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Fulltext Robust identification of polyethylene terephthalate (PET) plastics through Bayesian decision

Zulkifley MA, Mustafa MM, Hussain A, Mustapha A, Ramli S

PLoS One, 2014;9(12):e114518.
PMID: 25485630 DOI: 10.1371/journal.pone.0114518

Recycling is one of the most efficient methods for environmental friendly waste management. Among municipal wastes, plastics are the most common material that can be easily recycled and polyethylene terephthalate (PET) is one of its major types. PET material is used in consumer goods packaging such as drinking bottles, toiletry containers, food packaging and many more. Usually, a recycling process is tailored to a specific material for optimal purification and decontamination to obtain high grade recyclable material. The quantity and quality of the sorting process are limited by the capacity of human workers that suffer from fatigue and boredom. Several automated sorting systems have been proposed in the literature that include using chemical, proximity and vision sensors. The main advantages of vision based sensors are its environmentally friendly approach, non-intrusive detection and capability of high throughput. However, the existing methods rely heavily on deterministic approaches that make them less accurate as the variations in PET plastic waste appearance are too high. We proposed a probabilistic approach of modeling the PET material by analyzing the reflection region and its surrounding. Three parameters are modeled by Gaussian and exponential distributions: color, size and distance of the reflection region. The final classification is made through a supervised training method of likelihood ratio test. The main novelty of the proposed method is the probabilistic approach in integrating various PET material signatures that are contaminated by stains under constant lighting changes. The system is evaluated by using four performance metrics: precision, recall, accuracy and error. Our system performed the best in all evaluation metrics compared to the benchmark methods. The system can be further improved by fusing all neighborhood information in decision making and by implementing the system in a graphics processing unit for faster processing speed.
Fulltext Telepointer technology in telemedicine: a review

Abdul Karim R, Zakaria NF, Zulkifley MA, Mustafa MM, Sagap I, Md Latar NH

Biomed Eng Online, 2013;12:21.
PMID: 23496940 DOI: 10.1186/1475-925X-12-21

Telepointer is a powerful tool in the telemedicine system that enhances the effectiveness of long-distance communication. Telepointer has been tested in telemedicine, and has potential to a big influence in improving quality of health care, especially in the rural area. A telepointer system works by sending additional information in the form of gesture that can convey more accurate instruction or information. It leads to more effective communication, precise diagnosis, and better decision by means of discussion and consultation between the expert and the junior clinicians. However, there is no review paper yet on the state of the art of the telepointer in telemedicine. This paper is intended to give the readers an overview of recent advancement of telepointer technology as a support tool in telemedicine. There are four most popular modes of telepointer system, namely cursor, hand, laser and sketching pointer. The result shows that telepointer technology has a huge potential for wider acceptance in real life applications, there are needs for more improvement in the real time positioning accuracy. More results from actual test (real patient) need to be reported. We believe that by addressing these two issues, telepointer technology will be embraced widely by researchers and practitioners.
Enhanced optical flow field of left ventricular motion using quasi-Gaussian DCT filter

Riyadi S, Mustafa MM, Hussain A, Maskon O, Nor IF

Adv Exp Med Biol, 2011;696:461-9.
PMID: 21431586 DOI: 10.1007/978-1-4419-7046-6_46

Left ventricular motion estimation is very important for diagnosing cardiac abnormality. One of the popular techniques, optical flow technique, promises useful results for motion quantification. However, optical flow technique often failed to provide smooth vector field due to the complexity of cardiac motion and the presence of speckle noise. This chapter proposed a new filtering technique, called quasi-Gaussian discrete cosine transform (QGDCT)-based filter, to enhance the optical flow field for myocardial motion estimation. Even though Gaussian filter and DCT concept have been implemented in other previous researches, this filter introduces a different approach of Gaussian filter model based on high frequency properties of cosine function. The QGDCT is a customized quasi discrete Gaussian filter in which its coefficients are derived from a selected two-dimensional DCT. This filter was implemented before and after the computation of optical flow to reduce the speckle noise and to improve the flow field smoothness, respectively. The algorithm was first validated on synthetic echocardiography image that simulates a contracting myocardium motion. Subsequently, this method was also implemented on clinical echocardiography images. To evaluate the performance of the technique, several quantitative measurements such as magnitude error, angular error, and standard error of measurement are computed and analyzed. The final motion estimation results were in good agreement with the physician manual interpretation.
On the use of collinear and triangle equation for automatic segmentation and boundary detection of cardiac cavity images

Sigit R, Mustafa MM, Hussain A, Maskon O, Nor IF

Adv Exp Med Biol, 2011;696:481-8.
PMID: 21431588 DOI: 10.1007/978-1-4419-7046-6_48

In this chapter, the computational biology of cardiac cavity images is proposed. The method uses collinear and triangle equation algorithms to detect and reconstruct the boundary of the cardiac cavity. The first step involves high boost filter to enhance the high frequency component without affecting the low frequency component. Second, the morphological and thresholding operators are applied to the image to eliminate noise and convert the image into a binary image. Next, the edge detection is performed using the negative Laplacian filter and followed by region filtering. Finally, the collinear and triangle equations are used to detect and reconstruct the more precise cavity boundary. Results obtained have proved that this technique is able to perform better segmentation and detection of the boundary of cardiac cavity from echocardiographic images.
Fulltext Epitope-Based Peptide Vaccine against Glycoprotein G of Nipah Henipavirus Using Immunoinformatics Approaches

Mohammed AA, Shantier SW, Mustafa MI, Osman HK, Elmansi HE, Osman IA, et al.

J Immunol Res, 2020;2020:2567957.
PMID: 32377531 DOI: 10.1155/2020/2567957

Background: Nipah belongs to the genus Henipavirus and the Paramyxoviridae family. It is an endemic most commonly found at South Asia and has first emerged in Malaysia in 1998. Bats are found to be the main reservoir for this virus, causing disease in both humans and animals. The last outbreak has occurred in May 2018 in Kerala. It is characterized by high pathogenicity and fatality rates which varies from 40% to 70% depending on the severity of the disease and on the availability of adequate healthcare facilities. Currently, there are no antiviral drugs available for NiV disease and the treatment is just supportive. Clinical presentations for this virus range from asymptomatic infection to fatal encephalitis.
Objective: This study is aimed at predicting an effective epitope-based vaccine against glycoprotein G of Nipah henipavirus, using immunoinformatics approaches.
Methods and Materials: Glycoprotein G of the Nipah virus sequence was retrieved from NCBI. Different prediction tools were used to analyze the epitopes, namely, BepiPred-2.0: Sequential B Cell Epitope Predictor for B cell and T cell MHC classes II and I. Then, the proposed peptides were docked using Autodock 4.0 software program. Results and Conclusions. The two peptides TVYHCSAVY and FLIDRINWI have showed a very strong binding affinity to MHC class I and MHC class II alleles. Furthermore, considering the conservancy, the affinity, and the population coverage, the peptide FLIDRINWIT is highly suitable to be utilized to formulate a new vaccine against glycoprotein G of Nipah henipavirus. An in vivo study for the proposed peptides is also highly recommended.