Displaying publications 61 - 80 of 133 in total

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  1. A state-of-the-art review on badminton lunge attributes
    Lee JJJ, Loh WP
    Comput Biol Med, 2019 05;108:213-222.
    PMID: 31005013 DOI: 10.1016/j.compbiomed.2019.04.003
    Good badminton lunge skills have been quantitatively described using biomechanical attributes at both static and dynamic phases. The measurement of badminton lunge attributes has often been complicated by various experimental protocols used. No review article has considered or critically reviewed the attributes that align with badminton lunge performance. This paper, hence, presents a review of badminton lunge postures governed by various determinant attributes. This review was performed by involving a number of relevant search engines. A total of 21 articles that fulfilled the predefined inclusion criteria were analysed. The lunge determinant attributes, such as time, lunge distance, plantar, ground reaction force, joint, dynamic balance and muscle attributes, had been examined. Contradictory findings in the dynamic balance attributes, specifically the relative displacement between the centre of mass and the centre of pressure, are presented in this paper. The findings showed that time, lunge distance and ground reaction force determined lunge performance. On the other hand, plantar, joint, dynamic balance and muscle attributes appeared useful in minimising injuries to ensure efficient lunge performance.
  2. A visual probe localization and calibration system for cost-effective computer-aided 3D ultrasound
    Ali A, Logeswaran R
    Comput Biol Med, 2007 Aug;37(8):1141-7.
    PMID: 17126314
    The 3D ultrasound systems produce much better reproductions than 2D ultrasound, but their prohibitively high cost deprives many less affluent organization this benefit. This paper proposes using the conventional 2D ultrasound equipment readily available in most hospitals, along with a single conventional digital camera, to construct 3D ultrasound images. The proposed system applies computer vision to extract position information of the ultrasound probe while the scanning takes place. The probe, calibrated in order to calculate the offset of the ultrasound scan from the position of the marker attached to it, is used to scan a number of geometrical objects. Using the proposed system, the 3D volumes of the objects were successfully reconstructed. The system was tested in clinical situations where human body parts were scanned. The results presented, and confirmed by medical staff, are very encouraging for cost-effective implementation of computer-aided 3D ultrasound using a simple setup with 2D ultrasound equipment and a conventional digital camera.
  3. Designing of 2,3-dihydrobenzofuran derivatives as inhibitors of PDE1B using pharmacophore screening, ensemble docking and molecular dynamics approach
    Al-Nema M, Gaurav A, Lee VS
    Comput Biol Med, 2023 Apr 03;159:106869.
    PMID: 37071939 DOI: 10.1016/j.compbiomed.2023.106869
    In recent years, the PDE1B enzyme has become a desirable drug target for the treatment of psychological and neurological disorders, particularly schizophrenia disorder, due to the expression of PDE1B in brain regions involved in volitional behaviour, learning and memory. Although several inhibitors of PDE1 have been identified using different methods, none of these inhibitors has reached the market yet. Thus, searching for novel PDE1B inhibitors is considered a major scientific challenge. In this study, pharmacophore-based screening, ensemble docking and molecular dynamics simulations have been performed to identify a lead inhibitor of PDE1B with a new chemical scaffold. Five PDE1B crystal structures have been utilised in the docking study to improve the possibility of identifying an active compound compared to the use of a single crystal structure. Finally, the structure-activity- relationship was studied, and the structure of the lead molecule was modified to design novel inhibitors with a high affinity for PDE1B. As a result, two novel compounds have been designed that exhibited a higher affinity to PDE1B compared to the lead compound and the other designed compounds.
  4. Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification
    Algamal ZY, Lee MH
    Comput Biol Med, 2015 Dec 1;67:136-45.
