Displaying publications 81 - 100 of 247 in total

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  1. A review of common beam hardening correction methods for industrial x-ray computed tomography
    Ahmed O, Yushou Song
    Sains Malaysiana, 2018;47:1883-1890.
    X-ray computed tomography (XCT) became an important instrument for quality assurance in industry products as a
    non-destructive testing tool for inspection, evaluation, analysis and dimensional metrology. Thus, a high-quality image
    is required. Due to the polychromatic nature of X-ray energy in XCT, this leads to errors in attenuation coefficient
    which is generally known as beam hardening artifact. This leads to a distortion or blurring-like cupping and streak in
    the reconstruction images, where a significant decrease in imaging quality is observed. In this paper, recent research
    publications regarding common practical correction methods that were adopted to improve an imaging quality have been
    discussed. It was observed from the discussion and evaluation, that a problem behind beam hardening reduction for the
    multi-materials object, especially in the absence of prior information about X-ray spectrum and material characterizations
    would be a significant research contribution, if the correction could be achieved without the need to perform forward
    projections and multiple reconstructions.
    Matched MeSH terms: Image Processing, Computer-Assisted
  2. Fulltext Entropy and Gaussian Filter-Based Adaptive Active Contour for Segmentation of Skin Lesions
    Mustafa S, Iqbal MW, Rana TA, Jaffar A, Shiraz M, Arif M, et al.
    Comput Intell Neurosci, 2022;2022:4348235.
    PMID: 35909861 DOI: 10.1155/2022/4348235
    Malignant melanoma is considered one of the deadliest skin diseases if ignored without treatment. The mortality rate caused by melanoma is more than two times that of other skin malignancy diseases. These facts encourage computer scientists to find automated methods to discover skin cancers. Nowadays, the analysis of skin images is widely used by assistant physicians to discover the first stage of the disease automatically. One of the challenges the computer science researchers faced when developing such a system is the un-clarity of the existing images, such as noise like shadows, low contrast, hairs, and specular reflections, which complicates detecting the skin lesions in that images. This paper proposes the solution to the problem mentioned earlier using the active contour method. Still, seed selection in the dynamic contour method has the main drawback of where it should start the segmentation process. This paper uses Gaussian filter-based maximum entropy and morphological processing methods to find automatic seed points for active contour. By incorporating this, it can segment the lesion from dermoscopic images automatically. Our proposed methodology tested quantitative and qualitative measures on standard dataset dermis and used to test the proposed method's reliability which shows encouraging results.
    Matched MeSH terms: Image Processing, Computer-Assisted
  3. Fulltext A chaotic cryptosystem for images based on Henon and Arnold cat map
    Soleymani A, Nordin MJ, Sundararajan E
    ScientificWorldJournal, 2014;2014:536930.
    PMID: 25258724 DOI: 10.1155/2014/536930
    The rapid evolution of imaging and communication technologies has transformed images into a widespread data type. Different types of data, such as personal medical information, official correspondence, or governmental and military documents, are saved and transmitted in the form of images over public networks. Hence, a fast and secure cryptosystem is needed for high-resolution images. In this paper, a novel encryption scheme is presented for securing images based on Arnold cat and Henon chaotic maps. The scheme uses Arnold cat map for bit- and pixel-level permutations on plain and secret images, while Henon map creates secret images and specific parameters for the permutations. Both the encryption and decryption processes are explained, formulated, and graphically presented. The results of security analysis of five different images demonstrate the strength of the proposed cryptosystem against statistical, brute force and differential attacks. The evaluated running time for both encryption and decryption processes guarantee that the cryptosystem can work effectively in real-time applications.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*; Image Processing, Computer-Assisted/standards
  4. Single image signal-to-noise ratio estimation for magnetic resonance images
    Sim KS, Lai MA, Tso CP, Teo CC
    J Med Syst, 2011 Feb;35(1):39-48.
    PMID: 20703587 DOI: 10.1007/s10916-009-9339-9
    A novel technique to quantify the signal-to-noise ratio (SNR) of magnetic resonance images is developed. The image SNR is quantified by estimating the amplitude of the signal spectrum using the autocorrelation function of just one single magnetic resonance image. To test the performance of the quantification, SNR measurement data are fitted to theoretically expected curves. It is shown that the technique can be implemented in a highly efficient way for the magnetic resonance imaging system.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*; Image Processing, Computer-Assisted/statistics & numerical data
  5. Fulltext Image reconstruction of a charge coupled device based optical tomographic instrumentation system for particle sizing
    Idroas M, Rahim RA, Green RG, Ibrahim MN, Rahiman MH
    Sensors (Basel), 2010;10(10):9512-28.
