Displaying publications 1 - 20 of 244 in total

Abstract:
Sort:
  1. Honarvar Shakibaei B, Jahanshahi P
    ScientificWorldJournal, 2014;2014:951842.
    PMID: 25202743 DOI: 10.1155/2014/951842
    Different blur invariant descriptors have been proposed so far, which are either in the spatial domain or based on the properties available in the moment domain. In this paper, a frequency framework is proposed to develop blur invariant features that are used to deconvolve a degraded image caused by a Gaussian blur. These descriptors are obtained by establishing an equivalent relationship between the normalized Fourier transforms of the blurred and original images, both normalized by their respective fixed frequencies set to one. Advantage of using the proposed invariant descriptors is that it is possible to estimate both the point spread function (PSF) and the original image. The performance of frequency invariants will be demonstrated through experiments. An image deconvolution is done as an additional application to verify the proposed blur invariant features.
    Matched MeSH terms: Image Processing, Computer-Assisted*
  2. Kubicek J, Penhaker M, Krejcar O, Selamat A
    Sensors (Basel), 2021 Jan 27;21(3).
    PMID: 33513910 DOI: 10.3390/s21030847
    There are various modern systems for the measurement and consequent acquisition of valuable patient's records in the form of medical signals and images, which are supposed to be processed to provide significant information about the state of biological tissues [...].
    Matched MeSH terms: Image Processing, Computer-Assisted*
  3. Yousef Kalafi E, Town C, Kaur Dhillon S
    Folia Morphol (Warsz), 2018;77(2):179-193.
    PMID: 28868609 DOI: 10.5603/FM.a2017.0079
    Identification of taxonomy at a specific level is time consuming and reliant upon expert ecologists. Hence the demand for automated species identification incre-ased over the last two decades. Automation of data classification is primarily focussed on images while incorporating and analysing image data has recently become easier due to developments in computational technology. Research ef-forts on identification of species include specimens' image processing, extraction of identical features, followed by classifying them into correct categories. In this paper, we discuss recent automated species identification systems, mainly for categorising and evaluating their methods. We reviewed and compared different methods in step by step scheme of automated identification and classification systems of species images. The selection of methods is influenced by many variables such as level of classification, number of training data and complexity of images. The aim of writing this paper is to provide researchers and scientists an extensive background study on work related to automated species identification, focusing on pattern recognition techniques in building such systems for biodiversity studies. (Folia Morphol 2018; 77, 2: 179-193).
    Matched MeSH terms: Image Processing, Computer-Assisted*
  4. Saifuddin Saif, A.F.M., Ali Garba Garba, Jamilu Awwalu, Haslina Arshad, Lailatul Qadri Zakaria
    MyJurnal
    Face detection and analysis is an important area in computer vision. Furthermore, face detection has been an active research field in the recent years following the advancement in digital image processing. The visualisation of visual entities or sub-pattern composition may become complex to visualise due to the high frequency of noise and light effect during examination. This study focuses on evaluating the ability of Haar classifier in detecting faces from three paired Min-Max values used on histogram stretching. Min-Max histogram stretching was the selected method for implementation given that it appears to be the appropriate technique from the observation carried out. Experimental results show that, 60-240 MinMax values, Haar classifier can accurately detect faces compared to the two values.
    Matched MeSH terms: Image Processing, Computer-Assisted
  5. Sim KS, Huang YH
    Scanning, 2015 Nov-Dec;37(6):381-8.
    PMID: 25969945 DOI: 10.1002/sca.21226
    This is the extended project by introducing the modified dynamic range histogram modification (MDRHM) and is presented in this paper. This technique is used to enhance the scanning electron microscope (SEM) imaging system. By comparing with the conventional histogram modification compensators, this technique utilizes histogram profiling by extending the dynamic range of each tile of an image to the limit of 0-255 range while retains its histogram shape. The proposed technique yields better image compensation compared to conventional methods.
    Matched MeSH terms: Image Processing, Computer-Assisted
  6. Nagaki K, Furuta T, Yamaji N, Kuniyoshi D, Ishihara M, Kishima Y, et al.
