Lim Chee Chin
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Lim Chee Chin
Official Name
Lim, Chee Chin
Alternative Name
Lim, Chee Chin
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Scopus Author ID
57201525827
Researcher ID
GNO-9181-2022

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  • Publication
    A visual tracking range of motion assessment system for lower limb joint
    ( 2016)
    Lim Chee Chin
    Accurate range of motion (ROM) measurement of lower limb joint motion is important for assessing the severity of human lower limb injuries. It is essential for assisting the medical doctor and physiotherapist to determine the suitable treatment and rehabilitation exercises that are required for lower limb injury patient specifically. Current medical measurement systems such as Universal Goniometer (UGM) has a large resolution of 1° which may cause to have observation error while Electrogoniometer (EGM) is affected by the inaccurate sensor’s position and detachment when moving due to its mechanical properties limitation. Thus, a visual tracking ROM assessment system (VTS) for lower limb joint measurement is proposed. The purpose of this investigation was to develop a method to quantify a ROM of the lower limb joint and examine the ROM obtained between the VTS with EGM and UGM, for the measurement of lower limb joint angles. There were three major experiments conducted i.e., Validation Experiment, Clinical Test and Clinical Case Study. Validation experiment was done on the developed visual tracking system before being applied on the real human subject to ensure the system performance and safety to be acceptable. The system had been tested under the several of light intensity level, camera distance, camera elevation angle and markers location to determine the optimum operating condition. In clinical test, there were two tests carried out; they were Healthy Control Test and Injured Subject Test. A total of 20 healthy control subjects’ findings proved that the left and right lower limbs of human were similar (99.80% ~ 97.64% of similarity) for the normal healthy subjects. Comparison between VTS, EGM and UGM found that the accuracy for each two systems compared to each other was significantly different for the VTS vs. EGM and the EGM vs. UGM. The VTS vs. UGM produced the highest accuracy for all the joint motions compared to VTS vs. EGM and the EGM vs. UGM; it was 99.46% for left knee flexion. In addition, total of 70 injured subjects (included ankle joint, knee joint, and hip joint) had undergone injured subject test to compare its severity level between illness and three measurement systems. In the injured subject test, VTS yielded the smallest coefficient of variation (CV) compared to the EGM and UGM for Knee flexion for moderate injuries which was 2.45%.
  • Publication
    Analysis of the performance of SLIC super-pixel toward pre-segmentation of soil-transmitted helminth
    (AIP Publishing, 2023)
    Loke Siew Wen
    ;
    Lim Chee Chin
    ;
    Aimi Salihah Abdul Nasir
    ;
    Norhanis Ayunie Ahmad Khairudin
    ;
    Chong Yen Fook
    ;
    Mohd Yusoff Mashor
    ;
    Zeehaida Mohamed
    Soil-Transmitted Helminth (STH) infections are one of the most severe health issues in the world including Malaysia and frequently happened in an unsanitary environment within the children group. The helminth infections are diagnosed by inspecting the faeces samples manually through light microscope. However, the manual inspection method to diagnose the helminth egg is a time-consuming and challenging process especially when are huge number of samples. To increase the efficiency and accuracy of the diagnosis, an analysis of super-pixel segmentation with different parameter adjustments on four different species was carried out. This work described a Simple Linear Iterative Clustering (SLIC) super-pixel algorithm that uses different parameter settings to explore more parasites image features for a better segmentation process in the future and to analyse the effect of different SLIC parameter settings towards the pre-segmentation process. There is total 80 images collected from the four helminth egg species which are Ascaris Lumbricoides Ova (ALO), Enterobius Vermicularis Ova (EVO), Hookworm Ova (HWO) and Trichuris Trichiura Ova (TTO). The proposed approach is divided into three steps. First, the images with various lighting conditions are enhanced by the partial contrast stretching (PCS) technique. The simple linear iterative clustering (SLIC) super-pixel algorithm was implemented to the enhanced images as a pre-segmentation algorithm to form super-pixel images. Lastly, image quality assessment will be performed on the SLIC images. The SLIC parameter compactness of super-pixel, m of 5 and number of super-pixels, k of 1000 was selected because they generate the greatest PSNR value, indicating that this combination of parameters could produce high-quality images. In future, a more in-depth analysis of the parameter k and m, which impacts the form of each super-pixel and the pre-segmentation process, might improve the recommended approach.
