Aimi Salihah Abdul Nasir
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Preferred name
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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Modified global and modified linear contrast stretching algorithms: new colour contrast enhancement techniques for microscopic analysis of Malaria slide images

2012-10-03 , Aimi Salihah Abdul Nasir , Mohd Yusoff Mashor , Zeehaida Mohamed

Malaria is one of the serious global health problem, causing widespread sufferings and deaths in various parts of the world. With the large number of cases diagnosed over the year, early detection and accurate diagnosis which facilitates prompt treatment is an essential requirement to control malaria. For centuries now, manual microscopic examination of blood slide remains the gold standard for malaria diagnosis. However, low contrast of the malaria and variable smears quality are some factors that may influence the accuracy of interpretation by microbiologists. In order to reduce this problem, this paper aims to investigate the performance of the proposed contrast enhancement techniques namely, modified global and modified linear contrast stretching as well as the conventional global and linear contrast stretching that have been applied on malaria images ofP. vivaxspecies. The results show that the proposed modified global and modified linear contrast stretching techniques have successfully increased the contrast of the parasites and the infected red blood cells compared to the conventional global and linear contrast stretching. Hence, the resultant images would become useful to microbiologists for identification of various stages and species of malaria.

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Comparative analysis of conventional and modern high-rise hotels in Penang based on hourly simulation of cooling load performance using DesignBuilder

2023 , Muhammad Hafeez Abdul Nasir , Ahmad Sanusi Hassan , Aimi Salihah Abdul Nasir , Mohd Suhaimi Mohd-Danuri , Mohd Nasrun Mohd Nawi , Rafikullah Deraman

The study examines the energy efficiency performance of hotel façades in relation to the annual cooling load simulation. In achieving the objective, two case studies of high-rise city hotels are selected within the locality of Penang, Malaysia. The case studies are selected based on the year of construction coupled with the architectural styles encompassing conventional and modern design of hotel facades. In traditional hotel facades, passive design elements, including proper window and wall materials selection alongside window-to-wall ratio (WWR), are less significant. Comparatively, elements of passive design in modern hotel facades are notable. In assessing the thermal performance of the hotel façade, a case study of the conventional and modern high-rise city hotels in Penang are selected to undergo hourly cooling load simulation in the hotel guestroom using the DesignBuilder simulation program in establishing the hotel’s energy efficiency performance. The findings revealed the average annual cooling energy of the conventional and modern high-rise city hotel guestrooms is 553 kWh/m2 and 538 kWh/m2, respectively. The study concludes the elements of passive design, including WWR, window material selection, and external wall colour are crucial in determining energy-efficient hotel operations.

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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.

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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.

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An intelligent diagnostic system for malaria

2015 , Aimi Salihah Abdul Nasir

Malaria is a serious health problem, causing many deaths and morbidity cases throughout the world particularly in Africa and south Asia. In 2013, there were about 198 million cases of malaria and an estimation of 584,000 deaths recorded globally, mostly among African children. Malaria is caused by infection of red blood cells with protozoan parasite of the genus Plasmodium. Plasmodium Falciparum and Plasmodium Vivax are the two main species that have caused the most malaria infections worldwide. Malaria can become lifethreatening if it is not treated immediately. Until now, microscopy-based diagnosis still remains the most widely used approaches for malaria diagnosis. However, this subjective evaluation procedure is time consuming, labour intensive and requires special training. Thus, this research has developed an intelligent diagnostic system for malaria which consists of image processing and intelligent classifier for the purpose of malaria diagnosis. A 3-stage classification of intelligent diagnostic system can be used as an early detection for malaria based on the classification of blood samples between normal and malaria on the first stage, and further classify the malaria sample as either P. falciparum or P. vivax species on the second stage, along with its four different life-cycle stages which are young trophozoite, mature trophozoite, schizont and gametocyte on the final stage. In order to perform the diagnosis process, the blood images were processed with various image processing techniques such as contrast enhancement and image segmentation for obtaining a fully segmented malaria parasite. As for contrast enhancement, this study proposed modified global and modified linear contrast stretching based on total pixel approach, as well as modified global and modified linear contrast stretching based on pixel level approach. After the image has been enhanced, the malaria image was segmented using different types of clustering algorithms. This included the used of the proposed enhanced kmeans clustering. The combination between contrast enhancement and image segmentation have provided good segmented malaria parasite. Later, various features such as size, shape and colour based features were extracted from the segmented malaria parasite. These features were fed as inputs to the three different classifiers which are multilayered perceptron (MLP) neural network trained by Levenberg-Marquardt (LM) algorithm, singlehidden layer feed forward neural network (SLFN) trained by online sequential extreme learning machine (OS-ELM) algorithm and random forest. The MLP network trained by LM algorithm has been proven to be the best with the highest classification performance as compared to others. Overall, the intelligent diagnostic system for malaria that has been developed using MLP network trained by LM algorithm is capable to perform the detection process by classifying a total of 1800 images consisting of malaria and normal blood images with testing accuracy, sensitivity and specificity of 95.28%, 96.06% and 86.00%, respectively. As for the diagnosis process, the system has classified a total of 1453 malaria images (accuracy of 90.81%) correctly into P. falciparum and P. vivax species, along with their four life-cycle stages. Thus, the proposed intelligent diagnostic system for malaria parasites is capable to perform the detection of malaria parasites, and then further diagnose the detected malaria parasites into its species and life-cycle stages.

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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.

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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.

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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.

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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.

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An Identification of Aspergillus Species: A Comparison on Supervised Classification Methods

2022-01-01 , Nur Rodiatul Raudah Mohamed Radzuan , Haryati Jaafar , Aimi Salihah Abdul Nasir

Aspergillus is one of the well-known existed saprophytic fungi that can withstand with various environments. Other can be beneficial in food industry, it also can be infectious to human and animals and normally, it attacks those with low immunity level. In order to keep the treatment in track with more accurate analysis, identification of Aspergillus plays an important role. Identification of Aspergillus is solely based on its characteristic and currently, there are two methods used which are microscopic and macroscopic examinations to observe its features. It handled by experienced microscopist and a few confirmations had to be done before presenting out the final result. Therefore, to prevent misidentification, an automated based identification is proposed. In this paper, different supervised classifiers are tested and compared to observe their ability to detect different 162 of Aspergillus images. The features have been extracted by using Principal component analysis (PCA) and several classifiers such as k- nearest neighbour (kNN), Sparse Representation Classifier (SRC), Support Vector Machine (SVM), Improved Fuzzy-Based k Nearest Centroid Neighbor (IFkNCN) and Kernal Sparse Representation Classifier (KSRC) are employed. Based on its accuracy, Aspergillus flavus recorded 80% of accuracy for all the classifiers.

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