    Chromosome Res, 2021 12;29(3-4):361-371.
    PMID: 34648121 DOI: 10.1007/s10577-021-09676-z
    Observing chromosomes is a time-consuming and labor-intensive process, and chromosomes have been analyzed manually for many years. In the last decade, automated acquisition systems for microscopic images have advanced dramatically due to advances in their controlling computer systems, and nowadays, it is possible to automatically acquire sets of tiling-images consisting of large number, more than 1000, of images from large areas of specimens. However, there has been no simple and inexpensive system to efficiently select images containing mitotic cells among these images. In this paper, a classification system of chromosomal images by deep learning artificial intelligence (AI) that can be easily handled by non-data scientists was applied. With this system, models suitable for our own samples could be easily built on a Macintosh computer with Create ML. As examples, models constructed by learning using chromosome images derived from various plant species were able to classify images containing mitotic cells among samples from plant species not used for learning in addition to samples from the species used. The system also worked for cells in tissue sections and tetrads. Since this system is inexpensive and can be easily trained via deep learning using scientists' own samples, it can be used not only for chromosomal image analysis but also for analysis of other biology-related images.
    Matched MeSH terms: Image Processing, Computer-Assisted
  7. Kipli K, Hoque ME, Lim LT, Mahmood MH, Sahari SK, Sapawi R, et al.
    Comput Math Methods Med, 2018;2018:4019538.
    PMID: 30065780 DOI: 10.1155/2018/4019538
    Digital image processing is one of the most widely used computer vision technologies in biomedical engineering. In the present modern ophthalmological practice, biomarkers analysis through digital fundus image processing analysis greatly contributes to vision science. This further facilitates developments in medical imaging, enabling this robust technology to attain extensive scopes in biomedical engineering platform. Various diagnostic techniques are used to analyze retinal microvasculature image to enable geometric features measurements such as vessel tortuosity, branching angles, branching coefficient, vessel diameter, and fractal dimension. These extracted markers or characterized fundus digital image features provide insights and relates quantitative retinal vascular topography abnormalities to various pathologies such as diabetic retinopathy, macular degeneration, hypertensive retinopathy, transient ischemic attack, neovascular glaucoma, and cardiovascular diseases. Apart from that, this noninvasive research tool is automated, allowing it to be used in large-scale screening programs, and all are described in this present review paper. This paper will also review recent research on the image processing-based extraction techniques of the quantitative retinal microvascular feature. It mainly focuses on features associated with the early symptom of transient ischemic attack or sharp stroke.
    Matched MeSH terms: Image Processing, Computer-Assisted*
  8. Liew A, Lee CC, Lan BL, Tan M
    Comput Biol Med, 2021 09;136:104690.
    PMID: 34352452 DOI: 10.1016/j.compbiomed.2021.104690
    Convolutional neural networks (CNNs) have been used quite successfully for semantic segmentation of brain tumors. However, current CNNs and attention mechanisms are stochastic in nature and neglect the morphological indicators used by radiologists to manually annotate regions of interest. In this paper, we introduce a channel and spatial wise asymmetric attention (CASPIAN) by leveraging the inherent structure of tumors to detect regions of saliency. To demonstrate the efficacy of our proposed layer, we integrate this into a well-established convolutional neural network (CNN) architecture to achieve higher Dice scores, with less GPU resources. Also, we investigate the inclusion of auxiliary multiscale and multiplanar attention branches to increase the spatial context crucial in semantic segmentation tasks. The resulting architecture is the new CASPIANET++, which achieves Dice Scores of 91.19%, 87.6% and 81.03% for whole tumor, tumor core and enhancing tumor respectively. Furthermore, driven by the scarcity of brain tumor data, we investigate the Noisy Student method for segmentation tasks. Our new Noisy Student Curriculum Learning paradigm, which infuses noise incrementally to increase the complexity of the training images exposed to the network, further boosts the enhancing tumor region to 81.53%. Additional validation performed on the BraTS2020 data shows that the Noisy Student Curriculum Learning method works well without any additional training or finetuning.