    PMID: 26520484 DOI: 10.1016/j.compbiomed.2015.10.008
    Cancer classification and gene selection in high-dimensional data have been popular research topics in genetics and molecular biology. Recently, adaptive regularized logistic regression using the elastic net regularization, which is called the adaptive elastic net, has been successfully applied in high-dimensional cancer classification to tackle both estimating the gene coefficients and performing gene selection simultaneously. The adaptive elastic net originally used elastic net estimates as the initial weight, however, using this weight may not be preferable for certain reasons: First, the elastic net estimator is biased in selecting genes. Second, it does not perform well when the pairwise correlations between variables are not high. Adjusted adaptive regularized logistic regression (AAElastic) is proposed to address these issues and encourage grouping effects simultaneously. The real data results indicate that AAElastic is significantly consistent in selecting genes compared to the other three competitor regularization methods. Additionally, the classification performance of AAElastic is comparable to the adaptive elastic net and better than other regularization methods. Thus, we can conclude that AAElastic is a reliable adaptive regularized logistic regression method in the field of high-dimensional cancer classification.
  5. Automated screening system for retinal health using bi-dimensional empirical mode decomposition and integrated index
    Acharya UR, Mookiah MR, Koh JE, Tan JH, Bhandary SV, Rao AK, et al.
    Comput Biol Med, 2016 08 01;75:54-62.
    PMID: 27253617 DOI: 10.1016/j.compbiomed.2016.04.015
    Posterior Segment Eye Diseases (PSED) namely Diabetic Retinopathy (DR), glaucoma and Age-related Macular Degeneration (AMD) are the prime causes of vision loss globally. Vision loss can be prevented, if these diseases are detected at an early stage. Structural abnormalities such as changes in cup-to-disc ratio, Hard Exudates (HE), drusen, Microaneurysms (MA), Cotton Wool Spots (CWS), Haemorrhages (HA), Geographic Atrophy (GA) and Choroidal Neovascularization (CNV) in PSED can be identified by manual examination of fundus images by clinicians. However, manual screening is labour-intensive, tiresome and time consuming. Hence, there is a need to automate the eye screening. In this work Bi-dimensional Empirical Mode Decomposition (BEMD) technique is used to decompose fundus images into 2D Intrinsic Mode Functions (IMFs) to capture variations in the pixels due to morphological changes. Further, various entropy namely Renyi, Fuzzy, Shannon, Vajda, Kapur and Yager and energy features are extracted from IMFs. These extracted features are ranked using Chernoff Bound and Bhattacharyya Distance (CBBD), Kullback-Leibler Divergence (KLD), Fuzzy-minimum Redundancy Maximum Relevance (FmRMR), Wilcoxon, Receiver Operating Characteristics Curve (ROC) and t-test methods. Further, these ranked features are fed to Support Vector Machine (SVM) classifier to classify normal and abnormal (DR, AMD and glaucoma) classes. The performance of the proposed eye screening system is evaluated using 800 (Normal=400 and Abnormal=400) digital fundus images and 10-fold cross validation method. Our proposed system automatically identifies normal and abnormal classes with an average accuracy of 88.63%, sensitivity of 86.25% and specificity of 91% using 17 optimal features ranked using CBBD and SVM-Radial Basis Function (RBF) classifier. Moreover, a novel Retinal Risk Index (RRI) is developed using two significant features to distinguish two classes using single number. Such a system helps to reduce eye screening time in polyclinics or community-based mass screening. They will refer the patients to main hospitals only if the diagnosis belong to the abnormal class. Hence, the main hospitals will not be unnecessarily crowded and doctors can devote their time for other urgent cases.
  6. Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images
    Mookiah MR, Acharya UR, Koh JE, Chandran V, Chua CK, Tan JH, et al.
    Comput Biol Med, 2014 Oct;53:55-64.
