Displaying publications 41 - 60 of 74 in total

Abstract:
Sort:
  1. ProLysED: an integrated database and meta-server of bacterial protease systems
    Firdaus Raih M, Ahmad HA, Sharum MY, Azizi N, Mohamed R
    Appl. Bioinformatics, 2005;4(2):147-50.
    PMID: 16128617
    Bacterial proteases are an important group of enzymes that have very diverse biochemical and cellular functions. Proteases from prokaryotic sources also have a wide range of uses, either in medicine as pathogenic factors or in industry and therapeutics. ProLysED (Prokaryotic Lysis Enzymes Database), our meta-server integrated database of bacterial proteases, is a useful, albeit very niche, resource. The features include protease classification browsing and searching, organism-specific protease browsing, molecular information and visualisation of protease structures from the Protein Data Bank (PDB) as well as predicted protease structures.
    Matched MeSH terms: User-Computer Interface*
  2. Neural network classification of autoregressive features from electroencephalogram signals for brain-computer interface design
    Huan NJ, Palaniappan R
    J Neural Eng, 2004 Sep;1(3):142-50.
    PMID: 15876633
    In this paper, we have designed a two-state brain-computer interface (BCI) using neural network (NN) classification of autoregressive (AR) features from electroencephalogram (EEG) signals extracted during mental tasks. The main purpose of the study is to use Keirn and Aunon's data to investigate the performance of different mental task combinations and different AR features for BCI design for individual subjects. In the experimental study, EEG signals from five mental tasks were recorded from four subjects. Different combinations of two mental tasks were studied for each subject. Six different feature extraction methods were used to extract the features from the EEG signals: AR coefficients computed with Burg's algorithm, AR coefficients computed with a least-squares (LS) algorithm and adaptive autoregressive (AAR) coefficients computed with a least-mean-square (LMS) algorithm. All the methods used order six applied to 125 data points and these three methods were repeated with the same data but with segmentation into five segments in increments of 25 data points. The multilayer perceptron NN trained by the back-propagation algorithm (MLP-BP) and linear discriminant analysis (LDA) were used to classify the computed features into different categories that represent the mental tasks. We compared the classification performances among the six different feature extraction methods. The results showed that sixth-order AR coefficients with the LS algorithm without segmentation gave the best performance (93.10%) using MLP-BP and (97.00%) using LDA. The results also showed that the segmentation and AAR methods are not suitable for this set of EEG signals. We conclude that, for different subjects, the best mental task combinations are different and proper selection of mental tasks and feature extraction methods are essential for the BCI design.
    Matched MeSH terms: User-Computer Interface*
  3. Fulltext Role of OpenEHR as an open source solution for the regional modelling of patient data in obstetrics
    Pahl C, Zare M, Nilashi M, de Faria Borges MA, Weingaertner D, Detschew V, et al.
    J Biomed Inform, 2015 Jun;55:174-87.
    PMID: 25900270 DOI: 10.1016/j.jbi.2015.04.004
    This work investigates, whether openEHR with its reference model, archetypes and templates is suitable for the digital representation of demographic as well as clinical data. Moreover, it elaborates openEHR as a tool for modelling Hospital Information Systems on a regional level based on a national logical infrastructure. OpenEHR is a dual model approach developed for the modelling of Hospital Information Systems enabling semantic interoperability. A holistic solution to this represents the use of dual model based Electronic Healthcare Record systems. Modelling data in the field of obstetrics is a challenge, since different regions demand locally specific information for the process of treatment. Smaller health units in developing countries like Brazil or Malaysia, which until recently handled automatable processes like the storage of sensitive patient data in paper form, start organizational reconstruction processes. This archetype proof-of-concept investigation has tried out some elements of the openEHR methodology in cooperation with a health unit in Colombo, Brazil. Two legal forms provided by the Brazilian Ministry of Health have been analyzed and classified into demographic and clinical data. LinkEHR-Ed editor was used to read, edit and create archetypes. Results show that 33 clinical and demographic concepts, which are necessary to cover data demanded by the Unified National Health System, were identified. Out of the concepts 61% were reused and 39% modified to cover domain requirements. The detailed process of reuse, modification and creation of archetypes is shown. We conclude that, although a major part of demographic and clinical patient data were already represented by existing archetypes, a significant part required major modifications. In this study openEHR proved to be a highly suitable tool in the modelling of complex health data. In combination with LinkEHR-Ed software it offers user-friendly and highly applicable tools, although the complexity built by the vast specifications requires expert networks to define generally excepted clinical models. Finally, this project has pointed out main benefits enclosing high coverage of obstetrics data on the Clinical Knowledge Manager, simple modelling, and wide network and support using openEHR. Moreover, barriers described are enclosing the allocation of clinical content to respective archetypes, as well as stagnant adaption of changes on the Clinical Knowledge Manager leading to redundant efforts in data contribution that need to be addressed in future works.