    Matched MeSH terms: Image Processing, Computer-Assisted*
  9. Ooi AZH, Embong Z, Abd Hamid AI, Zainon R, Wang SL, Ng TF, et al.
    Sensors (Basel), 2021 Sep 24;21(19).
    PMID: 34640698 DOI: 10.3390/s21196380
    Optometrists, ophthalmologists, orthoptists, and other trained medical professionals use fundus photography to monitor the progression of certain eye conditions or diseases. Segmentation of the vessel tree is an essential process of retinal analysis. In this paper, an interactive blood vessel segmentation from retinal fundus image based on Canny edge detection is proposed. Semi-automated segmentation of specific vessels can be done by simply moving the cursor across a particular vessel. The pre-processing stage includes the green color channel extraction, applying Contrast Limited Adaptive Histogram Equalization (CLAHE), and retinal outline removal. After that, the edge detection techniques, which are based on the Canny algorithm, will be applied. The vessels will be selected interactively on the developed graphical user interface (GUI). The program will draw out the vessel edges. After that, those vessel edges will be segmented to bring focus on its details or detect the abnormal vessel. This proposed approach is useful because different edge detection parameter settings can be applied to the same image to highlight particular vessels for analysis or presentation.
    Matched MeSH terms: Image Processing, Computer-Assisted*
  10. Ibrahim MF, Ahmad Sa'ad FS, Zakaria A, Md Shakaff AY
    Sensors (Basel), 2016 Oct 27;16(11).
    PMID: 27801799
    The conventional method of grading Harumanis mango is time-consuming, costly and affected by human bias. In this research, an in-line system was developed to classify Harumanis mango using computer vision. The system was able to identify the irregularity of mango shape and its estimated mass. A group of images of mangoes of different size and shape was used as database set. Some important features such as length, height, centroid and parameter were extracted from each image. Fourier descriptor and size-shape parameters were used to describe the mango shape while the disk method was used to estimate the mass of the mango. Four features have been selected by stepwise discriminant analysis which was effective in sorting regular and misshapen mango. The volume from water displacement method was compared with the volume estimated by image processing using paired t-test and Bland-Altman method. The result between both measurements was not significantly different (P > 0.05). The average correct classification for shape classification was 98% for a training set composed of 180 mangoes. The data was validated with another testing set consist of 140 mangoes which have the success rate of 92%. The same set was used for evaluating the performance of mass estimation. The average success rate of the classification for grading based on its mass was 94%. The results indicate that the in-line sorting system using machine vision has a great potential in automatic fruit sorting according to its shape and mass.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*; Image Processing, Computer-Assisted/standards
  11. Arif AS, Mansor S, Logeswaran R, Karim HA
    J Med Syst, 2015 Feb;39(2):5.
    PMID: 25628161 DOI: 10.1007/s10916-015-0200-z
    The massive number of medical images produced by fluoroscopic and other conventional diagnostic imaging devices demand a considerable amount of space for data storage. This paper proposes an effective method for lossless compression of fluoroscopic images. The main contribution in this paper is the extraction of the regions of interest (ROI) in fluoroscopic images using appropriate shapes. The extracted ROI is then effectively compressed using customized correlation and the combination of Run Length and Huffman coding, to increase compression ratio. The experimental results achieved show that the proposed method is able to improve the compression ratio by 400 % as compared to that of traditional methods.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods
  12. Sim KS, Ting HY, Lai MA, Tso CP
    J Microsc, 2009 Jun;234(3):243-50.
    PMID: 19493101 DOI: 10.1111/j.1365-2818.2009.03167.x
    An improvement to the previously proposed Canny optimization technique for scanning electron microscope image colorization is reported. The additional process is adaptive tuning, where colour tuning is performed adaptively, based on comparing the original luminance values with calculated luminance values. The complete adaptive Canny optimization technique gives significantly better mechanical contrast on scanning electron microscope grey-scale images than do existing methods.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  13. Sim KS, Thong LW, Ting HY, Tso CP