    PMID: 25127409 DOI: 10.1016/j.compbiomed.2014.07.015
    Age-related Macular Degeneration (AMD) is one of the major causes of vision loss and blindness in ageing population. Currently, there is no cure for AMD, however early detection and subsequent treatment may prevent the severe vision loss or slow the progression of the disease. AMD can be classified into two types: dry and wet AMDs. The people with macular degeneration are mostly affected by dry AMD. Early symptoms of AMD are formation of drusen and yellow pigmentation. These lesions are identified by manual inspection of fundus images by the ophthalmologists. It is a time consuming, tiresome process, and hence an automated diagnosis of AMD screening tool can aid clinicians in their diagnosis significantly. This study proposes an automated dry AMD detection system using various entropies (Shannon, Kapur, Renyi and Yager), Higher Order Spectra (HOS) bispectra features, Fractional Dimension (FD), and Gabor wavelet features extracted from greyscale fundus images. The features are ranked using t-test, Kullback-Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance (CBBD), Receiver Operating Characteristics (ROC) curve-based and Wilcoxon ranking methods in order to select optimum features and classified into normal and AMD classes using Naive Bayes (NB), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Decision Tree (DT) and Support Vector Machine (SVM) classifiers. The performance of the proposed system is evaluated using private (Kasturba Medical Hospital, Manipal, India), Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) datasets. The proposed system yielded the highest average classification accuracies of 90.19%, 95.07% and 95% with 42, 54 and 38 optimal ranked features using SVM classifier for private, ARIA and STARE datasets respectively. This automated AMD detection system can be used for mass fundus image screening and aid clinicians by making better use of their expertise on selected images that require further examination.
  7. Novel risk index for the identification of age-related macular degeneration using radon transform and DWT features
    Acharya UR, Mookiah MR, Koh JE, Tan JH, Noronha K, Bhandary SV, et al.
    Comput Biol Med, 2016 06 01;73:131-40.
    PMID: 27107676 DOI: 10.1016/j.compbiomed.2016.04.009
    Age-related Macular Degeneration (AMD) affects the central vision of aged people. It can be diagnosed due to the presence of drusen, Geographic Atrophy (GA) and Choroidal Neovascularization (CNV) in the fundus images. It is labor intensive and time-consuming for the ophthalmologists to screen these images. An automated digital fundus photography based screening system can overcome these drawbacks. Such a safe, non-contact and cost-effective platform can be used as a screening system for dry AMD. In this paper, we are proposing a novel algorithm using Radon Transform (RT), Discrete Wavelet Transform (DWT) coupled with Locality Sensitive Discriminant Analysis (LSDA) for automated diagnosis of AMD. First the image is subjected to RT followed by DWT. The extracted features are subjected to dimension reduction using LSDA and ranked using t-test. The performance of various supervised classifiers namely Decision Tree (DT), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN) are compared to automatically discriminate to normal and AMD classes using ranked LSDA components. The proposed approach is evaluated using private and public datasets such as ARIA and STARE. The highest classification accuracy of 99.49%, 96.89% and 100% are reported for private, ARIA and STARE datasets. Also, AMD index is devised using two LSDA components to distinguish two classes accurately. Hence, this proposed system can be extended for mass AMD screening.
  8. Automated diabetic macular edema (DME) grading system using DWT, DCT Features and maculopathy index
    Acharya UR, Mookiah MRK, Koh JEW, Tan JH, Bhandary SV, Rao AK, et al.
    Comput Biol Med, 2017 05 01;84:59-68.
    PMID: 28343061 DOI: 10.1016/j.compbiomed.2017.03.016
    The cause of diabetic macular edema (DME) is due to prolonged and uncontrolled diabetes mellitus (DM) which affects the vision of diabetic subjects. DME is graded based on the exudate location from the macula. It is clinically diagnosed using fundus images which is tedious and time-consuming. Regular eye screening and subsequent treatment may prevent the vision loss. Hence, in this work, a hybrid system based on Radon transform (RT), discrete wavelet transform (DWT) and discrete cosine transform (DCT) are proposed for an automated detection of DME. The fundus images are subjected to RT to obtain sinograms and DWT is applied on these sinograms to extract wavelet coefficients (approximate, horizontal, vertical and diagonal). DCT is applied on approximate coefficients to obtain 2D-DCT coefficients. Further, these coefficients are converted into 1D vector by arranging the coefficients in zig-zag manner. This 1D signal is subjected to locality sensitive discriminant analysis (LSDA). Finally, various supervised classifiers are used to classify the three classes using significant features. Our proposed technique yielded a classification accuracy of 100% and 97.01% using two and seven significant features for private and public (MESSIDOR) databases respectively. Also, a maculopathy index is formulated with two significant parameters to discriminate the three groups distinctly using a single integer. Hence, our obtained results suggest that this system can be used as an eye screening tool for diabetic subjects for DME.
