Displaying publications 21 - 40 of 167 in total

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  1. An image processing application for the localization and segmentation of lymphoblast cell using peripheral blood images
    Madhloom HT, Kareem SA, Ariffin H
    J Med Syst, 2012 Aug;36(4):2149-58.
    PMID: 21399912 DOI: 10.1007/s10916-011-9679-0
    An important preliminary step in the diagnosis of leukemia is the visual examination of the patient's peripheral blood smear under the microscope. Morphological changes in the white blood cells can be an indicator of the nature and severity of the disease. Manual techniques are labor intensive, slow, error prone and costly. A computerized system can be used as a supportive tool for the specialist in order to enhance and accelerate the morphological analysis process. This research present a new method that integrates color features with the morphological reconstruction to localize and isolate lymphoblast cells from a microscope image that contains many cells. The localization and segmentation are conducted using a proposed method that consists of an integration of several digital image processing techniques. 180 microscopic blood images were tested, and the proposed framework managed to obtain 100% accuracy for the localization of the lymphoblast cells and separate it from the image scene. The results obtained indicate that the proposed method can be safely used for the purpose of lymphoblast cells localization and segmentation and subsequently, aiding the diagnosis of leukemia.
    Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
  2. Vascular intersection detection in retina fundus images using a new hybrid approach
    Aibinu AM, Iqbal MI, Shafie AA, Salami MJ, Nilsson M
    Comput Biol Med, 2010 Jan;40(1):81-9.
    PMID: 20022595 DOI: 10.1016/j.compbiomed.2009.11.004
    The use of vascular intersection aberration as one of the signs when monitoring and diagnosing diabetic retinopathy from retina fundus images (FIs) has been widely reported in the literature. In this paper, a new hybrid approach called the combined cross-point number (CCN) method able to detect the vascular bifurcation and intersection points in FIs is proposed. The CCN method makes use of two vascular intersection detection techniques, namely the modified cross-point number (MCN) method and the simple cross-point number (SCN) method. Our proposed approach was tested on images obtained from two different and publicly available fundus image databases. The results show a very high precision, accuracy, sensitivity and low false rate in detecting both bifurcation and crossover points compared with both the MCN and the SCN methods.
    Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
  3. Fulltext Authentication and data hiding using a hybrid ROI-based watermarking scheme for DICOM images
    Al-Qershi OM, Khoo BE
    J Digit Imaging, 2011 Feb;24(1):114-25.
    PMID: 19937363 DOI: 10.1007/s10278-009-9253-1
    Authenticating medical images using watermarking techniques has become a very popular area of research, and some works in this area have been reported worldwide recently. Besides authentication, many data-hiding techniques have been proposed to conceal patient's data into medical images aiming to reduce the cost needed to store data and the time needed to transmit data when required. In this paper, we present a new hybrid watermarking scheme for DICOM images. In our scheme, two well-known techniques are combined to gain the advantages of both and fulfill the requirements of authentication and data hiding. The scheme divides the images into two parts, the region of interest (ROI) and the region of non-interest (RONI). Patient's data are embedded into ROI using a reversible technique based on difference expansion, while tamper detection and recovery data are embedded into RONI using a robust technique based on discrete wavelet transform. The experimental results show the ability of hiding patient's data with a very good visual quality, while ROI, the most important area for diagnosis, is retrieved exactly at the receiver side. The scheme also shows some robustness against certain levels of salt and pepper and cropping noise.
    Matched MeSH terms: Image Interpretation, Computer-Assisted*
  4. A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram
    Meselhy Eltoukhy M, Faye I, Belhaouari Samir B
    Comput Biol Med, 2010 Apr;40(4):384-91.
    PMID: 20163793 DOI: 10.1016/j.compbiomed.2010.02.002
    This paper presents a comparative study between wavelet and curvelet transform for breast cancer diagnosis in digital mammogram. Using multiresolution analysis, mammogram images are decomposed into different resolution levels, which are sensitive to different frequency bands. A set of the biggest coefficients from each decomposition level is extracted. Then a supervised classifier system based on Euclidian distance is constructed. The performance of the classifier is evaluated using a 2 x 5-fold cross validation followed by a statistical analysis. The experimental results suggest that curvelet transform outperforms wavelet transform and the difference is statistically significant.
    Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
  5. Area assessment of psoriasis lesions for PASI scoring
    Fadzil MH, Ihtatho D, Affandi AM, Hussein SH
    J Med Eng Technol, 2009;33(6):426-36.