    J Microsc, 2010 Feb;237(2):111-8.
    PMID: 20096041 DOI: 10.1111/j.1365-2818.2009.03325.x
    Interpolation techniques that are used for image magnification to obtain more useful details of the surface such as morphology and mechanical contrast usually rely on the signal information distributed around edges and areas of sharp changes and these signal information can also be used to predict missing details from the sample image. However, many of these interpolation methods tend to smooth or blur out image details around the edges. In the present study, a Lagrange time delay estimation interpolator method is proposed and this method only requires a small filter order and has no noticeable estimation bias. Comparing results with the original scanning electron microscope magnification and results of various other interpolation methods, the Lagrange time delay estimation interpolator is found to be more efficient, more robust and easier to execute.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  14. Saini R, Azmi AS, Ghani NB, Al-Salihi KA
    Med J Malaysia, 2007 Aug;62(3):238-40.
    PMID: 18246915 MyJurnal
    This study was designed to identify surface and subsurface microscopic changes in different carious lesions by using Confocal Laser Scanning Microscope (CLSM) and Image analyzer (light microscopy). Thirty extracted carious posterior teeth were fixed, embedded and polymerized in plastic fixation medium. The final thin sections (80mm) were stained with H&E and Masson Goldner's Tricome while others were left unstained. Under Confocal, marked differences between control sound enamel and dentin, and carious area of the samples were observed which illustrated that a correlation existed between the zone of autofluoresence, demineralization and carious enamel and dentin. Compared to CLSM, Image Analyzer only produce two-dimensional images but the histopathological changes were better appreciated by using various staining methods.
    Matched MeSH terms: Image Processing, Computer-Assisted*
  15. Sudarshan VK, Mookiah MR, Acharya UR, Chandran V, Molinari F, Fujita H, et al.
    Comput Biol Med, 2016 Feb 1;69:97-111.
    PMID: 26761591 DOI: 10.1016/j.compbiomed.2015.12.006
    Ultrasound is an important and low cost imaging modality used to study the internal organs of human body and blood flow through blood vessels. It uses high frequency sound waves to acquire images of internal organs. It is used to screen normal, benign and malignant tissues of various organs. Healthy and malignant tissues generate different echoes for ultrasound. Hence, it provides useful information about the potential tumor tissues that can be analyzed for diagnostic purposes before therapeutic procedures. Ultrasound images are affected with speckle noise due to an air gap between the transducer probe and the body. The challenge is to design and develop robust image preprocessing, segmentation and feature extraction algorithms to locate the tumor region and to extract subtle information from isolated tumor region for diagnosis. This information can be revealed using a scale space technique such as the Discrete Wavelet Transform (DWT). It decomposes an image into images at different scales using low pass and high pass filters. These filters help to identify the detail or sudden changes in intensity in the image. These changes are reflected in the wavelet coefficients. Various texture, statistical and image based features can be extracted from these coefficients. The extracted features are subjected to statistical analysis to identify the significant features to discriminate normal and malignant ultrasound images using supervised classifiers. This paper presents a review of wavelet techniques used for preprocessing, segmentation and feature extraction of breast, thyroid, ovarian and prostate cancer using ultrasound images.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  16. Pang T, Wong JHD, Ng WL, Chan CS
    Comput Methods Programs Biomed, 2021 May;203:106018.
    PMID: 33714900 DOI: 10.1016/j.cmpb.2021.106018
    BACKGROUND AND OBJECTIVE: The capability of deep learning radiomics (DLR) to extract high-level medical imaging features has promoted the use of computer-aided diagnosis of breast mass detected on ultrasound. Recently, generative adversarial network (GAN) has aided in tackling a general issue in DLR, i.e., obtaining a sufficient number of medical images. However, GAN methods require a pair of input and labeled images, which require an exhaustive human annotation process that is very time-consuming. The aim of this paper is to develop a radiomics model based on a semi-supervised GAN method to perform data augmentation in breast ultrasound images.