  9. Fully automated subchondral bone segmentation from knee MR images: Data from the Osteoarthritis Initiative
    Gandhamal A, Talbar S, Gajre S, Razak R, Hani AFM, Kumar D
    Comput Biol Med, 2017 Sep 01;88:110-125.
    PMID: 28711767 DOI: 10.1016/j.compbiomed.2017.07.008
    Knee osteoarthritis (OA) progression can be monitored by measuring changes in the subchondral bone structure such as area and shape from MR images as an imaging biomarker. However, measurements of these minute changes are highly dependent on the accurate segmentation of bone tissue from MR images and it is challenging task due to the complex tissue structure and inadequate image contrast/brightness. In this paper, a fully automated method for segmenting subchondral bone from knee MR images is proposed. Here, the contrast of knee MR images is enhanced using a gray-level S-curve transformation followed by automatic seed point detection using a three-dimensional multi-edge overlapping technique. Successively, bone regions are initially extracted using distance-regularized level-set evolution followed by identification and correction of leakages along the bone boundary regions using a boundary displacement technique. The performance of the developed technique is evaluated against ground truths by measuring sensitivity, specificity, dice similarity coefficient (DSC), average surface distance (AvgD) and root mean square surface distance (RMSD). An average sensitivity (91.14%), specificity (99.12%) and DSC (90.28%) with 95% confidence interval (CI) in the range 89.74-92.54%, 98.93-99.31% and 88.68-91.88% respectively is achieved for the femur bone segmentation in 8 datasets. For tibia bone, average sensitivity (90.69%), specificity (99.65%) and DSC (91.35%) with 95% CI in the range 88.59-92.79%, 99.50-99.80% and 88.68-91.88% respectively is achieved. AvgD and RMSD values for femur are 1.43 ± 0.23 (mm) and 2.10 ± 0.35 (mm) respectively while for tibia, the values are 0.95 ± 0.28 (mm) and 1.30 ± 0.42 (mm) respectively that demonstrates acceptable error between proposed method and ground truths. In conclusion, results obtained in this work demonstrate substantially significant performance with consistency and robustness that led the proposed method to be applicable for large scale and longitudinal knee OA studies in clinical settings.
  10. Local gray level S-curve transformation - A generalized contrast enhancement technique for medical images
    Gandhamal A, Talbar S, Gajre S, Hani AF, Kumar D
    Comput Biol Med, 2017 04 01;83:120-133.
    PMID: 28279861 DOI: 10.1016/j.compbiomed.2017.03.001
    Most medical images suffer from inadequate contrast and brightness, which leads to blurred or weak edges (low contrast) between adjacent tissues resulting in poor segmentation and errors in classification of tissues. Thus, contrast enhancement to improve visual information is extremely important in the development of computational approaches for obtaining quantitative measurements from medical images. In this research, a contrast enhancement algorithm that applies gray-level S-curve transformation technique locally in medical images obtained from various modalities is investigated. The S-curve transformation is an extended gray level transformation technique that results into a curve similar to a sigmoid function through a pixel to pixel transformation. This curve essentially increases the difference between minimum and maximum gray values and the image gradient, locally thereby, strengthening edges between adjacent tissues. The performance of the proposed technique is determined by measuring several parameters namely, edge content (improvement in image gradient), enhancement measure (degree of contrast enhancement), absolute mean brightness error (luminance distortion caused by the enhancement), and feature similarity index measure (preservation of the original image features). Based on medical image datasets comprising 1937 images from various modalities such as ultrasound, mammograms, fluorescent images, fundus, X-ray radiographs and MR images, it is found that the local gray-level S-curve transformation outperforms existing techniques in terms of improved contrast and brightness, resulting in clear and strong edges between adjacent tissues. The proposed technique can be used as a preprocessing tool for effective segmentation and classification of tissue structures in medical images.