  • Publication
    A fast and efficient segmentation of soil-transmitted helminths through various color models and k-means clustering
    ( 2021-01-01)
    Norhanis Ayunie Ahmad Khairudin
    ;
    Aimi Salihah Abdul Nasir
    ;
    Lim Chee Chin
    ;
    Haryati Jaafar
    ;
    Mohamed Z.
    Soil-transmitted helminths (STH) are one of the causes of health problems in children and adults. Based on a large number of helminthiases cases that have been diagnosed, a productive system is required for the identification and classification of STH in ensuring the health of the people is guaranteed. This paper presents a fast and efficient method to segment two types of STH; Ascaris Lumbricoides Ova (ALO) and Trichuris Trichiura Ova (TTO) based on the analysis of various color models. Firstly, the ALO and TTO images are enhanced using modified global contrast stretching (MGCS) technique, followed by the extraction of color components from various color models. In this study, segmentation based on various color models such as RGB, HSV, L*a*b and NSTC have been used to identify, simplify and extract the particular color needed. Then, k-means clustering is used to segment the color component images into three clusters region which are target (helminth eggs), unwanted and background regions. Then, additional processing steps are applied on the segmented images to remove the unwanted region from the images and to restore the information of the images. The proposed techniques have been evaluated on 100 images of ALO and TTO. Results obtained show saturation component of HSV color model is the most suitable color component to be used with the k-means clustering technique on ALO and TTO images which achieve segmentation performance of 99.06% for accuracy, 99.31% for specificity and 95.06% for sensitivity.
      2  33
  • Publication
    Effect of Mindfulness Meditation toward Improvement of Concentration based on Heart Rate Variability
    ( 2020-12-20)
    Lim Chee Chin
    ;
    Rosli F.F.B.
    ;
    Fook C.Y.
    ;
    Vikneswaran Vijean
    ;
    Saidatul Ardeenawatie Awang
    ;
    Palaniappan R.
    Mindfulness meditation is a type of therapy for a psychological cure like depression and anxiety that can significantly increase peoples' ability to concentrate and focus. Thus, this paper describes the analysis of mindfulness meditation effect toward concentration study in term of heart rate variability (HRV) signal. A memory test is used as a medium to test the concentration level of 20 participants, and their performance of the electrocardiogram signal was recorded. Peaks detection method and Pan-Tompkin method are used to extract the features like PQRST peaks and R-R interval from the ECG signal. Then, the extracted ECG signal features are classified using KNN method for before and after meditation during the memory test. The result shows that the effect of mindfulness meditation can improve the performance of participants' concentration level. The highest accuracy, sensitivity and specificity performance is obtained from the combination of all six features (P, Q, R, S, T peaks, and R-R interval value), which is 84.58 %, 88.77% and 80.39%. The analysis of memory test produces higher memory test score (69.2%), lesser miss selection (60.8%) and shorter taken time to complete the memory test (2.268 minutes) after mindfulness meditation compared to before mindfulness meditation. The R-R interval value represents heart rate variability (HRV) is important to prove that most of the participants are more relax and can handle their stress better after doing mindfulness meditation.
      5  19
  • Publication
    A review on contact lens inspection
    ( 2023-08-01)
    Mana N.A.M.A.
    ;
    Lim Chee Chin
    ;
    Fook C.Y.
    ;
    Haniza Yazid
    ;
    Ali Y.M.
    Over the year, contact lens detection has attracted attention and interest from many researchers to study further in this field of inspection. This paper provides a comprehensive review of the existing literature surrounding contact lens inspection methods. In this paper, contact lens-related, defects-related, and inspection methods related are described in detail. To detect contact lenses in a single image and also multi-image, numerous techniques have been developed and this paper is aimed at classifying and evaluating these algorithms. Also, contact lens inspection based on conventional and artificial intelligence methods will be discussed in detail. The industrial production process of contact lenses probably needs to be constructed with advanced tools based on recent technologies so that they can help in the inspection system to achieve accurate results of the inspection and reduce processing time.
