Aimi Salihah Abdul Nasir
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Aimi Salihah Abdul Nasir
Official Name
Aimi Salihah, Abdul Nasir
Alternative Name
Abdul Nasir, Aimi Salihah
Abdul-Nasir, Aimi Salihah
Nasir, Aimi Salihah Abdul
Main Affiliation
Scopus Author ID
57219027157
Researcher ID
AAG-1747-2021

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  • 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
    Superpixels-based automatic density peaks and fuzzy clustering approach in COVID-19 lung segmentation
    (IEEE, 2023-12)
    Ooi Wei Herng
    ;
    Aimi Salihah Abdul Nasir
    ;
    Fatin Nabilah Shaari
    ;
    Abdul Syafiq Abdull Sukor
    Clustering algorithms that rely on minimizing an objective function suffer from the drawback of requiring manual setting of the number of clusters. This limitation becomes particularly evident when applied to image segmentation, where the large number of pixels can lead to memory overflow issues. To overcome this challenge, a reference of Automatic Fuzzy Clustering Framework (AFCF) for image segmentation method has been used as the comparison to the Density Peaks Clustering (DPC) algorithm. AFCF used superpixel algorithm to reduce the spatial information of data during computation, DPC algorithm to generate decision graph, and prior entropy-based fuzzy clustering (PEFC) algorithm to achieve fully automatic segmentation method in determining the number of cluster and the clustering result. In this study, 50 open-source healthy, COVID-19 and pneumonia infected radiographs dataset are acquired from the Kaggle and Github. The radiographs dataset that segmented by DPC is down sampling to 100*100 pixels due to overloading computation. At the end of the image segmentation, a segmentation performance evaluation is conducted based on sensitivity, specificity, accuracy, precision, recall, F-score and time consumed. The result shows that AFCF algorithm has the better overall performance with higher accuracy of 92.48% and F-score 0.9455. Meanwhile, the most highlighted evaluation index is drop to the time consume comparison, AFCF has around 2.7 times faster processing speed compare to DPC algorithm.
  • Publication
    Comparability of edge detection techniques for automatic vehicle license plate detection and recognition
    ( 2021-01-01)
    Fatin Norazima Mohamad Ariff
    ;
    Aimi Salihah Abdul Nasir
    ;
    Haryati Jaafar
    ;
    Zulkifli A.N.
    License plate recognition system is one of the famous topics in image processing to identify the vehicle registration number. This system has been given a lot of beneficial toward transportation system, especially for security system. However, to get the perfect segmentation on alphabet shape for recognition purpose is quite challenging due to the non-uniform condition of image acquisition. Hence this paper proposes a methodology for segmentation of license plate number by using edge-based segmentation. In this study, image segmentation based on edge detection has been chosen due to the sharpness and detail in detecting the shape of an object. Since there are various types of edge detection techniques have been proposed by the previous researchers, several edge detection techniques from the most commonly used techniques have been chosen to be compared and analyze the results of various edge detection for license plate recognition. In this paper, several types of edge detection techniques such as Approxcanny, Canny, Chan-Vese, Kirsch, Prewitt, Robert, Sobel, Quadtree and Zero Crossing edge detector have been compared through greyscale images. Grayscale image has been enhancing before by modified white patch. Then, the holes area of the segmented license plate image are filled to obtain the characters, followed by step for removing the unwanted objects from the segmented license plate images. Later, the characters of the license plate are recognized based on template matching approach. This recognition analysis consists of two stages. First stage is all edge detector techniques have been used same standard values in removing the noise. Five edge detectors with best performance have been selected for next stage. In the second stage, the unwanted objects have been removed with appropriate values which are suitable for each of the edge detection techniques. The final result shows that Chan-Vese conquers the analysis with highest accuracy of edge detection obtained in license plate recognition.
      1
  • Publication
    Sauvola and Niblack Techniques Analysis for Segmentation of Vehicle License Plate
    ( 2020-07-09)
    Ariff Fatin Norazima Mohamad
    ;
    Aimi Salihah Abdul Nasir
    ;
    Haryati Jaafar
    ;
    Zulkifli A.
    License plate recognition system is functional to identify the vehicle registration number. This system is popular in image processing field. It's played important role in transportation system, especially for security system. However, variation condition of image acquisition causes the segmentation of license plate difficult to handle. This paper proposed a methodology for segmentation of license plate number by using thresholding segmentation group. In this study, image segmentation based on threshold has been chosen due to its ability in separating the foreground and the background. Hence, this technique is very useful for segmenting the characters which have tons of noise. Several threshold methods from the most commonly used techniques had been chosen to be compared and analyze the results for license plate detection and recognition. In this research, threshold techniques such as Savoula and Niblack have been select to compare. A total of 100 images captured by using a digital camera has been used the experimental analysis. After segmentation process, unwanted pixel has been removed with fixed value for each technique. Template matching has been used for classification of character recognition. The final result shows that Savoula conquers highest placed with great value in accuracy percentage of license plate recognition.
      36  5
  • 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
    Initial Study of Radio Tomographic Imaging for Human localization by using Simulation Model
    ( 2023-01-01)
    Abdullah M.S.M.
