category, KNN achieved 84.44% in term of accuracy, 100% sensitivity and 96.67% specificity. Here, it can also be concluded that the features of the KMC category are more suitable for the classifiers. In addition, leave-one-out cross-validation has been implemented, which can improve the performance of the two classifiers.
      13  30
  • Publication
    Effect of Image Thresholding on the Homogenized Properties of Trabecular Bone Model
    ( 2022-01-01)
    Khairul Salleh Basaruddin
    ;
    Haniza Yazid
    ;
    Muhammad Juhairi Aziz Safar
    ;
    Mohd Hafif Basha Mohamad Jamel Basha
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Muhamad Khairul Ali Hassan
    This paper presents a numerical study to determine the homogenized (apparent) properties of vertebral trabecular bone with different threshold values using homogenization method. Series set of micro-CT images of vertebral trabecular bone was used in the present digital image-based modeling technique to reconstruct the microstructure model. Three image thresholding values were selected based on Otsu’s method. The homogenized properties that include the Young’s moduli, Poisson’s ratio and shear moduli was obtained in this study. The results showed there is significant effect of image threshold on the homogenized properties of vertebral trabecular bone model.
      3  38
  • Publication
    An IoT Agricultural System for Harumanis Farm
    ( 2021-12-01)
    Ismail F.A.
    ;
    Muhamad Khairul Ali Hassan
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Haniza Yazid
    ;
    Muhammad Juhairi Aziz Safar
    ;
    Khairul Salleh Basaruddin
    ;
    Abdul Halim Ismail
    ;
    Mohd Nasir Ayob
    Internet of Things (IoT) is a revolutionary technology that represents the future of communication and computing. The field of IoT implementation is vast and can be applied in every field. This project is about to develop an IoT system for Harumanis Farm as agriculture is becoming an essential growing sector throughout the world due to the increasing population. The major challenge in the Harumanis sector is to improve the productivity and quality of Harumanis without continuous manual monitoring. IoT improves crop management, cost-effectiveness, crop monitoring and also improves the quality and quantity of the crop. This IoT system completes with several sensors to monitor the Harumanis farm, such as temperature and humidity sensor, pH level sensor, soil moisture sensor, also nitrogen, phosphorous, and potassium (NPK) sensor. The system is a simple IoT architecture where sensors collect information and send it over the Wi-Fi network to the mobile applications.
      1  39
  • Publication
    Modelling of Retinal Images for Analysis of Diabetic Retinopathy Severity Levels
    ( 2021-11-25)
    Qaid M.
    ;
    Shafriza Nisha Basah
    ;
    Haniza Yazid
    ;
    Muhammad Juhairi Aziz Safar
    ;
    Mohd Hanafi Mat Som
    ;
    Lim C.C.
    Synthetic data by various algorithms that resemble actual data in terms of statistical features. Computer-aided medical applications have been extensively applied to model specific scenarios, such as medical imaging of retinal images for diabetic retinopathy (DR) detection. The available data and annotated medical data are typically rare and costly due to the difficulties of conducting medical screening and rely on highly trained doctors to review and diagnose. The modelling of retinal images for DR analysis is essential since it will provide a model to guide and test DR detection algorithms. This paper aims to model normal retina and non-proliferative diabetic retinopathy (NPDR) stages (mild, moderate, and severe) data models with the variation of dynamic models. The Digital Retinal Images for Vessel Extraction (DRIVE), The Standard Diabetic Retinopathy Database, Calibration Level 1 (DIARETDB1), and E-OPHTHA datasets are analyzed to obtain the specification of the human retina and DR lesions. In the data modelling phases, the model includes the bright and dark retinal lesions with the variation of dynamic parameters. 4100 synthetic images are used where 200 normal images and 3900 NPDR images to test the performance of DR detection algorithms over the full range of parameters.
      31  4