    PMID: 22163423 DOI: 10.3390/s101009512
    This research investigates the use of charge coupled device (abbreviated as CCD) linear image sensors in an optical tomographic instrumentation system used for sizing particles. The measurement system, consisting of four CCD linear image sensors are configured around an octagonal shaped flow pipe for a four projections system is explained. The four linear image sensors provide 2,048 pixel imaging with a pixel size of 14 micron × 14 micron, hence constituting a high-resolution system. Image reconstruction for a four-projection optical tomography system is also discussed, where a simple optical model is used to relate attenuation due to variations in optical density, [R], within the measurement section. Expressed in matrix form this represents the forward problem in tomography [S] [R] = [M]. In practice, measurements [M] are used to estimate the optical density distribution by solving the inverse problem [R] = [S](-1)[M]. Direct inversion of the sensitivity matrix, [S], is not possible and two approximations are considered and compared-the transpose and the pseudo inverse sensitivity matrices.
    Matched MeSH terms: Image Processing, Computer-Assisted/instrumentation*; Image Processing, Computer-Assisted/methods*
  6. Fulltext QuickCount®: a novel automated software for rapid cell detection and quantification
    Tiong KH, Chang JK, Pathmanathan D, Hidayatullah Fadlullah MZ, Yee PS, Liew CS, et al.
    Biotechniques, 2018 12;65(6):322-330.
    PMID: 30477327 DOI: 10.2144/btn-2018-0072
    We describe a novel automated cell detection and counting software, QuickCount® (QC), designed for rapid quantification of cells. The Bland-Altman plot and intraclass correlation coefficient (ICC) analyses demonstrated strong agreement between cell counts from QC to manual counts (mean and SD: -3.3 ± 4.5; ICC = 0.95). QC has higher recall in comparison to ImageJauto, CellProfiler and CellC and the precision of QC, ImageJauto, CellProfiler and CellC are high and comparable. QC can precisely delineate and count single cells from images of different cell densities with precision and recall above 0.9. QC is unique as it is equipped with real-time preview while optimizing the parameters for accurate cell count and needs minimum hands-on time where hundreds of images can be analyzed automatically in a matter of milliseconds. In conclusion, QC offers a rapid, accurate and versatile solution for large-scale cell quantification and addresses the challenges often faced in cell biology research.
    Matched MeSH terms: Image Processing, Computer-Assisted/economics; Image Processing, Computer-Assisted/methods*
  7. Fulltext Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer
    Mehdy MM, Ng PY, Shair EF, Saleh NIM, Gomes C
    Comput Math Methods Med, 2017;2017:2610628.
    PMID: 28473865 DOI: 10.1155/2017/2610628
    Medical imaging techniques have widely been in use in the diagnosis and detection of breast cancer. The drawback of applying these techniques is the large time consumption in the manual diagnosis of each image pattern by a professional radiologist. Automated classifiers could substantially upgrade the diagnosis process, in terms of both accuracy and time requirement by distinguishing benign and malignant patterns automatically. Neural network (NN) plays an important role in this respect, especially in the application of breast cancer detection. Despite the large number of publications that describe the utilization of NN in various medical techniques, only a few reviews are available that guide the development of these algorithms to enhance the detection techniques with respect to specificity and sensitivity. The purpose of this review is to analyze the contents of recently published literature with special attention to techniques and states of the art of NN in medical imaging. We discuss the usage of NN in four different medical imaging applications to show that NN is not restricted to few areas of medicine. Types of NN used, along with the various types of feeding data, have been reviewed. We also address hybrid NN adaptation in breast cancer detection.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*; Image Processing, Computer-Assisted/standards
  8. Real time mass flow rate measurement using multiple fan beam optical tomography
    Abdul Rahim R, Leong LC, Chan KS, Rahiman MH, Pang JF
    ISA Trans, 2008 Jan;47(1):3-14.
    PMID: 17709106
    This paper presents the implementing multiple fan beam projection technique using optical fibre sensors for a tomography system. From the dynamic experiment of solid/gas flow using plastic beads in a gravity flow rig, the designed optical fibre sensors are reliable in measuring the mass flow rate below 40% of flow. Another important matter that has been discussed is the image processing rate or IPR. Generally, the applied image reconstruction algorithms, the construction of the sensor and also the designed software are considered to be reliable and suitable to perform real-time image reconstruction and mass flow rate measurements.
    Matched MeSH terms: Image Processing, Computer-Assisted
  9. An evolution of image source camera attribution approaches
    Jahanirad M, Wahab AW, Anuar NB
    Forensic Sci Int, 2016 May;262:242-75.