    PMID: 19557605 DOI: 10.1080/07434610902744066
    Psoriasis is a skin disorder which is caused by a genetic fault. Although there is no cure for psoriasis, there are many treatment modalities to help control the disease. To evaluate treatment efficacy, the current gold standard method, PASI (Psoriasis Area and Severity Index), is used to measure psoriasis severity by evaluating the area, erythema, scaliness and thickness of the plaques. However, the determination of PASI can be tedious and subjective. In this work, we develop a computer vision method that determines one of the PASI parameters, the lesion area. The method isolates healthy and healed skin areas from lesion areas by analysing the hue and chroma information in the CIE L*a*b* colour space. Centroids of healthy skin and psoriasis in the hue-chroma space are determined from selected sample. The Euclidean distance of all pixels from each centroid is calculated. Pixels are assigned to either healthy skin or psorasis lesion classes based on the minimum Euclidean distance. The study involves patients from different ethnic origins having three different skin tones. Results obtained show that the proposed method is able to determine lesion areas with accuracy higher than 90% for 28 out of 30 cases.

    Study site: Dermatology Clinic, Hospital Kuala Lumpur
    Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
  6. Liver isolation in abdominal MRI
    Rajasvaran L, Haw TW, Sarker SZ
    J Med Syst, 2008 Aug;32(4):259-68.
    PMID: 18619090
    This work presents a method for liver isolation in magnetic resonance imaging (MRI) abdomen images. It is based on a priori statistical information about the shape of the liver obtained from a training set using the segmentation approach. Morphological watershed algorithm is used as a key technique as it is a simple and intuitive method, producing a complete division of the image in separated regions even if the contrast is poor, and it is fast, with possibility for parallel implementation. To overcome the over-segmentation problem of the watershed process, image preprocessing and postprocessing are applied. Morphological smoothing, Gaussian smoothing, intensity thresholding, gradient computation and gradient thresholding are proposed for preprocessing with morphological and graph based region adjacent list constructed for region merging. A new integrated region similarity function is also defined for region merging control. The proposed method produces good isolation of liver in axial MRI images of the abdomen, as is shown in this paper.
    Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
  7. Time-domain reconstruction using sensitivity coefficients for limited view ultrawide band tomography
    Abdullah MZ, Yin W, Bilal M, Armitage DW, Mackin R, Peyton AJ
    Rev Sci Instrum, 2007 Aug;78(8):084703.
    PMID: 17764343
    This article addresses time-domain ultrawide band (UWB) electromagnetic tomography for reconstructing the unknown spatial characteristic of an object from observations of the arrivals of short electromagnetic (EM) pulses. Here, the determination of the first peak arrival of the EM traces constitutes the forward problem, and the inverse problem aims to reconstruct the EM property distribution of the media. In this article, the finite-difference time-domain method implementing a perfectly matched layer is used to solve the forward problem from which the system sensitivity maps are determined. Image reconstruction is based on the combination of a linearized update and regularized Landweber minimization algorithm. Experimental data from a laboratory UWB system using targets of different contrasts, sizes, and shapes in an aqueous media are presented. The results show that this technique can accurately detect and locate unknown targets in spite of the presence of significant levels of noise in the data.
    Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
  8. Fulltext Watermark Compression in Medical Image Watermarking Using Lempel-Ziv-Welch (LZW) Lossless Compression Technique
    Badshah G, Liew SC, Zain JM, Ali M
    J Digit Imaging, 2016 Apr;29(2):216-25.
    PMID: 26429361 DOI: 10.1007/s10278-015-9822-4
    In teleradiology, image contents may be altered due to noisy communication channels and hacker manipulation. Medical image data is very sensitive and can not tolerate any illegal change. Illegally changed image-based analysis could result in wrong medical decision. Digital watermarking technique can be used to authenticate images and detect as well as recover illegal changes made to teleradiology images. Watermarking of medical images with heavy payload watermarks causes image perceptual degradation. The image perceptual degradation directly affects medical diagnosis. To maintain the image perceptual and diagnostic qualities standard during watermarking, the watermark should be lossless compressed. This paper focuses on watermarking of ultrasound medical images with Lempel-Ziv-Welch (LZW) lossless-compressed watermarks. The watermark lossless compression reduces watermark payload without data loss. In this research work, watermark is the combination of defined region of interest (ROI) and image watermarking secret key. The performance of the LZW compression technique was compared with other conventional compression methods based on compression ratio. LZW was found better and used for watermark lossless compression in ultrasound medical images watermarking. Tabulated results show the watermark bits reduction, image watermarking with effective tamper detection and lossless recovery.
