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

Research Output

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  • Publication
    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.
  • 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.
  • Publication
    Ankle Injury Rehabilitation Robot (AIRR): Review of Strengths and Opportunities Based on a SWOT (Strengths, Weaknesses, Opportunities, Threats) Analysis
    ( 2022-11-01)
    Shah M.N.
    ;
    Shafriza Nisha Basah
    ;
    Khairul Salleh Basaruddin
    ;
    Takemura H.
    ;
    Yeap E.J.
    ;
    Lim C.C.
    Generally, severity, any additional damage to the joint surface, and the optimal rehabilitation influence the recovery of an ankle injury. Optimal rehabilitation is the only approach for a human to heal as soon as possible. Ankle injury rehabilitation robots (AIRRs) are designed to fulfil the ideal rehabilitation by providing the required accuracy, consistency, and repeatability, compared to conventional rehabilitation methods. This review is to explore the performance of the existing AIRR using a SWOT analysis with a focus on the strengths and opportunities of an AIRR. Sources from journals and conference papers are selected for review after several screenings, according to the search conditions set by the authors. The results have shown a large group of AIRRs could accomplish all basic ankle motions and select parallel mechanisms to drive the foot platform. Most AIRRs provides crucial feedback sensors, such as position, torque, and angle. These factors determine the accuracy of the foot platform. Both the electrical/pneumatic actuation and wearable/platform-based AIRRs have their purpose for rehabilitation and must be considered as equal contributions to ankle injury rehabilitation research using robots. Opportunities to provide innovation to the already established AIRR research still exist in the ability to accommodate complex motion ankle rehabilitation exercises and to establish teaching and playback into the rehabilitation procedures for AIRRs. In general, the existing strengths of AIRRs provide advantages to patients where they can enhance the rehabilitation procedures while opportunities and knowledge gaps for AIRR research are still open to improvement.
  • 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.
  • Publication
    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.
  • Publication
    Spine Deformity Assessment for Scoliosis Diagnostics Utilizing Image Processing Techniques: A Systematic Review
    ( 2023-10-01)
    Amran N.N.
    ;
    Khairul Salleh Basaruddin
    ;
    Ijaz M.F.
    ;
    Haniza Yazid
    ;
    Shafriza Nisha Basah
    ;
    Muhayudin N.A.
    ;
    Sulaiman A.R.
    Spinal deformity refers to a range of disorders that are defined by anomalous curvature of the spine and may be classified as scoliosis, hypo/hyperlordosis, or hypo/hyperkyphosis. Among these, scoliosis stands out as the most common type of spinal deformity in human beings, and it can be distinguished by abnormal lateral spine curvature accompanied by axial rotation. Accurate identification of spinal deformity is crucial for a person’s diagnosis, and numerous assessment methods have been developed by researchers. Therefore, the present study aims to systematically review the recent works on spinal deformity assessment for scoliosis diagnosis utilizing image processing techniques. To gather relevant studies, a search strategy was conducted on three electronic databases (Scopus, ScienceDirect, and PubMed) between 2012 and 2022 using specific keywords and focusing on scoliosis cases. A total of 17 papers fully satisfied the established criteria and were extensively evaluated. Despite variations in methodological designs across the studies, all reviewed articles obtained quality ratings higher than satisfactory. Various diagnostic approaches have been employed, including artificial intelligence mechanisms, image processing, and scoliosis diagnosis systems. These approaches have the potential to save time and, more significantly, can reduce the incidence of human error. While all assessment methods have potential in scoliosis diagnosis, they possess several limitations that can be ameliorated in forthcoming studies. Therefore, the findings of this study may serve as guidelines for the development of a more accurate spinal deformity assessment method that can aid medical personnel in the real diagnosis of scoliosis.
  • Publication
    Parametric design optimization of an ankle rehabilitation robot using SolidWorks
    ( 2020-12-18)
    Muhammad Nazrin Shah Shahrol Aman
    ;
    Shafriza Nisha Basah
    ;
    Khairul Salleh Basaruddin
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Bin Ahmad S.A.
