Haryati Jaafar
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Preferred name
Haryati Jaafar
Official Name
Jaafar, Haryati
Alternative Name
Jaafar, Haryati
Jaafar, H.
Main Affiliation
Scopus Author ID
55357649900
Researcher ID
ILC-1943-2023

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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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Curvature-based active region segmentation for improved image processing of Aspergillus species

2025-04 , Haryati Jaafar , Nur Rodiatul Raudah Mohamed Radzuan , Farah Nabilah Zabani , Fatin Norazima Mohamad Ariff , Fatin Nadia Azman Fauzi

Aspergillus is one of the most ubiquitous of the airborne saprophytic fungi that can withstand various climatic conditions and could cause multiple type of illness. It can be beneficial to humankind and also can be infectious to humans and animals. Direct microscopic is used by trained microscopist as one of the alternatives in identification process to any specimen that suspected of having fungal infection. Confirmation towards identification is often necessary as the structure of Aspergillus is complex and dissimilar in each cycle. In addition, the structure of some species of Aspergillus are the almost same, which can be incorrectly recognized. In prevention of misidentification, computer-based Aspergillus species identification is proposed. The detection process is the earliest and important process hence, this paper proposed an active region-based segmentation method in order to detect the presence of fungi. This method is literally not depending on the gradient or sharp edges of the object and implementing level set function for curve evolution which able to reduce the computational cost. Originally, this function was developed for tracking fluid interfaces but in this study, this function has been applied to fungi database. Two different methods were tested and compared to observe their ability to segment different 80 of Aspergillus images which included four species. Experiments conducted have been compared with the baseline technique and the proposed method is outperformed in terms of accuracy, specificity with average of 90% and PSNR value of greater than 40dB. Meanwhile the active contour (snake) was slightly underperformed but well performed particularly in terms of sensitivity with greater than 80% for all the species. Moreover, upon scrutinizing the dice coefficients provided in both tables, it becomes apparent that there is a lack of significant variance in the values, except in the instance of Aspergillus fumigatus (active region-based) that which produces a result below 36%.

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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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