    METHODS: A total of 1447 ultrasound images, including 767 benign masses and 680 malignant masses were acquired from a tertiary hospital. A semi-supervised GAN model was developed to augment the breast ultrasound images. The synthesized images were subsequently used to classify breast masses using a convolutional neural network (CNN). The model was validated using a 5-fold cross-validation method.

    RESULTS: The proposed GAN architecture generated high-quality breast ultrasound images, verified by two experienced radiologists. The improved performance of semi-supervised learning increased the quality of the synthetic data produced in comparison to the baseline method. We achieved more accurate breast mass classification results (accuracy 90.41%, sensitivity 87.94%, specificity 85.86%) with our synthetic data augmentation compared to other state-of-the-art methods.

    CONCLUSION: The proposed radiomics model has demonstrated a promising potential to synthesize and classify breast masses on ultrasound in a semi-supervised manner.

    Matched MeSH terms: Image Processing, Computer-Assisted*
  17. Latif G, Iskandar DNFA, Alghazo J, Butt MM
    Curr Med Imaging, 2021;17(1):56-63.
    PMID: 32160848 DOI: 10.2174/1573405616666200311122429
    BACKGROUND: Detection of brain tumor is a complicated task, which requires specialized skills and interpretation techniques. Accurate brain tumor classification and segmentation from MR images provide an essential choice for medical treatments. Different objects within an MR image have similar size, shape, and density, which makes the tumor classification and segmentation even more complex.

    OBJECTIVE: Classification of the brain MR images into tumorous and non-tumorous using deep features and different classifiers to get higher accuracy.

    METHODS: In this study, a novel four-step process is proposed; pre-processing for image enhancement and compression, feature extraction using convolutional neural networks (CNN), classification using the multilayer perceptron and finally, tumor segmentation using enhanced fuzzy cmeans method.

    RESULTS: The system is tested on 65 cases in four modalities consisting of 40,300 MR Images obtained from the BRATS-2015 dataset. These include images of 26 Low-Grade Glioma (LGG) tumor cases and 39 High-Grade Glioma (HGG) tumor cases. The proposed CNN feature-based classification technique outperforms the existing methods by achieving an average accuracy of 98.77% and a noticeable improvement in the segmentation results are measured.

    CONCLUSION: The proposed method for brain MR image classification to detect Glioma Tumor detection can be adopted as it gives better results with high accuracies.

    Matched MeSH terms: Image Processing, Computer-Assisted*
  18. Latif G, Alghazo J, Sibai FN, Iskandar DNFA, Khan AH
    Curr Med Imaging, 2021;17(8):917-930.
    PMID: 33397241 DOI: 10.2174/1573405616666210104111218
    BACKGROUND: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques.

    OBJECTIVE: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers.

    RESULTS: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging.

    CONCLUSION: In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

    Matched MeSH terms: Image Processing, Computer-Assisted*
  19. Atee HA, Ahmad R, Noor NM, Rahma AM, Aljeroudi Y
    PLoS One, 2017;12(2):e0170329.
    PMID: 28196080 DOI: 10.1371/journal.pone.0170329
    In image steganography, determining the optimum location for embedding the secret message precisely with minimum distortion of the host medium remains a challenging issue. Yet, an effective approach for the selection of the best embedding location with least deformation is far from being achieved. To attain this goal, we propose a novel approach for image steganography with high-performance, where extreme learning machine (ELM) algorithm is modified to create a supervised mathematical model. This ELM is first trained on a part of an image or any host medium before being tested in the regression mode. This allowed us to choose the optimal location for embedding the message with best values of the predicted evaluation metrics. Contrast, homogeneity, and other texture features are used for training on a new metric. Furthermore, the developed ELM is exploited for counter over-fitting while training. The performance of the proposed steganography approach is evaluated by computing the correlation, structural similarity (SSIM) index, fusion matrices, and mean square error (MSE). The modified ELM is found to outperform the existing approaches in terms of imperceptibility. Excellent features of the experimental results demonstrate that the proposed steganographic approach is greatly proficient for preserving the visual information of an image. An improvement in the imperceptibility as much as 28% is achieved compared to the existing state of the art methods.
    Matched MeSH terms: Image Processing, Computer-Assisted*
  20. Chong JWR, Khoo KS, Chew KW, Vo DN, Balakrishnan D, Banat F, et al.
    Bioresour Technol, 2023 Feb;369:128418.
    PMID: 36470491 DOI: 10.1016/j.biortech.2022.128418
    The identification of microalgae species is an important tool in scientific research and commercial application to prevent harmful algae blooms (HABs) and recognizing potential microalgae strains for the bioaccumulation of valuable bioactive ingredients. The aim of this study is to incorporate rapid, high-accuracy, reliable, low-cost, simple, and state-of-the-art identification methods. Thus, increasing the possibility for the development of potential recognition applications, that could identify toxic-producing and valuable microalgae strains. Recently, deep learning (DL) has brought the study of microalgae species identification to a much higher depth of efficiency and accuracy. In doing so, this review paper emphasizes the significance of microalgae identification, and various forms of machine learning algorithms for image classification, followed by image pre-processing techniques, feature extraction, and selection for further classification accuracy. Future prospects over the challenges and improvements of potential DL classification model development, application in microalgae recognition, and image capturing technologies are discussed accordingly.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods
Filters
Contact Us

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links