Displaying publications 1 - 20 of 89 in total

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  1. Fulltext Primary stability recognition of the newly designed cementless femoral stem using digital signal processing
    Baharuddin MY, Salleh ShH, Hamedi M, Zulkifly AH, Lee MH, Mohd Noor A, et al.
    Biomed Res Int, 2014;2014:478248.
    PMID: 24800230 DOI: 10.1155/2014/478248
    Stress shielding and micromotion are two major issues which determine the success of newly designed cementless femoral stems. The correlation of experimental validation with finite element analysis (FEA) is commonly used to evaluate the stress distribution and fixation stability of the stem within the femoral canal. This paper focused on the applications of feature extraction and pattern recognition using support vector machine (SVM) to determine the primary stability of the implant. We measured strain with triaxial rosette at the metaphyseal region and micromotion with linear variable direct transducer proximally and distally using composite femora. The root mean squares technique is used to feed the classifier which provides maximum likelihood estimation of amplitude, and radial basis function is used as the kernel parameter which mapped the datasets into separable hyperplanes. The results showed 100% pattern recognition accuracy using SVM for both strain and micromotion. This indicates that DSP could be applied in determining the femoral stem primary stability with high pattern recognition accuracy in biomechanical testing.
    Matched MeSH terms: Pattern Recognition, Automated/methods*
  2. Modelling complex features from histone modification signatures using genetic algorithm for the prediction of enhancer region
    Lee NK, Fong PK, Abdullah MT
    Biomed Mater Eng, 2014;24(6):3807-14.
    PMID: 25227097 DOI: 10.3233/BME-141210
    Using Genetic Algorithm, this paper presents a modelling method to generate novel logical-based features from DNA sequences enriched with H3K4mel histone signatures. Current histone signature is mostly represented using k-mers content features incapable of representing all the possible complex interactions of various DNA segments. The main contributions are, among others: (a) demonstrating that there are complex interactions among sequence segments in the histone regions; (b) developing a parse tree representation of the logical complex features. The proposed novel feature is compared to the k-mers content features using datasets from the mouse (mm9) genome. Evaluation results show that the new feature improves the prediction performance as shown by f-measure for all datasets tested. Also, it is discovered that tree-based features generated from a single chromosome can be generalized to predict histone marks in other chromosomes not used in the training. These findings have a great impact on feature design considerations for histone signatures as well as other classifier design features.
    Matched MeSH terms: Pattern Recognition, Automated/methods
  3. A hybrid ART-GRNN online learning neural network with a epsilon -insensitive loss function
    Yap KS, Lim CP, Abidin IZ
    IEEE Trans Neural Netw, 2008 Sep;19(9):1641-6.
    PMID: 18779094 DOI: 10.1109/TNN.2008.2000992
    In this brief, a new neural network model called generalized adaptive resonance theory (GART) is introduced. GART is a hybrid model that comprises a modified Gaussian adaptive resonance theory (MGA) and the generalized regression neural network (GRNN). It is an enhanced version of the GRNN, which preserves the online learning properties of adaptive resonance theory (ART). A series of empirical studies to assess the effectiveness of GART in classification, regression, and time series prediction tasks is conducted. The results demonstrate that GART is able to produce good performances as compared with those of other methods, including the online sequential extreme learning machine (OSELM) and sequential learning radial basis function (RBF) neural network models.
    Matched MeSH terms: Pattern Recognition, Automated/methods*
  4. A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images
    Raghavendra U, Gudigar A, Bhandary SV, Rao TN, Ciaccio EJ, Acharya UR
    J Med Syst, 2019 Jul 30;43(9):299.