    Matched MeSH terms: User-Computer Interface*
  4. A new brain-computer interface design using fuzzy ARTMAP
    Palaniappan R, Paramesran R, Nishida S, Saiwaki N
    IEEE Trans Neural Syst Rehabil Eng, 2002 Sep;10(3):140-8.
    PMID: 12503778
    This paper proposes a new brain-computer interface (BCI) design using fuzzy ARTMAP (FA) neural network, as well as an application of the design. The objective of this BCI-FA design is to classify the best three of the five available mental tasks for each subject using power spectral density (PSD) values of electroencephalogram (EEG) signals. These PSD values are extracted using the Wiener-Khinchine and autoregressive methods. Ten experiments employing different triplets of mental tasks are studied for each subject. The findings show that the average BCI-FA outputs for four subjects gave less than 6% of error using the best triplets of mental tasks identified from the classification performances of FA. This implies that the BCI-FA can be successfully used with a tri-state switching device. As an application, a proposed tri-state Morse code scheme could be utilized to translate the outputs of this BCI-FA design into English letters. In this scheme, the three BCI-FA outputs correspond to a dot and a dash, which are the two basic Morse code alphabets and a space to denote the end (or beginning) of a dot or a dash. The construction of English letters using this tri-state Morse code scheme is determined only by the sequence of mental tasks and is independent of the time duration of each mental task. This is especially useful for constructing letters that are represented as multiple dots or dashes. This combination of BCI-FA design and the tri-state Morse code scheme could be developed as a communication system for paralyzed patients.
    Matched MeSH terms: User-Computer Interface*
  5. Fulltext Usability and utility evaluation of the web-based "Should I Start Insulin?" patient decision aid for patients with type 2 diabetes among older people
    Lee YK, Lee PY, Ng CJ, Teo CH, Abu Bakar AI, Abdullah KL, et al.
    Inform Health Soc Care, 2018 Jan;43(1):73-83.
    PMID: 28139158 DOI: 10.1080/17538157.2016.1269108
    This study aimed to evaluate the usability (ease of use) and utility (impact on user's decision-making process) of a web-based patient decision aid (PDA) among older-age users. A pragmatic, qualitative research design was used. We recruited patients with type 2 diabetes who were at the point of making a decision about starting insulin from a tertiary teaching hospital in Malaysia in 2014. Computer screen recording software was used to record the website browsing session and in-depth interviews were conducted while playing back the website recording. The interviews were analyzed using the framework approach to identify usability and utility issues. Three cycles of iteration were conducted until no more major issues emerged. Thirteen patients participated: median age 65 years old, 10 men, and nine had secondary education/diploma, four were graduates/had postgraduate degree. Four usability issues were identified (navigation between pages and sections, a layout with open display, simple language, and equipment preferences). For utility, participants commented that the website influenced their decision about insulin in three ways: it had provided information about insulin, it helped them deliberate choices using the option-attribute matrix, and it allowed them to involve others in their decision making by sharing the PDA summary printout.