  11. A deep CNN model for anomaly detection and localization in wireless capsule endoscopy images
    Jain S, Seal A, Ojha A, Yazidi A, Bures J, Tacheci I, et al.
    Comput Biol Med, 2021 10;137:104789.
    PMID: 34455302 DOI: 10.1016/j.compbiomed.2021.104789
    Wireless capsule endoscopy (WCE) is one of the most efficient methods for the examination of gastrointestinal tracts. Computer-aided intelligent diagnostic tools alleviate the challenges faced during manual inspection of long WCE videos. Several approaches have been proposed in the literature for the automatic detection and localization of anomalies in WCE images. Some of them focus on specific anomalies such as bleeding, polyp, lesion, etc. However, relatively fewer generic methods have been proposed to detect all those common anomalies simultaneously. In this paper, a deep convolutional neural network (CNN) based model 'WCENet' is proposed for anomaly detection and localization in WCE images. The model works in two phases. In the first phase, a simple and efficient attention-based CNN classifies an image into one of the four categories: polyp, vascular, inflammatory, or normal. If the image is classified in one of the abnormal categories, it is processed in the second phase for the anomaly localization. Fusion of Grad-CAM++ and a custom SegNet is used for anomalous region segmentation in the abnormal image. WCENet classifier attains accuracy and area under receiver operating characteristic of 98% and 99%. The WCENet segmentation model obtains a frequency weighted intersection over union of 81%, and an average dice score of 56% on the KID dataset. WCENet outperforms nine different state-of-the-art conventional machine learning and deep learning models on the KID dataset. The proposed model demonstrates potential for clinical applications.
  12. A knowledge interaction learning for multi-echo MRI motion artifact correction towards better enhancement of SWI
    Al-Masni MA, Lee S, Al-Shamiri AK, Gho SM, Choi YH, Kim DH
    Comput Biol Med, 2023 Feb;153:106553.
    PMID: 36641933 DOI: 10.1016/j.compbiomed.2023.106553
    Patient movement during Magnetic Resonance Imaging (MRI) scan can cause severe degradation of image quality. In Susceptibility Weighted Imaging (SWI), several echoes are typically measured during a single repetition period, where the earliest echoes show less contrast between various tissues, while the later echoes are more susceptible to artifacts and signal dropout. In this paper, we propose a knowledge interaction paradigm that jointly learns feature details from multiple distorted echoes by sharing their knowledge with unified training parameters, thereby simultaneously reducing motion artifacts of all echoes. This is accomplished by developing a new scheme that boosts a Single Encoder with Multiple Decoders (SEMD), which assures that the generated features not only get fused but also learned together. We called the proposed method Knowledge Interaction Learning between Multi-Echo data (KIL-ME-based SEMD). The proposed KIL-ME-based SEMD allows to share information and gain an understanding of the correlations between the multiple echoes. The main purpose of this work is to correct the motion artifacts and maintain image quality and structure details of all motion-corrupted echoes towards generating high-resolution susceptibility enhanced contrast images, i.e., SWI, using a weighted average of multi-echo motion-corrected acquisitions. We also compare various potential strategies that might be used to address the problem of reducing artifacts in multi-echoes data. The experimental results demonstrate the feasibility and effectiveness of the proposed method, reducing the severity of motion artifacts and improving the overall clinical image quality of all echoes with their associated SWI maps. Significant improvement of image quality is observed using both motion-simulated test data and actual volunteer data with various motion severity strengths. Eventually, by enhancing the overall image quality, the proposed network can increase the effectiveness of the physicians' capability to evaluate and correctly diagnose brain MR images.
  13. Robust biometric system using session invariant multimodal EEG and keystroke dynamics by the ensemble of self-ONNs
    Rahman A, Chowdhury MEH, Khandakar A, Tahir AM, Ibtehaz N, Hossain MS, et al.