    PMID: 27060542 DOI: 10.1016/j.forsciint.2016.03.035
    Camera attribution plays an important role in digital image forensics by providing the evidence and distinguishing characteristics of the origin of the digital image. It allows the forensic analyser to find the possible source camera which captured the image under investigation. However, in real-world applications, these approaches have faced many challenges due to the large set of multimedia data publicly available through photo sharing and social network sites, captured with uncontrolled conditions and undergone variety of hardware and software post-processing operations. Moreover, the legal system only accepts the forensic analysis of the digital image evidence if the applied camera attribution techniques are unbiased, reliable, nondestructive and widely accepted by the experts in the field. The aim of this paper is to investigate the evolutionary trend of image source camera attribution approaches from fundamental to practice, in particular, with the application of image processing and data mining techniques. Extracting implicit knowledge from images using intrinsic image artifacts for source camera attribution requires a structured image mining process. In this paper, we attempt to provide an introductory tutorial on the image processing pipeline, to determine the general classification of the features corresponding to different components for source camera attribution. The article also reviews techniques of the source camera attribution more comprehensively in the domain of the image forensics in conjunction with the presentation of classifying ongoing developments within the specified area. The classification of the existing source camera attribution approaches is presented based on the specific parameters, such as colour image processing pipeline, hardware- and software-related artifacts and the methods to extract such artifacts. The more recent source camera attribution approaches, which have not yet gained sufficient attention among image forensics researchers, are also critically analysed and further categorised into four different classes, namely, optical aberrations based, sensor camera fingerprints based, processing statistics based and processing regularities based, to present a classification. Furthermore, this paper aims to investigate the challenging problems, and the proposed strategies of such schemes based on the suggested taxonomy to plot an evolution of the source camera attribution approaches with respect to the subjective optimisation criteria over the last decade. The optimisation criteria were determined based on the strategies proposed to increase the detection accuracy, robustness and computational efficiency of source camera brand, model or device attribution.
    Matched MeSH terms: Image Processing, Computer-Assisted
  10. Denture microlabeling technique
    Ibrahim WM
    J Prosthet Dent, 1996 Jul;76(1):104.
    PMID: 8814640
    Matched MeSH terms: Image Processing, Computer-Assisted
  11. RERS-CC: Robotic facial recognition system for improving the accuracy of human face identification using HRI
    Jing W, Tao H, Rahman MA, Kabir MN, Yafeng L, Zhang R, et al.
    Work, 2021;68(3):923-934.
    PMID: 33612534 DOI: 10.3233/WOR-203426
    BACKGROUND: Human-Computer Interaction (HCI) is incorporated with a variety of applications for input processing and response actions. Facial recognition systems in workplaces and security systems help to improve the detection and classification of humans based on the vision experienced by the input system.

    OBJECTIVES: In this manuscript, the Robotic Facial Recognition System using the Compound Classifier (RERS-CC) is introduced to improve the recognition rate of human faces. The process is differentiated into classification, detection, and recognition phases that employ principal component analysis based learning. In this learning process, the errors in image processing based on the extracted different features are used for error classification and accuracy improvements.

    RESULTS: The performance of the proposed RERS-CC is validated experimentally using the input image dataset in MATLAB tool. The performance results show that the proposed method improves detection and recognition accuracy with fewer errors and processing time.

    CONCLUSION: The input image is processed with the knowledge of the features and errors that are observed with different orientations and time instances. With the help of matching dataset and the similarity index verification, the proposed method identifies precise human face with augmented true positives and recognition rate.

    Matched MeSH terms: Image Processing, Computer-Assisted
  12. Fulltext Narrowband Array Processing Beamforming Technique for Electrical Impedance Tomography
    Chitturi V, Farrukh N
    J Electr Bioimpedance, 2019 Jan;10(1):96-102.
    PMID: 33584889 DOI: 10.2478/joeb-2019-0014
    Electrical impedance tomography (EIT) has a large potential as a two dimensional imaging technique and is gaining attention among researchers across various fields of engineering. Beamforming techniques stem from the array signal processing field and is used for spatial filtering of array data to evaluate the location of objects. In this work the circular electrodes are treated as an array of sensors and beamforming technique is used to localize the object(s) in an electrical field. The conductivity distributions within a test tank is obtained by an EIT system in terms of electrode voltages. These voltages are then interpolated using elliptic partial differential equations. Finally, a narrowband beamformer detects the peak in the output response signal to localize the test object(s). Test results show that the beamforming technique can be used as a secondary method that may provide complementary information about accurate position of the test object(s) using an eight electrode EIT system. This method could possibly open new avenues for spatial EIT data filtering techniques with an understanding that the inverse problem is more likely considered here as a source localization algorithm instead as an image reconstruction algorithm.
