Shafriza Nisha Basah
Thumbnail Image
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

Filters

19
Reset filters

Settings
Now showing 1 - 10 of 19
Thumbnail Image
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.

View
Thumbnail Image
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.

View
Thumbnail Image
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.

View
Thumbnail Image
Publication

Smart fall detection monitoring system using wearable sensor and Raspberry Pi

2024-02-08 , Lim Chee Chin , Mahmud N.F.A. , Vikneswaran Vijean , Ali Yeon Md Shakaff , Tan X.J. , Ahmad Faizal Salleh , 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.

View
No Thumbnail Available
Publication

Performance analysis of diabetic retinopathy detection using fuzzy entropy multi-level thresholding

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.

View
Thumbnail Image
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.

View
Thumbnail Image
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.

View
Thumbnail Image
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.

View
Thumbnail Image
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.

View
Thumbnail Image
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.

View