    PMID: 31359230 DOI: 10.1007/s10916-019-1427-x
    Glaucoma is a type of eye condition which may result in partial or consummate vision loss. Higher intraocular pressure is the leading cause for this condition. Screening for glaucoma and early detection can avert vision loss. Computer aided diagnosis (CAD) is an automated process with the potential to identify glaucoma early through quantitative analysis of digital fundus images. Preparing an effective model for CAD requires a large database. This study presents a CAD tool for the precise detection of glaucoma using a machine learning approach. An autoencoder is trained to determine effective and important features from fundus images. These features are used to develop classes of glaucoma for testing. The method achieved an F - measure value of 0.95 utilizing 1426 digital fundus images (589 control and 837 glaucoma). The efficacy of the system is evident, and is suggestive of its possible utility as an additional tool for verification of clinical decisions.
    Matched MeSH terms: Pattern Recognition, Automated/methods
  5. A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images
    Acharya UR, Bhat S, Koh JEW, Bhandary SV, Adeli H
    Comput Biol Med, 2017 Sep 01;88:72-83.
    PMID: 28700902 DOI: 10.1016/j.compbiomed.2017.06.022
    Glaucoma is an optic neuropathy defined by characteristic damage to the optic nerve and accompanying visual field deficits. Early diagnosis and treatment are critical to prevent irreversible vision loss and ultimate blindness. Current techniques for computer-aided analysis of the optic nerve and retinal nerve fiber layer (RNFL) are expensive and require keen interpretation by trained specialists. Hence, an automated system is highly desirable for a cost-effective and accurate screening for the diagnosis of glaucoma. This paper presents a new methodology and a computerized diagnostic system. Adaptive histogram equalization is used to convert color images to grayscale images followed by convolution of these images with Leung-Malik (LM), Schmid (S), and maximum response (MR4 and MR8) filter banks. The basic microstructures in typical images are called textons. The convolution process produces textons. Local configuration pattern (LCP) features are extracted from these textons. The significant features are selected using a sequential floating forward search (SFFS) method and ranked using the statistical t-test. Finally, various classifiers are used for classification of images into normal and glaucomatous classes. A high classification accuracy of 95.8% is achieved using six features obtained from the LM filter bank and the k-nearest neighbor (kNN) classifier. A glaucoma integrative index (GRI) is also formulated to obtain a reliable and effective system.
    Matched MeSH terms: Pattern Recognition, Automated/methods*
  6. Fulltext Improving RLRN image splicing detection with the Use of PCA and kernel PCA
    Moghaddasi Z, Jalab HA, Md Noor R, Aghabozorgi S
    ScientificWorldJournal, 2014;2014:606570.
    PMID: 25295304 DOI: 10.1155/2014/606570
    Digital image forgery is becoming easier to perform because of the rapid development of various manipulation tools. Image splicing is one of the most prevalent techniques. Digital images had lost their trustability, and researches have exerted considerable effort to regain such trustability by focusing mostly on algorithms. However, most of the proposed algorithms are incapable of handling high dimensionality and redundancy in the extracted features. Moreover, existing algorithms are limited by high computational time. This study focuses on improving one of the image splicing detection algorithms, that is, the run length run number algorithm (RLRN), by applying two dimension reduction methods, namely, principal component analysis (PCA) and kernel PCA. Support vector machine is used to distinguish between authentic and spliced images. Results show that kernel PCA is a nonlinear dimension reduction method that has the best effect on R, G, B, and Y channels and gray-scale images.
    Matched MeSH terms: Pattern Recognition, Automated/methods*
  7. Fulltext Feasibility of fiber optic displacement sensor scanning system for imaging of dental cavity
    Rahman HA, Che Ani AI, Harun SW, Yasin M, Apsari R, Ahmad H
    J Biomed Opt, 2012 Jul;17(7):071308.
    PMID: 22894469 DOI: 10.1117/1.JBO.17.7.071308
    The purpose of this study is to investigate the potential of intensity modulated fiber optic displacement sensor scanning system for the imaging of dental cavity. Here, we discuss our preliminary results in the imaging of cavities on various teeth surfaces, as well as measurement of the diameter of the cavities which are represented by drilled holes on the teeth surfaces. Based on the analysis of displacement measurement, the sensitivities and linear range for the molar, canine, hybrid composite resin, and acrylic surfaces are obtained at 0.09667 mV/mm and 0.45 mm; 0.775 mV/mm and 0.4 mm; 0.5109 mV/mm and 0.5 mm; and 0.25 mV/mm and 0.5 mm, respectively, with a good linearity of more than 99%. The results also show a clear distinction between the cavity and surrounding tooth region. The stability, simplicity of design, and low cost of fabrication make it suitable for restorative dentistry.