    Matched MeSH terms: Image Processing, Computer-Assisted
  13. Fulltext ESTIMATION OF CORAL RUBBLE QUANTITY USING EDGE DETECTION AND MORPHOLOGICAL-BASED METHODS
    MUHAMMAD SUZURI HITAM, NURSYAHIRAH HAFIZ, ZAINUDDIN BACHOK, ZAINUDDIN BACHOK, MOHD SAFUAN CHE DIN
    UMT Journal of Undergraduate Research, 2019;1(3):105-119.
    MyJurnal
    Reef rubble representsthe broken components of the coraland reefstructure which could be in the form of dead,broken or other fragmented coral.The process to estimate the distribution of reef rubble is currently done manually and thus takesa long timeto completeand is laborious. This paper presentsan image-processing-basedmethod to estimate the distribution of reef rubbles in a coral reef environmentfrom a still image. The method is basically a series of image processing steps includingimage complement, image binarization, edgedetection, smoothing by Weiner filter and followed by erosion and dilation process.The experimentalresults showedthat the system wasable to roughly estimate the distribution of reef rubble.
    Matched MeSH terms: Image Processing, Computer-Assisted
  14. Fulltext Comparison of Low-Pass Filters for SPECT Imaging
    Sayed IS, Ismail SS
    Int J Biomed Imaging, 2020;2020:9239753.
    PMID: 32308670 DOI: 10.1155/2020/9239753
    In single photon emission computed tomography (SPECT) imaging, the choice of a suitable filter and its parameters for noise reduction purposes is a big challenge. Adverse effects on image quality arise if an improper filter is selected. Filtered back projection (FBP) is the most popular technique for image reconstruction in SPECT. With this technique, different types of reconstruction filters are used, such as the Butterworth and the Hamming. In this study, the effects on the quality of reconstructed images of the Butterworth filter were compared with the ones of the Hamming filter. A Philips ADAC forte gamma camera was used. A low-energy, high-resolution collimator was installed on the gamma camera. SPECT data were acquired by scanning a phantom with an insert composed of hot and cold regions. A Technetium-99m radioactive solution was homogenously mixed into the phantom. Furthermore, a symmetrical energy window (20%) centered at 140 keV was adjusted. Images were reconstructed by the FBP method. Various cutoff frequency values, namely, 0.35, 0.40, 0.45, and 0.50 cycles/cm, were selected for both filters, whereas for the Butterworth filter, the order was set at 7. Images of hot and cold regions were analyzed in terms of detectability, contrast, and signal-to-noise ratio (SNR). The findings of our study indicate that the Butterworth filter was able to expose more hot and cold regions in reconstructed images. In addition, higher contrast values were recorded, as compared to the Hamming filter. However, with the Butterworth filter, the decrease in SNR for both types of regions with the increase in cutoff frequency as compared to the Hamming filter was obtained. Overall, the Butterworth filter under investigation provided superior results than the Hamming filter. Effects of both filters on the quality of hot and cold region images varied with the change in cutoff frequency.
    Matched MeSH terms: Image Processing, Computer-Assisted
  15. Fulltext Automatic Polyp Segmentation in Colonoscopy Images Using a Modified Deep Convolutional Encoder-Decoder Architecture
    Eu CY, Tang TB, Lin CH, Lee LH, Lu CK
    Sensors (Basel), 2021 Aug 20;21(16).
    PMID: 34451072 DOI: 10.3390/s21165630
    Colorectal cancer has become the third most commonly diagnosed form of cancer, and has the second highest fatality rate of cancers worldwide. Currently, optical colonoscopy is the preferred tool of choice for the diagnosis of polyps and to avert colorectal cancer. Colon screening is time-consuming and highly operator dependent. In view of this, a computer-aided diagnosis (CAD) method needs to be developed for the automatic segmentation of polyps in colonoscopy images. This paper proposes a modified SegNet Visual Geometry Group-19 (VGG-19), a form of convolutional neural network, as a CAD method for polyp segmentation. The modifications include skip connections, 5 × 5 convolutional filters, and the concatenation of four dilated convolutions applied in parallel form. The CVC-ClinicDB, CVC-ColonDB, and ETIS-LaribPolypDB databases were used to evaluate the model, and it was found that our proposed polyp segmentation model achieved an accuracy, sensitivity, specificity, precision, mean intersection over union, and dice coefficient of 96.06%, 94.55%, 97.56%, 97.48%, 92.3%, and 95.99%, respectively. These results indicate that our model performs as well as or better than previous schemes in the literature. We believe that this study will offer benefits in terms of the future development of CAD tools for polyp segmentation for colorectal cancer diagnosis and management. In the future, we intend to embed our proposed network into a medical capsule robot for practical usage and try it in a hospital setting with clinicians.