    Study site: urban tertiary teaching hospital outpatient clinic in Malaysia (primary care clinic, University Malaya Medical Centre, UMMC, Kuala Lumpur, Malaysia)
    Matched MeSH terms: User-Computer Interface*
  6. Challenges, alternatives, and paths to sustainability: better public health promotion using social networking pages as key tools
    Zaidan AA, Zaidan BB, Kadhem Z, Larbani M, Lakulu MB, Hashim M
    J Med Syst, 2015 Feb;39(2):7.
    PMID: 25631841 DOI: 10.1007/s10916-015-0201-y
    This paper discusses the possibility of promoting public health and implementing educational health services using Facebook. We discuss the challenges and strengths of using such a platform as a tool for public health care systems from two different perspectives, namely, the view of IT developers and that of physicians. We present a new way of evaluating user interactivity in health care systems from tools provided by Facebook that measure statistical traffic in the Internet. Findings show that Facebook is a very promising tool in promoting e-health services in Web 2.0. Results from statistical traffic show that a Facebook page is more efficient than other pages in promoting public health.
    Matched MeSH terms: User-Computer Interface
  7. Fulltext Human facial neural activities and gesture recognition for machine-interfacing applications
    Hamedi M, Salleh ShH, Tan TS, Ismail K, Ali J, Dee-Uam C, et al.
    Int J Nanomedicine, 2011;6:3461-72.
    PMID: 22267930 DOI: 10.2147/IJN.S26619
    The authors present a new method of recognizing different human facial gestures through their neural activities and muscle movements, which can be used in machine-interfacing applications. Human-machine interface (HMI) technology utilizes human neural activities as input controllers for the machine. Recently, much work has been done on the specific application of facial electromyography (EMG)-based HMI, which have used limited and fixed numbers of facial gestures. In this work, a multipurpose interface is suggested that can support 2-11 control commands that can be applied to various HMI systems. The significance of this work is finding the most accurate facial gestures for any application with a maximum of eleven control commands. Eleven facial gesture EMGs are recorded from ten volunteers. Detected EMGs are passed through a band-pass filter and root mean square features are extracted. Various combinations of gestures with a different number of gestures in each group are made from the existing facial gestures. Finally, all combinations are trained and classified by a Fuzzy c-means classifier. In conclusion, combinations with the highest recognition accuracy in each group are chosen. An average accuracy >90% of chosen combinations proved their ability to be used as command controllers.
    Matched MeSH terms: User-Computer Interface
  8. Asynchronous brain machine interface-based control of a wheelchair
    Hema CR, Paulraj MP, Yaacob S, Adom AH, Nagarajan R
    Adv Exp Med Biol, 2011;696:565-72.
    PMID: 21431597 DOI: 10.1007/978-1-4419-7046-6_57
    A brain machine interface (BMI) design for controlling the navigation of a power wheelchair is proposed. Real-time experiments with four able bodied subjects are carried out using the BMI-controlled wheelchair. The BMI is based on only two electrodes and operated by motor imagery of four states. A recurrent neural classifier is proposed for the classification of the four mental states. The real-time experiment results of four subjects are reported and problems emerging from asynchronous control are discussed.
    Matched MeSH terms: User-Computer Interface
  9. Evaluation of a content-based retrieval system for blood cell images with automated methods
    Seng WC, Mirisaee SH
    J Med Syst, 2011 Aug;35(4):571-8.