    ;
    Mohd Hafiz Fazalul Rahiman
    ;
    Nurul Syahirah Khalid
    ;
    Aimi Salihah Abdul Nasir
    This paper explains the details of modelling the simulation works designin setup for the RTI system. Th simulation modelling using software is focused on the interaction of electromagnetic behaviour in a dielectric medium of human inside a monitoring area. The modelling works have involved the criteria of the human, frequency and number of sensor nodes, dielectric properties of the human and last but not least, the configurations of the Radio tomography imaging (RTI) system. The model is then developed in the software to observe and investigate the result.
      1
  • Publication
    Robust Image Processing Framework for Intelligent Multi-Stage Malaria Parasite Recognition of Thick and Thin Smear Images
    ( 2023-02-01)
    Aris T.A.
    ;
    Aimi Salihah Abdul Nasir
    ;
    Wan Azani Wan Mustafa
    ;
    Mohd Yusoff Mashor
    ;
    Haryanto E.V.
    ;
    Mohamed Z.
    Malaria is a pressing medical issue in tropical and subtropical regions. Currently, the manual microscopic examination remains the gold standard malaria diagnosis method. Nevertheless, this procedure required highly skilled lab technicians to prepare and examine the slides. Therefore, a framework encompassing image processing and machine learning is proposed due to inconsistencies in manual inspection, counting, and staging. Here, a standardized segmentation framework utilizing thresholding and clustering is developed to segment parasites’ stages of P. falciparum and P. vivax species. Moreover, a multi-stage classifier is designed for recognizing parasite species and staging in both species. Experimental results indicate the effectiveness of segmenting thick smear images based on Phansalkar thresholding garnered an accuracy of 99.86%. The employment of variance and new transferring process for the clustered members, enhanced k-means (EKM) clustering has successfully segmented all malaria stages with accuracy and an F1-score of 99.20% and 0.9033, respectively. In addition, the accuracies of parasite detection, species recognition, and staging obtained through a random forest (RF) accounted for 86.89%, 98.82%, and 90.78%, respectively, simultaneously. The proposed framework enables versatile malaria parasite detection and staging with an interactive result, paving the path for future improvements by utilizing the proposed framework on all others malaria species.
      20  2
  • 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
    Malaria Parasite Diagnosis Using Computational Techniques: A Comprehensive Review
    ( 2021-12-01)
    Wan Azani Wan Mustafa
    ;
    Hiam Alquran
    ;
    Muhammad Zaid Aihsan
    ;
    Mohd Saifizi Saidon
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Aimi Salihah Abdul Nasir
    ;
    Mohamed Mydin Hj M.Abdul Kader
    ;
    Midhat Nabil Ahmad Salimi
    ;
    Mohd Wafi Nasrudin
    Malaria is a very serious disease that caused by the transmitted of parasites through the bites of infected Anopheles mosquito. Malaria death cases can be reduced and prevented through early diagnosis and prompt treatment. A fast and easy-to-use method, with high performance is required to differentiate malaria from non-malarial fevers. Manual examination of blood smears is currently the gold standard, but it is time-consuming, labour-intensive, requires skilled microscopists and the sensitivity of the method depends heavily on the skills of the microscopist. Currently, microscopy-based diagnosis remains the most widely used approach for malaria diagnosis. The development of automated malaria detection techniques is still a field of interest. Automated detection is faster and high accuracy compared to the traditional technique using microscopy. This paper presents an exhaustive review of these studies and suggests a direction for future developments of the malaria detection techniques. This paper analysis of three popular computational approaches which is k-mean clustering, neural network, and morphological approach was presented. Based on overall performance, many research proposed based on the morphological approach in order to detect malaria.
      9  29
  • Publication
    Real-time vision-based hand gesture to text interpreter by using artificial intelligence with augmented reality element
    ( 2024-03-07)
    Rosnazri M.H.
    ;
    Mohd Rizal Manan
    ;
    Rosnazri Ali
    ;
    Baseemah Mat Jalaluddin
    ;
    Aimi Salihah Abdul Nasir
    ;
    Muhammad Aizat Abu Bakar
    ;
    Zamri N.F.
    ;
    Rahmat M.A.
    ;
    Zamzuri M.A.
    ;
    Azmi M.A.A.
    Real-time Vision-based Hand Gesture to Text Interpreter by Using Artificial Intelligence with Augmented Reality Element is a device that can interpret sign language to text in real-time. This communicator used a machine learning approach with a slight touch of deep learning elements, which are OpenCV, MediaPipe, and TensorFlow algorithms. Those algorithms have been used to differentiate the hand from other objects, detect the movement and coordinate of hands and perform imagery data analysis to produce output instantly in real-time. The camera will detect the user's hand movement, and the output will be produced on an LCD monitor. This project has been developed by using Python programming language. 13,000 of ASL's alphabets and 5,000 of ASL's number imagery datasets have been collected and trained by using cloud platforms which are Google Teachable Machine and Google Colab. The training process produced 99.85% of accuracy for the alphabets and 100% accuracy for the number. Finally, the constructed machine learning algorithm able to display alphabets and numbers on an LCD monitor by performing ASL's alphabet and number hand gesture in real-time. The performance of the prototype has been analyzed and experimented by two users at plain and noise background with different determined distances.
      3  36