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
    Kinematics Mathematical Modelling of Lower Limb Exoskeleton for Paralyzed Stroke Patients
    ( 2024-01-01)
    Rahman M.A.A.
    ;
    Chettiar V.C.K.
    ;
    Aman M.N.S.S.
    ;
    Chin L.C.
    ;
    Shafriza Nisha Basah
    ;
    Takemura H.
    ;
    Yeap E.J.
    This paper presents the development of a lower limb rehabilitation robot to be used with bedridden patients. Strokes are one of the significant causes of death in 17% of the 109,155 medically certified death in 2020 in Malaysia. In most cases, stroke paralysis affects the opposite side of the damaged brain, and any part of the body can be affected. 90% of stroke patients get paralysis to some degree. Patients can recover from the disease and restore body motions by undergoing paralysis stroke physiotherapy, which involves numerous sessions with patients. There were several successful robotic rehabilitations in recent years; however, their design is inflexible and large, requiring the patient to sit or stand in a static position. This project will be built on a motor-driven parallel architecture that will offer motion assistance throughout the human’s wide range of motion (ROM). This project development is divided into two parts: structure design and simulation. The design process for the lower limb devices used syncretization and mathematical analysis. The structure design is from the kinematic analysis. The mathematical models are then used to design in MATLAB simulation which is trajectory simulation. The outcome shows that the simulations that have been developed is compatible with the motion of human lower limb. This robot develops for bedridden use of lower limb rehabilitation exercises.
  • Publication
    Sensitivity Analysis of Tracking Point for A Visual Tracking System on Lower Limb Joint Assessment
    ( 2021-09-13)
    Chin L.C.
    ;
    Affandi M.
    ;
    Shah M.N.
    ;
    Shafriza Nisha Basah
    ;
    Jian T.X.
    ;
    Din M.Y.
    No matter accurate that putting a sensor in its place there is always a possibility that the position of the sensor is not correct. An inaccurate position may produce an error, which eventually affects the result of the measurement. Sensitivity analysis is intended to determine the amount of error that may occur in measurement by varying important parameters slightly in that measurement and calculating the change of the result. In this paper, sensitivity analysis was simulated in the visual tracking system for lower limb joint measurements. In doing the measurements, markers were put on the limbs of the patients at determined positions. Sensitivity analysis was then simulated by moving the points slightly. There was a total of 729 possible positions coming from three marker positions. The effects of the changes for the distances to be measured were analyzed. It is found that the errors depend on the size of the marker; for a 10-mm marker, the maximum error is only 7.85%, which is relatively small for practical application. When the marker diameter is 13 mm, the maximum error is slightly over 10%, which is still acceptable for practical purposes. There are exactly 27 positions that do not produce errors. Knowing these positions will help the user to reduce the error that may occur during the measurement.
  • 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.
  • 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.
  • 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%.
  • 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
    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
    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.
      2
  • 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.
      20  3
  • 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.
      2  18