    PMID: 20703533 DOI: 10.1007/s10916-009-9393-3
    Content-based image retrieval techniques have been extensively studied for the past few years. With the growth of digital medical image databases, the demand for content-based analysis and retrieval tools has been increasing remarkably. Blood cell image is a key diagnostic tool for hematologists. An automated system that can retrieved relevant blood cell images correctly and efficiently would save the effort and time of hematologists. The purpose of this work is to develop such a content-based image retrieval system. Global color histogram and wavelet-based methods are used in the prototype. The system allows users to search by providing a query image and select one of four implemented methods. The obtained results demonstrate the proposed extended query refinement has the potential to capture a user's high level query and perception subjectivity by dynamically giving better query combinations. Color-based methods performed better than wavelet-based methods with regard to precision, recall rate and retrieval time. Shape and density of blood cells are suggested as measurements for future improvement. The system developed is useful for undergraduate education.
    Matched MeSH terms: User-Computer Interface
  10. A novel strategy for load balancing of distributed medical applications
    Logeswaran R, Chen LC
    J Med Syst, 2012 Apr;36(2):483-90.
    PMID: 20703702 DOI: 10.1007/s10916-010-9493-0
    Current trends in medicine, specifically in the electronic handling of medical applications, ranging from digital imaging, paperless hospital administration and electronic medical records, telemedicine, to computer-aided diagnosis, creates a burden on the network. Distributed Service Architectures, such as Intelligent Network (IN), Telecommunication Information Networking Architecture (TINA) and Open Service Access (OSA), are able to meet this new challenge. Distribution enables computational tasks to be spread among multiple processors; hence, performance is an important issue. This paper proposes a novel approach in load balancing, the Random Sender Initiated Algorithm, for distribution of tasks among several nodes sharing the same computational object (CO) instances in Distributed Service Architectures. Simulations illustrate that the proposed algorithm produces better network performance than the benchmark load balancing algorithms-the Random Node Selection Algorithm and the Shortest Queue Algorithm, especially under medium and heavily loaded conditions.
    Matched MeSH terms: User-Computer Interface
  11. Investigating data entry task performance on a laptop under the impact of vibration: the effect of color
    Mallick Z
    Int J Occup Saf Ergon, 2007;13(3):291-303.
    PMID: 17888238
    The last 20 years have seen a tremendous growth in mobile computing and wireless communications and services. An experimental study was conducted to explore the effect of text/background color on a laptop computing system along with variable environmental vibration on operators' data entry task performance in moving automobiles. The operators' performance was measured in terms of the number of characters entered per minute without spaces (NCEPMWS) on a laptop computing system. The subjects were divided into 3 categories, namely, Novices, Intermediates and Experts. Findings suggest a re-evaluation of existing laptop designs taking ergonomics into consideration. It appears that proper selection of text/background color on the laptop coupled with controlled vehicular speed could result in a better quality of interaction between human and laptops and it could also resolve the problem of poor data entry task performance.
    Matched MeSH terms: User-Computer Interface
  12. Asphyxia screening kit
    Zabidi A, Khuan LY, Mansor W
    Annu Int Conf IEEE Eng Med Biol Soc, 2012;2012:1298-301.
    PMID: 23366136 DOI: 10.1109/EMBC.2012.6346175
    Infant asphyxia is a condition due to insufficient oxygen intake suffered by newborn babies. A 4 to 9 million occurrences of infant asphyxia are reported each year by WHO. Early diagnosis of asphyxia is important to avoid complications such as damage to the brain, organ and tissue that could lead to fatality. This is possible with the automation of screening of infant asphyxia. Here, a non-invasive Asphyxia Screening Kit is developed. It is a Graphical User Interface that automatically detects asphyxia in infants from early birth to 6 months from their cries and displays the outcome of analysis. It is built with Matlab GUI underlied with signal processing algorithms, capable of achieving a classification accuracy of 96.03%. Successful implementation of ASK will assist to screen infant asphyxia for reference to clinicians for early diagnosis. In addition, ASK also provides an interface to enter patient information and images to be integrated with existing Hospital Information Management System.
    Matched MeSH terms: User-Computer Interface
  13. Fulltext Computational discovery and RT-PCR validation of novel Burkholderia conserved and Burkholderia pseudomallei unique sRNAs
    Khoo JS, Chai SF, Mohamed R, Nathan S, Firdaus-Raih M
    BMC Genomics, 2012;13 Suppl 7:S13.
