Shafriza Nisha Basah
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Preferred name
Shafriza Nisha Basah
Official Name
Shafriza Nisha, Basah
Alternative Name
Basah, Shafriza N.
Basah, Shafiza Nisha
Basah, S. • Nisha
Basah, Shafriza Nisha Bin
Basah, S. N.
Nisha Basah, Shafriza
Shafriza Nisha, B.
Basah, Shafriza Nisha B.
Bin Basah, Shafriza Nisha
Basha, Shafriza Nisha
Main Affiliation
Scopus Author ID
26653958200
Researcher ID
AAN-8887-2020

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Analysis of Optical Character Recognition using EasyOCR under Image Degradation

2023-01-01 , Salehudin M.A.M. , Shafriza Nisha Basah , Yazid H. , Khairul Salleh Basaruddin , Safar M.J.A. , Mohd Hanafi Mat Som , Sidek K.A.

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.

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Performance analysis of entropy thresholding for successful image segmentation

2022-02-01 , Yazid H. , Shafriza Nisha Basah , Rahim S.A. , Muhammad Juhairi Aziz Safar , Khairul Salleh Basaruddin

Image segmentation refers to a procedure of segmenting the foreground (object of interest) from the background. One of the well-known methods is thresholding based segmentation that segments an image according to a threshold value. Most of the proposed methods either proposing a new algorithm or improvising the algorithm to segment the foreground. However, there is no analysis is carried out to determine the successfulness of the methods under different conditions. This main contribution of this paper is to analyse the entropy thresholding namely the method proposed by Kapur and Li for various parameters which include noise measurement, size of the object, and the difference in intensity between the background and object. In this paper, a few conditions were proposed to ensure successful image segmentation. Based on the experimental result, intensity difference needs to be around 35% and the object size is about 73% for all noise levels for Kapur. For Li entropy, the intensity difference needs to be at a minimum of 44% and 80% for object size. It is demonstrated that the proposed conditions accurately foresee the result of image thresholding based on Kapur and Li entropy.

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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.

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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.

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