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

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Improvising non-uniform illumination and low contrast images of soil transmitted helminths image using contrast enhancement techniques

2021-01-01 , Norhanis Ayunie Ahmad Khairudin , Aimi Salihah Abdul Nasir , Lim Chee Chin , Haryati Jaafar , Mohamed Z.

Image enhancement plays an important role in image processing and computer vision. It is used to enhance the visual appearance in an image and also to convert the image suited to the requirement needed for image processing. In this paper, image enhancement is used to produce a better image by enhancing the image quality and highlighting the morphological features of the helminth eggs. Result obtained from enhancement is prepared for segmentation and classification process. The helminth eggs used in this paper are Ascaris Lumbricoides Ova (ALO) and Trichuris Trichiura Ova (TTO). In this study, several enhancement techniques have been performed on 100 images of ALO and TTO which have been captured under three different illuminations: normal, under-exposed and over-exposed images. The techniques used are global contrast stretching, limit contrast, linear contrast stretching, modified global contrast stretching, modified linear contrast stretching, partial contrast and reduce haze. Based on results obtained from these techniques, modified linear contrast stretching and modified global contrast stretching are able to equalize the lighting in the non-uniform illumination images of helminth eggs. Both techniques are suitable to be used on non-uniform illumination images and also able to improve the contrast in the image without affecting or removing the key features in ALO and TTO images as compared to the other techniques. Hence, the resultant images would become useful for parasitologist in analyzing helminth eggs.

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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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Home Service Robot Based on Image Recognition System

2022-01-01 , Syed Muhamad Akid Syed Zainal Abidin , Haryati Jaafar , Seng Lee Yeng

In a modern world with the hectic schedules, finding and delivering the object becomes a crucial task for some people. It can be troublesome to some people who has memory problem, tight schedule and disable person. Thus, an indoor autonomous face recognition and object tracking robot is proposed. The robot is created to detect and find small scale object in order to reduce time in finding the user’s personal belongings that missing. Three stages will be developed which are face recognition, searching object and pick and place. At the initial stage, the face recognition system is investigated to avoid misused by the unknown user. Once the user is identified, the avoidance obstacle robot will be functioned to find the particular object. The image of the object that has been captured initially is then processed by using image processing in this stage. The object detection is based on the template matching process. If the target object is false, it will search for the next object until the right one is detected. Once the object detected match the object instructed to be found, it would pick up that certain object. Three objects from different size and shape of object has been tested to determine the accuracy, specificity and sensitivity of the robot. The results shows the robot is able to perform with 80% accuracy and above for all objects.

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