    Matched MeSH terms: Image Processing, Computer-Assisted
  16. Computer aided diagnosis of Coronary Artery Disease, Myocardial Infarction and carotid atherosclerosis using ultrasound images: A review
    Faust O, Acharya UR, Sudarshan VK, Tan RS, Yeong CH, Molinari F, et al.
    Phys Med, 2017 Jan;33:1-15.
    PMID: 28010920 DOI: 10.1016/j.ejmp.2016.12.005
    The diagnosis of Coronary Artery Disease (CAD), Myocardial Infarction (MI) and carotid atherosclerosis is of paramount importance, as these cardiovascular diseases may cause medical complications and large number of death. Ultrasound (US) is a widely used imaging modality, as it captures moving images and image features correlate well with results obtained from other imaging methods. Furthermore, US does not use ionizing radiation and it is economical when compared to other imaging modalities. However, reading US images takes time and the relationship between image and tissue composition is complex. Therefore, the diagnostic accuracy depends on both time taken to read the images and experience of the screening practitioner. Computer support tools can reduce the inter-operator variability with lower subject specific expertise, when appropriate processing methods are used. In the current review, we analysed automatic detection methods for the diagnosis of CAD, MI and carotid atherosclerosis based on thoracic and Intravascular Ultrasound (IVUS). We found that IVUS is more often used than thoracic US for CAD. But for MI and carotid atherosclerosis IVUS is still in the experimental stage. Furthermore, thoracic US is more often used than IVUS for computer aided diagnosis systems.
    Matched MeSH terms: Image Processing, Computer-Assisted
  17. Enhancing the three-dimensional visualization of a foreign object using Mimics software
    Asif MK, Nambiar P, Khan IM, Aziz ZABCA, Noor NSBM, Shanmuhasuntharam P, et al.
    Radiol Case Rep, 2019 Dec;14(12):1545-1549.
    PMID: 31719943 DOI: 10.1016/j.radcr.2019.10.001
    A patient was referred to the Oral and Maxillofacial Imaging Division and the attending dental specialist suspected a foreign object at the anterior region of the maxilla. The region was scanned using Kodak 9000 3D cone-beam computed tomography (CBCT) extraoral imaging system (Carestream Health, Inc.) to determine the type and morphometric characteristic of foreign object. The CBCT images failed to determine the identity and nature of the foreign object. CBCT images were then exported to the Materialise Interactive Medical Image Control System (Mimics) software to evaluate whether this software can help in enhancing the visualization of the foreign object in the maxillofacial region. The findings showed that there was an improved visualization of the foreign body and the type of the object could be determined with certainty. The object was identified as an endodontic file and was clearly visible when visualized as a reconstructed 3D model in Mimics software. Although the identification of abnormalities has been dramatically improved using 3D scans, the visualization can be further enhanced using image processing software like Mimics.
    Matched MeSH terms: Image Processing, Computer-Assisted
  18. Application of a simple mono window land surface temperature algorithm from Landsat ETM+ over Al Qassim, Saudi Arabia
    Lim H, Mat Jafri M, Abdullah K, Sultan Alsultan
    Sains Malaysiana, 2012;41:841-846.
    This study was conducted to retrieve the land surface temperature (LST) from Landsat ETM+ data for Al Qassim, Saudi Arabia. The proposed technique employed a mono window LST algorithm for retrieving surface temperature from Landsat ETM+. The land surface emissivity and solar angle values were needed in order to apply these in the proposed algorithm. The surface emissivity values were computed based on the NDVI values. The LST values derived from ATCOR2_T in the PCI Geomatica image processing software was used for algorithm calibration. The results showed a high correlation
    coefficient (R) and low root-mean-square error (RMS) between the LST values retrieved from the proposed algorithm and ATCOR2_T. This study indicated that the proposed algorithm is capable of retrieving accurate LST values and the derived information can be used in the environmental impact assessment for Al Qassim area.
    Matched MeSH terms: Image Processing, Computer-Assisted
  19. 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.
    Matched MeSH terms: Image Processing, Computer-Assisted
  20. Skull-photograph superimposition: elucidating and resolving misinterpretations pertaining to methods and reliability
    Jayaprakash PT
    Int J Legal Med, 2023 Mar;137(2):379-381.
    PMID: 36520208 DOI: 10.1007/s00414-022-02931-w
    Matched MeSH terms: Image Processing, Computer-Assisted
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