    Matched MeSH terms: Image Interpretation, Computer-Assisted/standards*
  9. Deblurring Computed Tomography Medical Images Using a Novel Amended Landweber Algorithm
    Al-Ameen Z, Sulong G
    Interdiscip Sci, 2015 Sep;7(3):319-25.
    PMID: 26199211 DOI: 10.1007/s12539-015-0022-1
    In computed tomography (CT), blurring occurs due to different hardware or software errors and hides certain medical details that are present in an image. Image blur is difficult to avoid in many circumstances and can frequently ruin an image. For this, many methods have been developed to reduce the blurring artifact from CT images. The problems with these methods are the high implementation time, noise amplification and boundary artifacts. Hence, this article presents an amended version of the iterative Landweber algorithm to attain artifact-free boundaries and less noise amplification in a faster application time. In this study, both synthetic and real blurred CT images are used to validate the proposed method properly. Similarly, the quality of the processed synthetic images is measured using the feature similarity index, structural similarity and visual information fidelity in pixel domain metrics. Finally, the results obtained from intensive experiments and performance evaluations show the efficiency of the proposed algorithm, which has potential as a new approach in medical image processing.
    Matched MeSH terms: Radiographic Image Interpretation, Computer-Assisted/methods*
  10. Fulltext Invariant domain watermarking using heaviside function of order alpha and fractional Gaussian field
    Abbasi A, Woo CS, Ibrahim RW, Islam S
    PLoS One, 2015;10(4):e0123427.
    PMID: 25884854 DOI: 10.1371/journal.pone.0123427
    Digital image watermarking is an important technique for the authentication of multimedia content and copyright protection. Conventional digital image watermarking techniques are often vulnerable to geometric distortions such as Rotation, Scaling, and Translation (RST). These distortions desynchronize the watermark information embedded in an image and thus disable watermark detection. To solve this problem, we propose an RST invariant domain watermarking technique based on fractional calculus. We have constructed a domain using Heaviside function of order alpha (HFOA). The HFOA models the signal as a polynomial for watermark embedding. The watermark is embedded in all the coefficients of the image. We have also constructed a fractional variance formula using fractional Gaussian field. A cross correlation method based on the fractional Gaussian field is used for watermark detection. Furthermore the proposed method enables blind watermark detection where the original image is not required during the watermark detection thereby making it more practical than non-blind watermarking techniques. Experimental results confirmed that the proposed technique has a high level of robustness.
    Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
  11. 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.
    Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
  12. A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images
    Acharya UR, Bhat S, Koh JEW, Bhandary SV, Adeli H
    Comput Biol Med, 2017 Sep 01;88:72-83.
    PMID: 28700902 DOI: 10.1016/j.compbiomed.2017.06.022
    Glaucoma is an optic neuropathy defined by characteristic damage to the optic nerve and accompanying visual field deficits. Early diagnosis and treatment are critical to prevent irreversible vision loss and ultimate blindness. Current techniques for computer-aided analysis of the optic nerve and retinal nerve fiber layer (RNFL) are expensive and require keen interpretation by trained specialists. Hence, an automated system is highly desirable for a cost-effective and accurate screening for the diagnosis of glaucoma. This paper presents a new methodology and a computerized diagnostic system. Adaptive histogram equalization is used to convert color images to grayscale images followed by convolution of these images with Leung-Malik (LM), Schmid (S), and maximum response (MR4 and MR8) filter banks. The basic microstructures in typical images are called textons. The convolution process produces textons. Local configuration pattern (LCP) features are extracted from these textons. The significant features are selected using a sequential floating forward search (SFFS) method and ranked using the statistical t-test. Finally, various classifiers are used for classification of images into normal and glaucomatous classes. A high classification accuracy of 95.8% is achieved using six features obtained from the LM filter bank and the k-nearest neighbor (kNN) classifier. A glaucoma integrative index (GRI) is also formulated to obtain a reliable and effective system.
    Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
  13. Iterative variational mode decomposition based automated detection of glaucoma using fundus images
    Maheshwari S, Pachori RB, Kanhangad V, Bhandary SV, Acharya UR
    Comput Biol Med, 2017 Sep 01;88:142-149.