    Matched MeSH terms: Pattern Recognition, Automated/methods*
  8. Fulltext Intelligent bar chart plagiarism detection in documents
    Al-Dabbagh MM, Salim N, Rehman A, Alkawaz MH, Saba T, Al-Rodhaan M, et al.
    ScientificWorldJournal, 2014;2014:612787.
    PMID: 25309952 DOI: 10.1155/2014/612787
    This paper presents a novel features mining approach from documents that could not be mined via optical character recognition (OCR). By identifying the intimate relationship between the text and graphical components, the proposed technique pulls out the Start, End, and Exact values for each bar. Furthermore, the word 2-gram and Euclidean distance methods are used to accurately detect and determine plagiarism in bar charts.
    Matched MeSH terms: Pattern Recognition, Automated/methods*
  9. Classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications
    Al-Quraishi MS, Ishak AJ, Ahmad SA, Hasan MK, Al-Qurishi M, Ghapanchizadeh H, et al.
    Med Biol Eng Comput, 2017 May;55(5):747-758.
    PMID: 27484411 DOI: 10.1007/s11517-016-1551-4
    Electromyography (EMG)-based control is the core of prostheses, orthoses, and other rehabilitation devices in recent research. Nonetheless, EMG is difficult to use as a control signal given the complex nature of the signal. To overcome this problem, the researchers employed a pattern recognition technique. EMG pattern recognition mainly involves four stages: signal detection, preprocessing feature extraction, dimensionality reduction, and classification. In particular, the success of any pattern recognition technique depends on the feature extraction stage. In this study, a modified time-domain features set and logarithmic transferred time-domain features (LTD) were evaluated and compared with other traditional time-domain features set (TTD). Three classifiers were employed to assess the two feature sets, namely linear discriminant analysis (LDA), k nearest neighborhood, and Naïve Bayes. Results indicated the superiority of the new time-domain feature set LTD, on conventional time-domain features TTD with the average classification accuracy of 97.23 %. In addition, the LDA classifier outperformed the other two classifiers considered in this study.
    Matched MeSH terms: Pattern Recognition, Automated/methods
  10. Fulltext LAVRF: Sign language recognition via Lightweight Attentive VGG16 with Random Forest
    Ewe ELR, Lee CP, Lim KM, Kwek LC, Alqahtani A
    PLoS One, 2024;19(4):e0298699.
    PMID: 38574042 DOI: 10.1371/journal.pone.0298699
    Sign language recognition presents significant challenges due to the intricate nature of hand gestures and the necessity to capture fine-grained details. In response to these challenges, a novel approach is proposed-Lightweight Attentive VGG16 with Random Forest (LAVRF) model. LAVRF introduces a refined adaptation of the VGG16 model integrated with attention modules, complemented by a Random Forest classifier. By streamlining the VGG16 architecture, the Lightweight Attentive VGG16 effectively manages complexity while incorporating attention mechanisms that dynamically concentrate on pertinent regions within input images, resulting in enhanced representation learning. Leveraging the Random Forest classifier provides notable benefits, including proficient handling of high-dimensional feature representations, reduction of variance and overfitting concerns, and resilience against noisy and incomplete data. Additionally, the model performance is further optimized through hyperparameter optimization, utilizing the Optuna in conjunction with hill climbing, which efficiently explores the hyperparameter space to discover optimal configurations. The proposed LAVRF model demonstrates outstanding accuracy on three datasets, achieving remarkable results of 99.98%, 99.90%, and 100% on the American Sign Language, American Sign Language with Digits, and NUS Hand Posture datasets, respectively.