    Comput Biol Med, 2022 Mar;142:105238.
    PMID: 35077938 DOI: 10.1016/j.compbiomed.2022.105238
    Harnessing the inherent anti-spoofing quality from electroencephalogram (EEG) signals has become a potential field of research in recent years. Although several studies have been conducted, still there are some vital challenges present in the deployment of EEG-based biometrics, which is stable and capable of handling the real-world scenario. One of the key challenges is the large signal variability of EEG when recorded on different days or sessions which impedes the performance of biometric systems significantly. To address this issue, a session invariant multimodal Self-organized Operational Neural Network (Self-ONN) based ensemble model combining EEG and keystroke dynamics is proposed in this paper. Our model is tested successfully on a large number of sessions (10 recording days) with many challenging noisy and variable environments for the identification and authentication tasks. In most of the previous studies, training and testing were performed either over a single recording session (same day) only or without ensuring appropriate splitting of the data on multiple recording days. Unlike those studies, in our work, we have rigorously split the data so that train and test sets do not share the data of the same recording day. The proposed multimodal Self-ONN based ensemble model has achieved identification accuracy of 98% in rigorous validation cases and outperformed the equivalent ensemble of deep CNN models. A novel Self-ONN Siamese network has also been proposed to measure the similarity of templates during the authentication task instead of the commonly used simple distance measure techniques. The multimodal Siamese network reduces the Equal Error Rate (EER) to 1.56% in rigorous authentication. The obtained results indicate that the proposed multimodal Self-ONN model can automatically extract session invariant unique non-linear features to identify and authenticate users with high accuracy.
  14. Numerical investigation of treated brain glioma model using a two-stage successive over-relaxation method
    Hussain A, Muthuvalu MS, Faye I, Zafar M, Inc M, Afzal F, et al.
    Comput Biol Med, 2023 Feb;153:106429.
    PMID: 36587570 DOI: 10.1016/j.compbiomed.2022.106429
    A brain tumor is a dynamic system in which cells develop rapidly and abnormally, as is the case with most cancers. Cancer develops in the brain or inside the skull when aberrant and odd cells proliferate in the brain. By depriving the healthy cells of leisure, nutrition, and oxygen, these aberrant cells eventually cause the healthy cells to perish. This article investigated the development of glioma cells in treating brain tumors. Mathematically, reaction-diffusion models have been developed for brain glioma growth to quantify the diffusion and proliferation of the tumor cells within brain tissues. This study presents the formulation the two-stage successive over-relaxation (TSSOR) algorithm based on the finite difference approximation for solving the treated brain glioma model to predict glioma cells in treating the brain tumor. Also, the performance of TSSOR method is compared to the Gauss-Seidel (GS) and two-stage Gauss-Seidel (TSGS) methods in terms of the number of iterations, the amount of time it takes to process the data, and the rate at which glioma cells grow the fastest. The implementation of the TSSOR, TSGS, and GS methods predicts the growth of tumor cells under the treatment protocol. The results show that the number of glioma cells decreased initially and then increased gradually by the next day. The computational complexity analysis is also used and concludes that the TSSOR method is faster compared to the TSGS and GS methods. According to the results of the treated glioma development model, the TSSOR approach reduced the number of iterations by between 8.0 and 71.95%. In terms of computational time, the TSSOR approach is around 1.18-76.34% faster than the TSGS and GS methods.
  15. Optimal control strategies for SGLT2 inhibitors as a novel anti-tumor agent and their effect on human breast cancer cells with the effect of time delay and hyperglycemia
    Alblowy AH, Maan N, Ibrahim AA
    Comput Biol Med, 2023 Oct 05;166:107552.