      26  4
  • Publication
    Investigation on Medicated Drugs in ECG of Healthy Subjects
    ( 2022-01-01)
    Vikneswaran Vijean
    ;
    Abdul Ghapar Ahmad
    ;
    Syakirah Afiza Mohammed
    ;
    Razi Ahmad
    ;
    Wan Amiza Amneera Wan Ahmad
    ;
    Ragunathan Al Santiagoo
    ;
    Lim Chee Chin
    Heart diseases are now the leading cause of death worldwide, it is estimated that around 7 million patients who are living in developed countries, lost their lives due to diseases related to their cardiovascular system. In Malaysia, cardiovascular diseases represents one fifth of total deaths in the country in the past three decades. Currently patients need some sort of drugs that help them to stabilize and restore the regular patterns of their heart beat because if the patients cannot manage to restore the normal heart beat pattern, the undesired heart condition could lead life threatening situations. Advancement of biotechnology has enabled the creation of new medicated drugs to provide better treatment options. However, when this treatment option fails and there is a need to provide emergency intervention to the patients in hospitals, the medical experts often need to know about the patients' intake of any medications prior to hospital admittance for providing suitable treatments. Sometimes, this would be a difficult task as the patient might be admitted in semi-conscious or unconscious state. Therefore, this study focusses on identification of different medicated drugs usage through analysis of ECG data of the users. The data for the experiment was obtained from physionet library, which provides ECG data of subjects administered with a combination of Dofetilide, Mexiletine, lidocaine, Moxifloxacin and Diltiazem medicated drugs. The use of morphological and non-linear features derived from the ECG signals were able to provide prediction accuracy of 77.26% using SVM classifier.
      38  2
  • Publication
    Comparative analysis of image processing techniques for enhanced MRI image quality: 3D reconstruction and segmentation using 3D U-Net architecture
    (MDPI, 2023)
    Lim Chee Chin
    ;
    Apple Ho Wei Ling
    ;
    Yen Fook Chong
    ;
    Mohd Yusoff Mashor
    ;
    Khalilalrahman Alshantti
    ;
    Mohd Ezane Aziz
    Osteosarcoma is a common type of bone tumor, particularly prevalent in children and adolescents between the ages of 5 and 25 who are experiencing growth spurts during puberty. Manual delineation of tumor regions in MRI images can be laborious and time-consuming, and results may be subjective and difficult to replicate. Therefore, a convolutional neural network (CNN) was developed to automatically segment osteosarcoma cancerous cells in three types of MRI images. The study consisted of five main stages. First, 3692 DICOM format MRI images were acquired from 46 patients, including T1-weighted, T2-weighted, and T1-weighted with injection of Gadolinium (T1W + Gd) images. Contrast stretching and median filter were applied to enhance image intensity and remove noise, and the pre-processed images were reconstructed into NIfTI format files for deep learning. The MRI images were then transformed to fit the CNN’s requirements. A 3D U-Net architecture was proposed with optimized parameters to build an automatic segmentation model capable of segmenting osteosarcoma from the MRI images. The 3D U-Net segmentation model achieved excellent results, with mean dice similarity coefficients (DSC) of 83.75%, 85.45%, and 87.62% for T1W, T2W, and T1W + Gd images, respectively. However, the study found that the proposed method had some limitations, including poorly defined borders, missing lesion portions, and other confounding factors. In summary, an automatic segmentation method based on a CNN has been developed to address the challenge of manually segmenting osteosarcoma cancerous cells in MRI images. While the proposed method showed promise, the study revealed limitations that need to be addressed to improve its efficacy.
      16  1
  • Publication
    Investigation on Body Mass Index Prediction from Face Images
    ( 2021-03-01)
    Chong Yen Fook
    ;
    Lim Chee Chin
    ;
    Vikneswaran Vijean
    ;
    Lim Whey Teen
    ;
    Hasimah Ali
    ;
    Aimi Salihah Abdul Nasir
    Body mass index is a measurement of obesity based on measured height and weight of a person and classified as underweight, normal, overweight and obese. This paper reviews the investigation and evaluation of the body mass index prediction from face images. Human faces contain a number of cues that are able to be a subject of a study. Hence, face image is used to predict BMI especially for rural folks, patients that are paralyzed or severely ill patient who unable to undergoes basic BMI measurement and for emergency medical service. In this framework, 3 stages will be implemented including image pre-processing such as face detection that uses the technique of Viola-Jones, iris detection, image enhancement and image resizing, face feature extraction that use facial metric and classification that consists of 3 types of machine learning approaches which are artificial neural network, Support Vector Machine and k-nearest neighbor to analyze the performance of the classification. From the results obtained, artificial neural network is the best classifier for BMI prediction system with the highest recognition rate of 95.50% by using the data separation of 10% of testing data and 90% of training data. In a conclusion, this system will help to advance the study of social aspect based on the body weight.
      1
  • 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.
      5  37
  • 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.
      5  24