    PMID: 23282220 DOI: 10.1186/1471-2164-13-S7-S13
    The sRNAs of bacterial pathogens are known to be involved in various cellular roles including environmental adaptation as well as regulation of virulence and pathogenicity. It is expected that sRNAs may also have similar functions for Burkholderia pseudomallei, a soil bacterium that can adapt to diverse environmental conditions, which causes the disease melioidosis and is also able to infect a wide variety of hosts.
    Matched MeSH terms: User-Computer Interface
  14. Conceptual Design of Haptic-Feedback Navigation Device for Individuals with Alzheimer's Disease
    Che Me R, Biamonti A, Mohd Saad MR
    Stud Health Technol Inform, 2015;217:195-203.
    PMID: 26294473
    Wayfinding ability in older adults with Alzheimer's disease (AD) is progressively impaired due to ageing and deterioration of cognitive domains. Usually, the sense of direction is deteriorated as visuospatial and spatial cognition are associated with the sensory acuity. Therefore, navigation systems that support only visual interactions may not be appropriate in case of AD. This paper presents a concept of wearable navigation device that integrates the haptic-feedback technology to facilitate the wayfinding of individuals with AD. The system provides the simplest instructions; left/right using haptic signals, as to avoid users' distraction during navigation. The advantages of haptic/tactile modality for wayfinding purpose based on several significant studies are presented. As preliminary assessment, a survey is conducted to understand the potential of this design concept in terms of (1) acceptability, (2) practicality, (3) wearability, and (4) environmental settings. Results indicate that the concept is highly acceptable and commercially implementable. A working prototype will be developed based on the results of the preliminary assessment. Introducing a new method of navigation should be followed by continuous practices for familiarization purpose. Improved navigability allows the good performance of activities of daily living (ADLs) hence maintain the good quality of life in older adults with AD.
    Matched MeSH terms: User-Computer Interface
  15. Visual Hybrid Development Learning System (VHDLS) framework for children with autism
    Banire B, Jomhari N, Ahmad R
    J Autism Dev Disord, 2015 Oct;45(10):3069-84.
    PMID: 25997598 DOI: 10.1007/s10803-015-2469-7
    The effect of education on children with autism serves as a relative cure for their deficits. As a result of this, they require special techniques to gain their attention and interest in learning as compared to typical children. Several studies have shown that these children are visual learners. In this study, we proposed a Visual Hybrid Development Learning System (VHDLS) framework that is based on an instructional design model, multimedia cognitive learning theory, and learning style in order to guide software developers in developing learning systems for children with autism. The results from this study showed that the attention of children with autism increased more with the proposed VHDLS framework.
    Matched MeSH terms: User-Computer Interface
  16. Fulltext Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm
    Al-Saffar A, Awang S, Tao H, Omar N, Al-Saiagh W, Al-Bared M
    PLoS One, 2018;13(4):e0194852.
    PMID: 29684036 DOI: 10.1371/journal.pone.0194852
    Sentiment analysis techniques are increasingly exploited to categorize the opinion text to one or more predefined sentiment classes for the creation and automated maintenance of review-aggregation websites. In this paper, a Malay sentiment analysis classification model is proposed to improve classification performances based on the semantic orientation and machine learning approaches. First, a total of 2,478 Malay sentiment-lexicon phrases and words are assigned with a synonym and stored with the help of more than one Malay native speaker, and the polarity is manually allotted with a score. In addition, the supervised machine learning approaches and lexicon knowledge method are combined for Malay sentiment classification with evaluating thirteen features. Finally, three individual classifiers and a combined classifier are used to evaluate the classification accuracy. In experimental results, a wide-range of comparative experiments is conducted on a Malay Reviews Corpus (MRC), and it demonstrates that the feature extraction improves the performance of Malay sentiment analysis based on the combined classification. However, the results depend on three factors, the features, the number of features and the classification approach.