    PMID: 37826954 DOI: 10.1016/j.compbiomed.2023.107552
    Breast cancer is the most frequent cancer in the world, and it continues to have a significant impact on the total number of cancer deaths. Recently, oncology findings hint at the role of excessive glucose in cancer progression and immune cells' suppression. Sequel to this revelation is ongoing researches on possible inhibition of glucose flow into the tumor micro-environment as therapeutics for malignant treatment. In this study, the effect of glucose blockage therapeutics such as SGLT-2 inhibitors drug on the dynamics of normal, tumors and immune cells interaction is mathematically studied. The asymptomatic nature of the breast cancer is factored into the model using time delay. We first investigate the boundedness and non-negativity of the solution. The condition for existence of critical equilibrium point is determined, and its global stability conditions are derived using Lyapunov function. This revealed that a timely administration of the SGLT-2 inhibitors drug can eliminate tumor cells. Secondly, we determine the sufficient and necessary conditions for optimal control strategy of SGLT-2 inhibitors so as to avert side effects on normal cells using a Pontryagin's Minimum Principle. The results showed that if the ingestion rate of the inhibitor drug is equal to the digestion rate, the tumor cells can be completely eliminated within 9 months without side effects. The analytical results were numerically verified and the qualitative views of interacting cells dynamics is showcased.
  16. 3D surface roughness measurement for scaliness scoring of psoriasis lesions
    Ahmad Fadzil MH, Prakasa E, Asirvadam VS, Nugroho H, Affandi AM, Hussein SH
    Comput Biol Med, 2013 Nov;43(11):1987-2000.
    PMID: 24054912 DOI: 10.1016/j.compbiomed.2013.08.009
    Psoriasis is an incurable skin disorder affecting 2-3% of the world population. The scaliness of psoriasis is a key assessment parameter of the Psoriasis Area and Severity Index (PASI). Dermatologists typically use visual and tactile senses in PASI scaliness assessment. However, the assessment can be subjective resulting in inter- and intra-rater variability in the scores. This paper proposes an assessment method that incorporates 3D surface roughness with standard clustering techniques to objectively determine the PASI scaliness score for psoriasis lesions. A surface roughness algorithm using structured light projection has been applied to 1999 3D psoriasis lesion surfaces. The algorithm has been validated with an accuracy of 94.12%. Clustering algorithms were used to classify the surface roughness measured using the proposed assessment method for PASI scaliness scoring. The reliability of the developed PASI scaliness algorithm was high with kappa coefficients>0.84 (almost perfect agreement).
  17. Effect of miscentering and low-dose protocols on contrast resolution in computed tomography head examination
    Shaffiq Said Rahmat SM, Abdul Karim MK, Che Isa IN, Abd Rahman MA, Noor NM, Hoong NK
    Comput Biol Med, 2020 08;123:103840.
    PMID: 32658782 DOI: 10.1016/j.compbiomed.2020.103840
    BACKGROUND: Unoptimized protocols, including a miscentered position, might affect the outcome of diagnostic in CT examinations. In this study, we investigate the effects of miscentering position during CT head examination on the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR).

    METHOD: We simulate the CT head examination using a water phantom with a standard protocol (120 kVp/180 mAs) and a low dose protocol (100 kVp/142 mAs). The table height was adjusted to simulate miscentering by 5 cm from the isocenter, where the height was miscentered superiorly (MCS) at 109, 114, 119, and 124 cm, and miscentered inferiorly (MCI) at 99, 94, 89, and 84 cm. Seven circular regions of interest were used, with one drawn at the center, four at the peripheral area of the phantom, and two at the background area of the image.

    RESULTS: For the standard protocol, the mean CNR decreased uniformly as table height increased and significantly differed (p 

  18. Accurate wavelet thresholding method for ECG signals
    Yu K, Feng L, Chen Y, Wu M, Zhang Y, Zhu P, et al.
    Comput Biol Med, 2024 Feb;169:107835.