    Matched MeSH terms: Pattern Recognition, Automated/methods
  11. Quantification of diffusion tensor imaging in normal white matter maturation of early childhood using an automated processing pipeline
    Loh KB, Ramli N, Tan LK, Roziah M, Rahmat K, Ariffin H
    Eur Radiol, 2012 Jul;22(7):1413-26.
    PMID: 22434420 DOI: 10.1007/s00330-012-2396-3
    OBJECTIVES: The degree and status of white matter myelination can be sensitively monitored using diffusion tensor imaging (DTI). This study looks at the measurement of fractional anistropy (FA) and mean diffusivity (MD) using an automated ROI with an existing DTI atlas.

    METHODS: Anatomical MRI and structural DTI were performed cross-sectionally on 26 normal children (newborn to 48 months old), using 1.5-T MRI. The automated processing pipeline was implemented to convert diffusion-weighted images into the NIfTI format. DTI-TK software was used to register the processed images to the ICBM DTI-81 atlas, while AFNI software was used for automated atlas-based volumes of interest (VOIs) and statistical value extraction.

    RESULTS: DTI exhibited consistent grey-white matter contrast. Triphasic temporal variation of the FA and MD values was noted, with FA increasing and MD decreasing rapidly early in the first 12 months. The second phase lasted 12-24 months during which the rate of FA and MD changes was reduced. After 24 months, the FA and MD values plateaued.

    CONCLUSION: DTI is a superior technique to conventional MR imaging in depicting WM maturation. The use of the automated processing pipeline provides a reliable environment for quantitative analysis of high-throughput DTI data.

    KEY POINTS: Diffusion tensor imaging outperforms conventional MRI in depicting white matter maturation. • DTI will become an important clinical tool for diagnosing paediatric neurological diseases. • DTI appears especially helpful for developmental abnormalities, tumours and white matter disease. • An automated processing pipeline assists quantitative analysis of high throughput DTI data.

    Matched MeSH terms: Pattern Recognition, Automated/methods
  12. Improved GART neural network model for pattern classification and rule extraction with application to power systems
    Yap KS, Lim CP, Au MT
    IEEE Trans Neural Netw, 2011 Dec;22(12):2310-23.
    PMID: 22067292 DOI: 10.1109/TNN.2011.2173502
    Generalized adaptive resonance theory (GART) is a neural network model that is capable of online learning and is effective in tackling pattern classification tasks. In this paper, we propose an improved GART model (IGART), and demonstrate its applicability to power systems. IGART enhances the dynamics of GART in several aspects, which include the use of the Laplacian likelihood function, a new vigilance function, a new match-tracking mechanism, an ordering algorithm for determining the sequence of training data, and a rule extraction capability to elicit if-then rules from the network. To assess the effectiveness of IGART and to compare its performances with those from other methods, three datasets that are related to power systems are employed. The experimental results demonstrate the usefulness of IGART with the rule extraction capability in undertaking classification problems in power systems engineering.
    Matched MeSH terms: Pattern Recognition, Automated/methods*
  13. Fulltext Surface electromyography signal processing and classification techniques
    Chowdhury RH, Reaz MB, Ali MA, Bakar AA, Chellappan K, Chang TG
    Sensors (Basel), 2013;13(9):12431-66.
    PMID: 24048337 DOI: 10.3390/s130912431
    Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.
    Matched MeSH terms: Pattern Recognition, Automated/methods*
  14. Fulltext An artifacts removal post-processing for epiphyseal region-of-interest (EROI) localization in automated bone age assessment (BAA)
    Chai HY, Wee LK, Swee TT, Salleh ShH, Chea LY
    Biomed Eng Online, 2011;10:87.
    PMID: 21952080 DOI: 10.1186/1475-925X-10-87
    Segmentation is the most crucial part in the computer-aided bone age assessment. A well-known type of segmentation performed in the system is adaptive segmentation. While providing better result than global thresholding method, the adaptive segmentation produces a lot of unwanted noise that could affect the latter process of epiphysis extraction.
