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
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Scopus Author ID
57219027157
Researcher ID
AAG-1747-2021

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Fast k-means clustering algorithm for malaria detection in thick blood smear

2020-11-09 , Aris T.A. , Aimi Salihah Abdul Nasir , Lim Chee Chin , Haryati Jaafar , Mohamed Z.

Lots of people all over the world is threaten by a popular blood infection illness that is called as malaria. According to this fact, immediate diagnosis tests are essential to avoid the malaria parasites from expanding in every part of the body. Malaria detection is based on parasitic count process on thick blood smear samples. Anyhow, this mechanism consist the chances of misinterpretation of parasites on behalf to human flaws. Thus, this research objective is to investigate the segmentation performance for improving malaria detection in thick blood smear images through fast k-means clustering algorithm on various color models. In this research, fast kmeans clustering is used because of its advantage which is no need to retrain cluster center that causes time taken to train the image cluster centers is reduce. Meanwhile, different color models have been utilized in order to identify the most relevant color model that obviously highlight the parasites. Five varied color models namely RGB, XYZ, HSV, YUV and CMY are selected and 15 color components namely R, G, B, X, Y, Z, H, S, V, Y, U, V, C, M and Y component have been derived with the aim to discover which color component is the topnotch for malaria parasites detection. In general, around 100 thick blood smear images have been tested in this study and the outcomes reveal that the best segmentation performance is segmentation through R component of RGB with 99.81% accuracy.

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Character segmentation for automatic vehicle license plate recognition based on fast k-means clustering

2020-11-09 , Ariff F.N.M. , Aimi Salihah Abdul Nasir , Haryati Jaafar , Zulkifli A.N.

Automatic vehicle license plate recognition (AVLPR) system is one of application for transportation area under intelligent transport system. This system helps in monitor and identify the vehicle by reading the vehicles license plate numbers and recognize the plate characters automatically. However, various factors such as diversity of plate character viewpoint, shape, format and unstable light conditions at the time of image acquisition were obtained, have challenged the system to segment and recognize the characters. Therefore, this paper, presents an effective procedure approached based on fast k-mean (FKM) clustering. FKM approached have an ability to shortening the time of the image cluster centers process consumed. In addition, the FKM algorithm also able to overcomes the cluster center re-processing problem when constantly added the image in huge quantities. The proposed procedure begins with enhancing the input image by using modified white patch and converted into grayscale image. A total of 100 of images has been tested for the segmentation process with clustering techniques approach used. Template matching is used to standardize the recognition results obtained. The highest achieved was 88.57% of average accuracy for FKM clustering technique compared to k-means clustering where it was only able to achieve an average accuracy of 85.78% and 86.14% for fuzzy c-means. Thus, this show that the most efficient, quicker and more useful algorithm goes to FKM rather than the algorithm for fuzzy c-means (FCM) and k-means (KM). Therefore, it is possible toward consider the proposed FKM clustering as an image segmentation method for segmenting license plate images.

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Robust segmentation of COVID-19 chest X-Ray images: analysis of variant k-means based clustering algorithms

2025-02 , Aimi Salihah Abdul Nasir , Abdul Syafiq Abdull Sukor , Ooi Wei Herng

Computer aided diagnosis (CADx) become one the most famous method in diagnostic medical field due to the high reliability and efficiency. Recently, the coronavirus disease (COVID-19) has become severe global pandemic. Particularly, the Chest X-ray (CXR) imaging has become an essentiality in COVID-19 detection. As a result, the convergence of CADx technology with Chest X-ray analysis has achieved great efficiency in COVID-19 diagnosis. Therefore, the research value of CADx in COVID-19 diagnosis is exceptionally high. This study aims to evaluate different k-means based clustering algorithms and identifying the one with the highest overall accuracy. First of all, 150 COVID-19 CXR open-source images are acquired from Kaggle and Github. All the images will be unified into a same image size with 1000*1000 pixels and quality during the image pre-processing. Next, the resized images are enhanced by the Modified Global Contrast Stretching (MGCS) enhancement method to increase the quality of images. Then, the traditional k-means, k-medians, k-medoids and fast k-means clustering methods have been implemented in the image segmentation. At the same time, five different numbers <2, 4, 6, 8, 10> of clusters also tested out in this study. Lastly, all the segmented is proceeded to the segmentation performance based on sensitivity, specificity, accuracy, precision, recall and F-score. The result proves that the k-medoids clustering algorithm with 2 clusters archived the best overall segmentation performance as it obtained the highest sensitivity, accuracy, recall and F-score with 66.14%, 87.98%, 0.6614 and 0.7327.

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