    PMID: 38096762 DOI: 10.1016/j.compbiomed.2023.107835
    Current wavelet thresholding methods for cardiogram signals captured by flexible wearable sensors face a challenge in achieving both accurate thresholding and real-time signal denoising. This paper proposes a real-time accurate thresholding method based on signal estimation, specifically the normalized ACF, as an alternative to traditional noise estimation without the need for parameter fine-tuning and extensive data training. This method is experimentally validated using a variety of electrocardiogram (ECG) signals from different databases, each containing specific types of noise such as additive white Gaussian (AWG) noise, baseline wander noise, electrode motion noise, and muscle artifact noise. Although this method only slightly outperforms other methods in removing AWG noise in ECG signals, it far outperforms conventional methods in removing other real noise. This is attributed to the method's ability to accurately distinguish not only AWG noise that is significantly different spectrum of the ECG signal, but also real noise with similar spectra. In contrast, the conventional methods are effective only for AWG noise. In additional, this method improves the denoising visualization of the measured ECG signals and can be used to optimize other parameters of other wavelet methods to enhancing the denoised periodic signals, thereby improving diagnostic accuracy.
  19. Utilizing hybrid functional fuzzy wavelet neural networks with a teaching learning-based optimization algorithm for medical disease diagnosis
    Majeed Alneamy JS, A Hameed Alnaish Z, Mohd Hashim SZ, Hamed Alnaish RA
    Comput Biol Med, 2019 09;112:103348.
    PMID: 31356992 DOI: 10.1016/j.compbiomed.2019.103348
    Accurate medical disease diagnosis is considered to be an important classification problem. The main goal of the classification process is to determine the class to which a certain pattern belongs. In this article, a new classification technique based on a combination of The Teaching Learning-Based Optimization (TLBO) algorithm and Fuzzy Wavelet Neural Network (FWNN) with Functional Link Neural Network (FLNN) is proposed. In addition, the TLBO algorithm is utilized for training the new hybrid Functional Fuzzy Wavelet Neural Network (FFWNN) and optimizing the learning parameters, which are weights, dilation and translation. To evaluate the performance of the proposed method, five standard medical datasets were used: Breast Cancer, Heart Disease, Hepatitis, Pima-Indian diabetes and Appendicitis. The efficiency of the proposed method is evaluated using 5-fold cross-validation and 10-fold cross-validation in terms of mean square error (MSE), classification accuracy, running time, sensitivity, specificity and kappa. The experimental results show that the efficiency of the proposed method for the medical classification problems is 98.309%, 91.1%, 91.39%, 88.67% and 93.51% for the Breast Cancer, Heart Disease, Hepatitis, Pima-Indian diabetes and Appendicitis datasets, respectively, in terms of accuracy after 30 runs for each dataset with low computational complexity. In addition, it has been observed that the proposed method has efficient performance compared with the performance of other methods found in the related previous studies.
  20. Computerized segmentation and measurement of chronic wound images
    Ahmad Fauzi MF, Khansa I, Catignani K, Gordillo G, Sen CK, Gurcan MN
    Comput Biol Med, 2015 May;60:74-85.
    PMID: 25756704 DOI: 10.1016/j.compbiomed.2015.02.015
    An estimated 6.5 million patients in the United States are affected by chronic wounds, with more than US$25 billion and countless hours spent annually for all aspects of chronic wound care. There is a need for an intelligent software tool to analyze wound images, characterize wound tissue composition, measure wound size, and monitor changes in wound in between visits. Performed manually, this process is very time-consuming and subject to intra- and inter-reader variability. In this work, our objective is to develop methods to segment, measure and characterize clinically presented chronic wounds from photographic images. The first step of our method is to generate a Red-Yellow-Black-White (RYKW) probability map, which then guides the segmentation process using either optimal thresholding or region growing. The red, yellow and black probability maps are designed to handle the granulation, slough and eschar tissues, respectively; while the white probability map is to detect the white label card for measurement calibration purposes. The innovative aspects of this work include defining a four-dimensional probability map specific to wound characteristics, a computationally efficient method to segment wound images utilizing the probability map, and auto-calibration of wound measurements using the content of the image. These methods were applied to 80 wound images, captured in a clinical setting at the Ohio State University Comprehensive Wound Center, with the ground truth independently generated by the consensus of at least two clinicians. While the mean inter-reader agreement between the readers varied between 67.4% and 84.3%, the computer achieved an average accuracy of 75.1%.
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