    Matched MeSH terms: User-Computer Interface
  17. A review of disability EEG based wheelchair control system: Coherent taxonomy, open challenges and recommendations
    Al-Qaysi ZT, Zaidan BB, Zaidan AA, Suzani MS
    Comput Methods Programs Biomed, 2018 Oct;164:221-237.
    PMID: 29958722 DOI: 10.1016/j.cmpb.2018.06.012
    CONTEXT: Intelligent wheelchair technology has recently been utilised to address several mobility problems. Techniques based on brain-computer interface (BCI) are currently used to develop electric wheelchairs. Using human brain control in wheelchairs for people with disability has elicited widespread attention due to its flexibility.

    OBJECTIVE: This study aims to determine the background of recent studies on wheelchair control based on BCI for disability and map the literature survey into a coherent taxonomy. The study intends to identify the most important aspects in this emerging field as an impetus for using BCI for disability in electric-powered wheelchair (EPW) control, which remains a challenge. The study also attempts to provide recommendations for solving other existing limitations and challenges.

    METHODS: We systematically searched all articles about EPW control based on BCI for disability in three popular databases: ScienceDirect, IEEE and Web of Science. These databases contain numerous articles that considerably influenced this field and cover most of the relevant theoretical and technical issues.

    RESULTS: We selected 100 articles on the basis of our inclusion and exclusion criteria. A large set of articles (55) discussed on developing real-time wheelchair control systems based on BCI for disability signals. Another set of articles (25) focused on analysing BCI for disability signals for wheelchair control. The third set of articles (14) considered the simulation of wheelchair control based on BCI for disability signals. Four articles designed a framework for wheelchair control based on BCI for disability signals. Finally, one article reviewed concerns regarding wheelchair control based on BCI for disability signals.

    DISCUSSION: Since 2007, researchers have pursued the possibility of using BCI for disability in EPW control through different approaches. Regardless of type, articles have focused on addressing limitations that impede the full efficiency of BCI for disability and recommended solutions for these limitations.

    CONCLUSIONS: Studies on wheelchair control based on BCI for disability considerably influence society due to the large number of people with disability. Therefore, we aim to provide researchers and developers with a clear understanding of this platform and highlight the challenges and gaps in the current and future studies.

    Matched MeSH terms: User-Computer Interface
  18. Fulltext An analytical model to minimize the latency in healthcare internet-of-things in fog computing environment
    Shukla S, Hassan MF, Khan MK, Jung LT, Awang A
    PLoS One, 2019;14(11):e0224934.
    PMID: 31721807 DOI: 10.1371/journal.pone.0224934
    Fog computing (FC) is an evolving computing technology that operates in a distributed environment. FC aims to bring cloud computing features close to edge devices. The approach is expected to fulfill the minimum latency requirement for healthcare Internet-of-Things (IoT) devices. Healthcare IoT devices generate various volumes of healthcare data. This large volume of data results in high data traffic that causes network congestion and high latency. An increase in round-trip time delay owing to large data transmission and large hop counts between IoTs and cloud servers render healthcare data meaningless and inadequate for end-users. Time-sensitive healthcare applications require real-time data. Traditional cloud servers cannot fulfill the minimum latency demands of healthcare IoT devices and end-users. Therefore, communication latency, computation latency, and network latency must be reduced for IoT data transmission. FC affords the storage, processing, and analysis of data from cloud computing to a network edge to reduce high latency. A novel solution for the abovementioned problem is proposed herein. It includes an analytical model and a hybrid fuzzy-based reinforcement learning algorithm in an FC environment. The aim is to reduce high latency among healthcare IoTs, end-users, and cloud servers. The proposed intelligent FC analytical model and algorithm use a fuzzy inference system combined with reinforcement learning and neural network evolution strategies for data packet allocation and selection in an IoT-FC environment. The approach is tested on simulators iFogSim (Net-Beans) and Spyder (Python). The obtained results indicated the better performance of the proposed approach compared with existing methods.
