Fathinul Syahir Ahmad Sa'ad
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Preferred name
Fathinul Syahir Ahmad Sa'ad
Official Name
Fathinul Syahir, Ahmad Sa'ad
Alternative Name
Ahmad Saad, Fathinul Syahir
Sa’ad, Fathinul Syahir Ahmad
Main Affiliation
Scopus Author ID
57205307496
Researcher ID
R-5360-2019

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  • 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  37
  • 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  34
  • Publication
    Development of Ripeness Indicator for Quality Assessment of Harumanis Mango by using Image Processing Technique
    ( 2020-12-18)
    Mohd Nazri Abu Bakar
    ;
    Abu Hassan Abdullah
    ;
    Haniza Yazid
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Ahmad K.
    ;
    Norasmadi Abdul Rahim
    Visual appearance is the main source of information that can be used for quality assessment of mango. In this study, a non-destructive ripeness level estimation for mango of the cultivar Harumanis based on digital image analysis was employed. The changing peel and flesh colour of mango is strongly correlated to ripeness that can be measured as a sensual quality parameter. This measurement of ripeness level has been determined by image analysis technique which needs to attribute external and internal colour feature from image segmentation Multilevel thresholding technique is proposed for colour image segmentation to extract the mango region from the background which every channel of five colour spaces have been applied. Colour analysis technique and Total soluble solids (TSS) is used to compare and evaluate for the prediction. The optimal results were obtained that a∗ channel from L∗a∗b colour space has given more logical and better performance of prediction which is more than 92% accuracy.
      4  29
  • Publication
    Segmentation of Diabetic Retinopathy Using Entropy-Based Thresholding - A Review
    ( 2022-01-01)
    Qaid M.S.A.
    ;
    Shafriza Nisha Basah
    ;
    Haniza Yazid
    ;
    Fathinul Syahir Ahmad Sa'ad
    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.
      27  2
  • Publication
    Analysis on Clustering Based Method for Diabetic Retinopathy Using Color Information
    ( 2022-01-01)
    Selvam S.A
    ;
    Haniza Yazid
    ;
    Shafriza Nisha Basah
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Muhamad Khairul Ali Hassan
    Diabetic Retinopathy (DR) is an important global health concern and it can causes blindness. Early detection and treatment can prevent the patients from loss their vision. This study presents an approach of color image segmentation for automatic exudate detection. The color retinal images are converted into four different color spaces and preprocessed by applying Contrast Limited Adaptive Histogram Equalization (CLAHE). Fuzzy C-Means (FCM) and K-means clustering (KMC) algorithms are applied on the preprocessed image for the segmentation purpose. Then, optic disc is detected and eliminated by using Circular Hough Transform (CHT). Performance evaluation of developed algorithm is done using Structured Analysis of the Retina (STARE) dataset. The proposed algorithm achieved sensitivity of 93.4% for STARE datasets for LUV color space with KMC.
      30  1
  • 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  35