    Matched MeSH terms: Pattern Recognition, Automated/methods*
  15. Fulltext Naive Bayes-guided bat algorithm for feature selection
    Taha AM, Mustapha A, Chen SD
    ScientificWorldJournal, 2013;2013:325973.
    PMID: 24396295 DOI: 10.1155/2013/325973
    When the amount of data and information is said to double in every 20 months or so, feature selection has become highly important and beneficial. Further improvements in feature selection will positively affect a wide array of applications in fields such as pattern recognition, machine learning, or signal processing. Bio-inspired method called Bat Algorithm hybridized with a Naive Bayes classifier has been presented in this work. The performance of the proposed feature selection algorithm was investigated using twelve benchmark datasets from different domains and was compared to three other well-known feature selection algorithms. Discussion focused on four perspectives: number of features, classification accuracy, stability, and feature generalization. The results showed that BANB significantly outperformed other algorithms in selecting lower number of features, hence removing irrelevant, redundant, or noisy features while maintaining the classification accuracy. BANB is also proven to be more stable than other methods and is capable of producing more general feature subsets.
    Matched MeSH terms: Pattern Recognition, Automated/methods*
  16. Fulltext QuaBingo: A Prediction System for Protein Quaternary Structure Attributes Using Block Composition
    Tung CH, Chen CW, Guo RC, Ng HF, Chu YW
    Biomed Res Int, 2016;2016:9480276.
    PMID: 27610389 DOI: 10.1155/2016/9480276
    Background. Quaternary structures of proteins are closely relevant to gene regulation, signal transduction, and many other biological functions of proteins. In the current study, a new method based on protein-conserved motif composition in block format for feature extraction is proposed, which is termed block composition. Results. The protein quaternary assembly states prediction system which combines blocks with functional domain composition, called QuaBingo, is constructed by three layers of classifiers that can categorize quaternary structural attributes of monomer, homooligomer, and heterooligomer. The building of the first layer classifier uses support vector machines (SVM) based on blocks and functional domains of proteins, and the second layer SVM was utilized to process the outputs of the first layer. Finally, the result is determined by the Random Forest of the third layer. We compared the effectiveness of the combination of block composition, functional domain composition, and pseudoamino acid composition of the model. In the 11 kinds of functional protein families, QuaBingo is 23% of Matthews Correlation Coefficient (MCC) higher than the existing prediction system. The results also revealed the biological characterization of the top five block compositions. Conclusions. QuaBingo provides better predictive ability for predicting the quaternary structural attributes of proteins.
    Matched MeSH terms: Pattern Recognition, Automated/methods
  17. Fulltext Automated identification of Monogeneans using digital image processing and K-nearest neighbour approaches
    Yousef Kalafi E, Tan WB, Town C, Dhillon SK
    BMC Bioinformatics, 2016 Dec 22;17(Suppl 19):511.
    PMID: 28155722 DOI: 10.1186/s12859-016-1376-z
    BACKGROUND: Monogeneans are flatworms (Platyhelminthes) that are primarily found on gills and skin of fishes. Monogenean parasites have attachment appendages at their haptoral regions that help them to move about the body surface and feed on skin and gill debris. Haptoral attachment organs consist of sclerotized hard parts such as hooks, anchors and marginal hooks. Monogenean species are differentiated based on their haptoral bars, anchors, marginal hooks, reproductive parts' (male and female copulatory organs) morphological characters and soft anatomical parts. The complex structure of these diagnostic organs and also their overlapping in microscopic digital images are impediments for developing fully automated identification system for monogeneans (LNCS 7666:256-263, 2012), (ISDA; 457-462, 2011), (J Zoolog Syst Evol Res 52(2): 95-99. 2013;). In this study images of hard parts of the haptoral organs such as bars and anchors are used to develop a fully automated identification technique for monogenean species identification by implementing image processing techniques and machine learning methods.