    Matched MeSH terms: User-Computer Interface
  19. Fulltext SuCComBase: a manually curated repository of plant sulfur-containing compounds
    Harun S, Abdullah-Zawawi MR, A-Rahman MRA, Muhammad NAN, Mohamed-Hussein ZA
    Database (Oxford), 2019 01 01;2019.
    PMID: 30793170 DOI: 10.1093/database/baz021
    Plants produce a wide range of secondary metabolites that play important roles in plant defense and immunity, their interaction with the environment and symbiotic associations. Sulfur-containing compounds (SCCs) are a group of important secondary metabolites produced in members of the Brassicales order. SCCs constitute various groups of phytochemicals, but not much is known about them. Findings from previous studies on SCCs were scattered in published literatures, hence SuCComBase was developed to store all molecular information related to the biosynthesis of SCCs. Information that includes genes, proteins and compounds that are involved in the SCC biosynthetic pathway was manually identified from databases and published scientific literatures. Sets of co-expression data was analyzed to search for other possible (previously unknown) genes that might be involved in the biosynthesis of SCC. These genes were named as potential SCC-related encoding genes. A total of 147 known and 92 putative Arabidopsis thaliana SCC-related genes from literatures were used to identify other potential SCC-related encoding genes. We identified 778 potential SCC-related encoding genes, 4026 homologs to the SCC-related encoding genes and 116 SCCs as shown on SuCComBase homepage. Data entries are searchable from the Main page, Search, Browse and Datasets tabs. Users can easily download all data stored in SuCComBase. All publications related to SCCs are also indexed in SuCComBase, which is currently the first and only database dedicated to plant SCCs. SuCComBase aims to become a manually curated and au fait knowledge-based repository for plant SCCs.
    Matched MeSH terms: User-Computer Interface
  20. Fulltext Automated craniofacial landmarks detection on 3D image using geometry characteristics information
    Abu A, Ngo CG, Abu-Hassan NIA, Othman SA
    BMC Bioinformatics, 2019 Feb 04;19(Suppl 13):548.
    PMID: 30717658 DOI: 10.1186/s12859-018-2548-9
    BACKGROUND: Indirect anthropometry (IA) is one of the craniofacial anthropometry methods to perform the measurements on the digital facial images. In order to get the linear measurements, a few definable points on the structures of individual facial images have to be plotted as landmark points. Currently, most anthropometric studies use landmark points that are manually plotted on a 3D facial image by the examiner. This method is time-consuming and leads to human biases, which will vary from intra-examiners to inter-examiners when involving large data sets. Biased judgment also leads to a wider gap in measurement error. Thus, this work aims to automate the process of landmarks detection to help in enhancing the accuracy of measurement. In this work, automated craniofacial landmarks (ACL) on a 3D facial image system was developed using geometry characteristics information to identify the nasion (n), pronasale (prn), subnasale (sn), alare (al), labiale superius (ls), stomion (sto), labiale inferius (li), and chelion (ch). These landmarks were detected on the 3D facial image in .obj file format. The IA was also performed by manually plotting the craniofacial landmarks using Mirror software. In both methods, once all landmarks were detected, the eight linear measurements were then extracted. Paired t-test was performed to check the validity of ACL (i) between the subjects and (ii) between the two methods, by comparing the linear measurements extracted from both ACL and AI. The tests were performed on 60 subjects (30 males and 30 females).

    RESULTS: The results on the validity of the ACL against IA between the subjects show accurate detection of n, sn, prn, sto, ls and li landmarks. The paired t-test showed that the seven linear measurements were statistically significant when p 

    Matched MeSH terms: User-Computer Interface
Related Terms
Filters
Contact Us

Please provide feedback to Administrator ([email protected])

External Links