Mohd Hanafi Mat Som
Thumbnail Image
Preferred name
Mohd Hanafi Mat Som
Official Name
Mohd Hanafi , Mat Som
Alternative Name
Mat Som, M. H
Mat Som, Mohd Hanafi
Som, M. H.M.
Som, M. H.Mat
Som, Mohd Hanafi Mat
Main Affiliation
Scopus Author ID
57215715847
Researcher ID
R-4326-2019

Research Output

Filters

6 1
1
2
6
Reset filters

Settings
Now showing 1 - 6 of 6
  • Publication
    Performance analysis of diabetic retinopathy detection using fuzzy entropy multi-level thresholding
    (Elsevier Ltd, 2023)
    Mohammed Saleh Ahmed Qaid
    ;
    Shafriza Nisha Basah
    ;
    Khairul Salleh Basaruddin
    ;
    Haniza Yazid
    ;
    Muhamad Khairul Ali Hassan
    ;
    Mohd Hanafi Mat Som
    Diabetic Retinopathy (DR) is one of the major causes of blindness. Many DR detection systems were developed to segment and determine the type and number of lesions that appeared on retinal images and used to classify DR and its severity level. Even though several researchers have already proposed many automated diagnosis systems with different image segmentation algorithms, their accuracy and reliability are generally unexplored. The accuracy of an automated diagnosis system usually depends on the segmentation techniques. The accuracy of this system is heavily dependent upon the retinal and image parameters, which have intensity level difference between background (BG)-blood vessels (BV), BV-bright lesions, BV-dark lesions, and noise levels. In this work, the automated diagnosis system accuracy has been analysed to successfully detect DR and its severity levels. The focus is on fundus image modalities segmentation based on fuzzy entropy multi-level thresholding. The analysis aimed to develop conditions to guarantee accurate DR detection and its severity level. Firstly, a retinal image model was developed that represents the retina under the variation of all retinal and image parameters. Overall, 45,000 images were developed using the retinal model. Secondly, feasibility and consistency analysis were performed based on a specific design Monte Carlo statistical method to quantify the successful detection of DR and its severity levels. The conditions to guarantee accurate DR detections are: BG to BV > 30% and BV to the dark lesions (MAs) >15% for mild DR, BG to BV > 40% and BV to the dark lesions (MAs and HEM) > 20% for moderate DR, and BG to BV > 30% and BV to the dark lesions (MAs and HEM) > 15%, and BV to the bright lesions (EX) > 55% for severe DR. Finally, the validity of these conditions was verified by comparing their accuracy against real retinal images from publicly available datasets. The verification results demonstrated that the condition for the analysis could be used to predict the success of DR detection.
  • Publication
    Performance analysis of diabetic retinopathy detection using fuzzy entropy multi-level thresholding
    ( 2023-07-01)
    Qaid M.S.A.
    ;
    Shafriza Nisha Basah
    ;
    Yazid H.
    ;
    Mohd Hanafi Mat Som
    ;
    Khairul Salleh Basaruddin
    ;
    Ali Hassan M.K.
    Diabetic Retinopathy (DR) is one of the major causes of blindness. Many DR detection systems were developed to segment and determine the type and number of lesions that appeared on retinal images and used to classify DR and its severity level. Even though several researchers have already proposed many automated diagnosis systems with different image segmentation algorithms, their accuracy and reliability are generally unexplored. The accuracy of an automated diagnosis system usually depends on the segmentation techniques. The accuracy of this system is heavily dependent upon the retinal and image parameters, which have intensity level difference between background (BG)-blood vessels (BV), BV-bright lesions, BV-dark lesions, and noise levels. In this work, the automated diagnosis system accuracy has been analysed to successfully detect DR and its severity levels. The focus is on fundus image modalities segmentation based on fuzzy entropy multi-level thresholding. The analysis aimed to develop conditions to guarantee accurate DR detection and its severity level. Firstly, a retinal image model was developed that represents the retina under the variation of all retinal and image parameters. Overall, 45,000 images were developed using the retinal model. Secondly, feasibility and consistency analysis were performed based on a specific design Monte Carlo statistical method to quantify the successful detection of DR and its severity levels. The conditions to guarantee accurate DR detections are: BG to BV > 30% and BV to the dark lesions (MAs) >15% for mild DR, BG to BV > 40% and BV to the dark lesions (MAs and HEM) > 20% for moderate DR, and BG to BV > 30% and BV to the dark lesions (MAs and HEM) > 15%, and BV to the bright lesions (EX) > 55% for severe DR. Finally, the validity of these conditions was verified by comparing their accuracy against real retinal images from publicly available datasets. The verification results demonstrated that the condition for the analysis could be used to predict the success of DR detection.
      2  25
  • 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
    A Review on Edge Detection on Osteogenesis Imperfecta (OI) Image using Fuzzy Logic
    ( 2021-11-25)
    Zaki M.Z.A.A.
    ;
    Mohd Hanafi Mat Som
    ;
    Yazid H.
    ;
    Khairul Salleh Basaruddin
    ;
    Shafriza Nisha Basah
    ;
    Ali M.S.A.M.
    Osteogenesis Imperfecta (OI) is a bone disorder that causes bone to be brittle and easy to fracture. The patient suffered from this disease will have poor quality of life. Simulation on the bone fracture risk would help medical doctors to make decision in their diagnosis. Detection of edges from the OI images is very important as it helps radiologist to segmentize cortical and cancellous bone to make a good 3D bone model for analysis. The purpose of this paper is to review the fundamentals of fuzzy logic in edge detection of OI bone as it is yet to be implemented. Several fuzzy logic concepts are reviewed by previous studies which include fuzziness, membership functions and fuzzy sets regarding digital images. The OI images were produced by modalities such as Magnetic Resonance Imaging (MRI), Ultrasound, or Computed Tomography (CT). In summary, researchers from the reviewed papers concluded that fuzzy logic can be implemented to detect edges in noisy clinical images.
      3  32
  • 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
  • Publication
    Performance Analysis on the Effect of Noise in Inverse Surface Adaptive Thresholding (ISAT)
    ( 2021-11-25)
    Haniza Yazid
    ;
    Mohd Hanafi Mat Som
    ;
    Shafriza Nisha Basah
    ;
    Rahim S.A.
    ;
    Mahmud M.F.
    ;
    Arof H.
    Thresholding is one of the powerful methods in segmentation phase. Numerous methods were proposed to segment the foreground from the background but there is limited number of studies that analyse the effect of noise since the present of noise will affect the performance of the thresholding method. In this paper, the main idea is to analyse the effect of noise in Inverse Surface Adaptive Thresholding (ISAT) method. ISAT method is known as an excellent method to segment the image with the present of noise. The result of this analysis can be a guideline to researcher when implementing ISAT method especially in medical image diagnosis. Initially, several images with different noise variations were prepared and underwent ISAT method. In ISAT method, several image processing methods were incorporated namely edge detection, Otsu thresholding and inverse surface construction. The resulting images were evaluated using Misclassification Error (ME) to evaluate the performance of the segmentation result. Based on the obtained results, ISAT performance is consistent although the noise percentage increases from 5% to 25%.
      1  29