    RESULT: Images of four monogenean species namely Sinodiplectanotrema malayanus, Trianchoratus pahangensis, Metahaliotrema mizellei and Metahaliotrema sp. (undescribed) were used to develop an automated technique for identification. K-nearest neighbour (KNN) was applied to classify the monogenean specimens based on the extracted features. 50% of the dataset was used for training and the other 50% was used as testing for system evaluation. Our approach demonstrated overall classification accuracy of 90%. In this study Leave One Out (LOO) cross validation is used for validation of our system and the accuracy is 91.25%.

    CONCLUSIONS: The methods presented in this study facilitate fast and accurate fully automated classification of monogeneans at the species level. In future studies more classes will be included in the model, the time to capture the monogenean images will be reduced and improvements in extraction and selection of features will be implemented.

    Matched MeSH terms: Pattern Recognition, Automated/methods*
  18. Fulltext A new blood vessel extraction technique using edge enhancement and object classification
    Badsha S, Reza AW, Tan KG, Dimyati K
    J Digit Imaging, 2013 Dec;26(6):1107-15.
    PMID: 23515843 DOI: 10.1007/s10278-013-9585-8
    Diabetic retinopathy (DR) is increasing progressively pushing the demand of automatic extraction and classification of severity of diseases. Blood vessel extraction from the fundus image is a vital and challenging task. Therefore, this paper presents a new, computationally simple, and automatic method to extract the retinal blood vessel. The proposed method comprises several basic image processing techniques, namely edge enhancement by standard template, noise removal, thresholding, morphological operation, and object classification. The proposed method has been tested on a set of retinal images. The retinal images were collected from the DRIVE database and we have employed robust performance analysis to evaluate the accuracy. The results obtained from this study reveal that the proposed method offers an average accuracy of about 97 %, sensitivity of 99 %, specificity of 86 %, and predictive value of 98 %, which is superior to various well-known techniques.
    Matched MeSH terms: Pattern Recognition, Automated/methods*
  19. Glove-based approach to online signature verification
    Kamel NS, Sayeed S, Ellis GA
    IEEE Trans Pattern Anal Mach Intell, 2008 Jun;30(6):1109-13.
    PMID: 18421114 DOI: 10.1109/TPAMI.2008.32
    Utilizing the multiple degrees of freedom offered by the data glove for each finger and the hand, a novel on-line signature verification system using the Singular Value Decomposition (SVD) numerical tool for signature classification and verification is presented. The proposed technique is based on the Singular Value Decomposition in finding r singular vectors sensing the maximal energy of glove data matrix A, called principal subspace, so the effective dimensionality of A can be reduced. Having modeled the data glove signature through its r-principal subspace, signature authentication is performed by finding the angles between the different subspaces. A demonstration of the data glove is presented as an effective high-bandwidth data entry device for signature verification. This SVD-based signature verification technique is tested and its performance is shown to be able to recognize forgery signatures with a false acceptance rate of less than 1.2%.
    Matched MeSH terms: Pattern Recognition, Automated/methods*
  20. An efficient algorithm for retinal blood vessel segmentation using h-maxima transform and multilevel thresholding
    Saleh MD, Eswaran C
    Comput Methods Biomech Biomed Engin, 2012;15(5):517-25.
    PMID: 21331960 DOI: 10.1080/10255842.2010.545949
    Retinal blood vessel detection and analysis play vital roles in early diagnosis and prevention of several diseases, such as hypertension, diabetes, arteriosclerosis, cardiovascular disease and stroke. This paper presents an automated algorithm for retinal blood vessel segmentation. The proposed algorithm takes advantage of powerful image processing techniques such as contrast enhancement, filtration and thresholding for more efficient segmentation. To evaluate the performance of the proposed algorithm, experiments were conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm yields an accuracy rate of 96.5%, which is higher than the results achieved by other known algorithms.
    Matched MeSH terms: Pattern Recognition, Automated/methods
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