    ;
    Wan Azani Wan Mustafa
    This paper presents the approach to determine most suitable dimensions and volume of the proposed ankle rehabilitation robot design. This design aim is the robot needs to be portable without compromising the workspace of the proposed robot and it must fulfill all required basic ankle motions. To do this, optimisation was used to generate possible initial dimensions in order to achieve suitable length for the outer frame through minimization of the dimensions. Based on the selected variables and constraints, the result of the optimization shows minimization of the proposed design has been achieved through reduction of the dimension of the outer frame of the robot in which translate the reduction of the weight of the robot.
  • Publication
    Clinical validation of 3D mesh reconstruction system for spine curvature angle measurement
    ( 2023-02-21)
    Shanyu C.
    ;
    Lim Chee Chin
    ;
    Fook C.Y.
    ;
    Azizan A.F.
    ;
    Vikneswaran Vijean
    ;
    Ahmad Faizal Salleh
    ;
    Shafriza Nisha Basah
    Spine curvature disorders are scoliosis, lordosis, and kyphosis. These disorders are mainly caused by the bad habits of the person during sitting, standing, and lying. There are about 3 to 5 out of 1,000 people who are affected by spine curvature disorder. The current conventional method used for diagnose this disorder, such as radiography, goniometry and palpation. However, these conventional methods require human skills and can be time-consuming, resulting to exhaustion of logistic. Therefore, there is a need to solve this problem by creating a Graphical User Interface (GUI) to analyse the human body posture through the 3D reconstructed model of the person. Hence, 3D map meshing reconstruction of the human body method is proposed. This project divided into three parts, which are the development of the GUI for human posture analysis, clinical validation and posture analysis of the 3D model. The 3D model reconstructed from 3D mapping parameters shows 100% accuracy of the assessed point. The lowest difference of angle for the comparison between clinical method (goniometer) and the GUI for male is (A.Pe) 0.930±0.870 and 1.240±0.860 for female (P.Pe). This finding of 3D model assessment system can be helpful for medical doctor to diagnose patient who have spine problem.
  • Publication
    Smart fall detection monitoring system using wearable sensor and Raspberry Pi
    ( 2024-02-08)
    Lim C.C.
    ;
    Mahmud N.F.A.
    ;
    Vikneswaran Vijean
    ;
    Ali Yeon Md Shakaff
    ;
    Ahmad Faizal Salleh
    ;
    Tan X.J.
    ;
    Shafriza Nisha Basah
    The Smart Fall Detection Monitoring System is the name of the programme that monitors everyday activities and falls. It has an accelerometer sensor (ADXL345) and Raspberry Pi 3 microcontroller board to recognise and classify the patient's fall. Python programming was done on the Raspberry Pi terminal to enable communication between the accelerometer sensor and the computer. There were 10 subjects (5 males and 5 females) collected. While daily living activities include standing, squatting, walking, sitting, and lying, the data on falling includes forward falls and falls from medical beds. The K-nearest Neighbour (kNN) classifier can categorise the data of falling and non-falling (everyday living activity). The accuracy of the kNN classifier was 100% for the combined feature and (>87%) for each feature during the categorization of the falling and non-falling classes. In the meantime, multiclass classification performance for combining features and for each feature separately was >85%. kNN classifier was used to assess the feature. The feature was chosen based on the k-NN classifier's accuracy score as a percentage. For feature selection for falling and non-falling, feature (AcclX, AcclY, AngX, AngY and AngZ) in City-block distance was selected as they performed high accuracy which was 100%. The performance of the AngZ (77%) was good during the sub-classification of the sub-class dataset. As a result, all feature characteristics were chosen to be incorporated in the IoT fall detection device. The system is real-time communication for classifying fall and non-fall conditions with 100% accuracy using kNN classifier with cityblock distance.