    PMID: 28728059 DOI: 10.1016/j.compbiomed.2017.06.017
    Glaucoma is one of the leading causes of permanent vision loss. It is an ocular disorder caused by increased fluid pressure within the eye. The clinical methods available for the diagnosis of glaucoma require skilled supervision. They are manual, time consuming, and out of reach of common people. Hence, there is a need for an automated glaucoma diagnosis system for mass screening. In this paper, we present a novel method for an automated diagnosis of glaucoma using digital fundus images. Variational mode decomposition (VMD) method is used in an iterative manner for image decomposition. Various features namely, Kapoor entropy, Renyi entropy, Yager entropy, and fractal dimensions are extracted from VMD components. ReliefF algorithm is used to select the discriminatory features and these features are then fed to the least squares support vector machine (LS-SVM) for classification. Our proposed method achieved classification accuracies of 95.19% and 94.79% using three-fold and ten-fold cross-validation strategies, respectively. This system can aid the ophthalmologists in confirming their manual reading of classes (glaucoma or normal) using fundus images.
    Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
  14. Diagnosis of retinal health in digital fundus images using continuous wavelet transform (CWT) and entropies
    Koh JEW, Acharya UR, Hagiwara Y, Raghavendra U, Tan JH, Sree SV, et al.
    Comput Biol Med, 2017 05 01;84:89-97.
    PMID: 28351716 DOI: 10.1016/j.compbiomed.2017.03.008
    Vision is paramount to humans to lead an active personal and professional life. The prevalence of ocular diseases is rising, and diseases such as glaucoma, Diabetic Retinopathy (DR) and Age-related Macular Degeneration (AMD) are the leading causes of blindness in developed countries. Identifying these diseases in mass screening programmes is time-consuming, labor-intensive and the diagnosis can be subjective. The use of an automated computer aided diagnosis system will reduce the time taken for analysis and will also reduce the inter-observer subjective variabilities in image interpretation. In this work, we propose one such system for the automatic classification of normal from abnormal (DR, AMD, glaucoma) images. We had a total of 404 normal and 1082 abnormal fundus images in our database. As the first step, 2D-Continuous Wavelet Transform (CWT) decomposition on the fundus images of two classes was performed. Subsequently, energy features and various entropies namely Yager, Renyi, Kapoor, Shannon, and Fuzzy were extracted from the decomposed images. Then, adaptive synthetic sampling approach was applied to balance the normal and abnormal datasets. Next, the extracted features were ranked according to the significances using Particle Swarm Optimization (PSO). Thereupon, the ranked and selected features were used to train the random forest classifier using stratified 10-fold cross validation. Overall, the proposed system presented a performance rate of 92.48%, and a sensitivity and specificity of 89.37% and 95.58% respectively using 15 features. This novel system shows promise in detecting abnormal fundus images, and hence, could be a valuable adjunct eye health screening tool that could be employed in polyclinics, and thereby reduce the workload of specialists at hospitals.
    Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
  15. Shear wave elastography for characterization of breast lesions: Shearlet transform and local binary pattern histogram techniques
    Acharya UR, Ng WL, Rahmat K, Sudarshan VK, Koh JEW, Tan JH, et al.
    Comput Biol Med, 2017 12 01;91:13-20.
    PMID: 29031099 DOI: 10.1016/j.compbiomed.2017.10.001
    Shear wave elastography (SWE) examination using ultrasound elastography (USE) is a popular imaging procedure for obtaining elasticity information of breast lesions. Elasticity parameters obtained through SWE can be used as biomarkers that can distinguish malignant breast lesions from benign ones. Furthermore, the elasticity parameters extracted from SWE can speed up the diagnosis and possibly reduce human errors. In this paper, Shearlet transform and local binary pattern histograms (LBPH) are proposed as an original algorithm to differentiate malignant and benign breast lesions. First, Shearlet transform is applied on the SWE images to acquire low frequency, horizontal and vertical cone coefficients. Next, LBPH features are extracted from the Shearlet transform coefficients and subjected to dimensionality reduction using locality sensitivity discriminating analysis (LSDA). The reduced LSDA components are ranked and then fed to several classifiers for the automated classification of breast lesions. A probabilistic neural network classifier trained only with seven top ranked features performed best, and achieved 98.08% accuracy, 98.63% sensitivity, and 97.59% specificity in distinguishing malignant from benign breast lesions. The high sensitivity and specificity of our system indicates that it can be employed as a primary screening tool for faster diagnosis of breast malignancies, thereby possibly reducing the mortality rate due to breast cancer.
    Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
  16. FPGA implementation of real-time SENSE reconstruction using pre-scan and Emaps sensitivities
    Siddiqui MF, Reza AW, Shafique A, Omer H, Kanesan J
    Magn Reson Imaging, 2017 12;44:82-91.
    PMID: 28855113 DOI: 10.1016/j.mri.2017.08.005
    Sensitivity Encoding (SENSE) is a widely used technique in Parallel Magnetic Resonance Imaging (MRI) to reduce scan time. Reconfigurable hardware based architecture for SENSE can potentially provide image reconstruction with much less computation time. Application specific hardware platform for SENSE may dramatically increase the power efficiency of the system and can decrease the execution time to obtain MR images. A new implementation of SENSE on Field Programmable Gate Array (FPGA) is presented in this study, which provides real-time SENSE reconstruction right on the receiver coil data acquisition system with no need to transfer the raw data to the MRI server, thereby minimizing the transmission noise and memory usage. The proposed SENSE architecture can reconstruct MR images using receiver coil sensitivity maps obtained using pre-scan and eigenvector (E-maps) methods. The results show that the proposed system consumes remarkably less computation time for SENSE reconstruction, i.e., 0.164ms @ 200MHz, while maintaining the quality of the reconstructed images with good mean SNR (29+ dB), less RMSE (<5×10-2) and comparable artefact power (<9×10-4) to conventional SENSE reconstruction. A comparison of the center line profiles of the reconstructed and reference images also indicates a good quality of the reconstructed images. Furthermore, the results indicate that the proposed architectural design can prove to be a significant tool for SENSE reconstruction in modern MRI scanners and its low power consumption feature can be remarkable for portable MRI scanners.
    Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
  17. Correlation and agreement of a digital and conventional method to measure arch parameters
    Nawi N, Mohamed AM, Marizan Nor M, Ashar NA
    J Orofac Orthop, 2018 Jan;79(1):19-27.
    PMID: 29116344 DOI: 10.1007/s00056-017-0111-3
    OBJECTIVE: The aim of the present study was to determine the overall reliability and validity of arch parameters measured digitally compared to conventional measurement.

    METHODS: A sample of 111 plaster study models of Down syndrome (DS) patients were digitized using a blue light three-dimensional (3D) scanner. Digital and manual measurements of defined parameters were performed using Geomagic analysis software (Geomagic Studio 2014 software, 3D Systems, Rock Hill, SC, USA) on digital models and with a digital calliper (Tuten, Germany) on plaster study models. Both measurements were repeated twice to validate the intraexaminer reliability based on intraclass correlation coefficients (ICCs) using the independent t test and Pearson's correlation, respectively. The Bland-Altman method of analysis was used to evaluate the agreement of the measurement between the digital and plaster models.

    RESULTS: No statistically significant differences (p > 0.05) were found between the manual and digital methods when measuring the arch width, arch length, and space analysis. In addition, all parameters showed a significant correlation coefficient (r ≥ 0.972; p 

    Matched MeSH terms: Image Interpretation, Computer-Assisted*
  18. 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.
    Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
  19. Fulltext Sudden event recognition: a survey
    Suriani NS, Hussain A, Zulkifley MA
    Sensors (Basel), 2013 Aug 05;13(8):9966-98.
    PMID: 23921828 DOI: 10.3390/s130809966
    Event recognition is one of the most active research areas in video surveillance fields. Advancement in event recognition systems mainly aims to provide convenience, safety and an efficient lifestyle for humanity. A precise, accurate and robust approach is necessary to enable event recognition systems to respond to sudden changes in various uncontrolled environments, such as the case of an emergency, physical threat and a fire or bomb alert. The performance of sudden event recognition systems depends heavily on the accuracy of low level processing, like detection, recognition, tracking and machine learning algorithms. This survey aims to detect and characterize a sudden event, which is a subset of an abnormal event in several video surveillance applications. This paper discusses the following in detail: (1) the importance of a sudden event over a general anomalous event; (2) frameworks used in sudden event recognition; (3) the requirements and comparative studies of a sudden event recognition system and (4) various decision-making approaches for sudden event recognition. The advantages and drawbacks of using 3D images from multiple cameras for real-time application are also discussed. The paper concludes with suggestions for future research directions in sudden event recognition.
    Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
  20. MPS-FFA: A multiplane and multiscale feature fusion attention network for Alzheimer's disease prediction with structural MRI
    Liu F, Wang H, Liang SN, Jin Z, Wei S, Li X, et al.
    Comput Biol Med, 2023 May;157:106790.
    PMID: 36958239 DOI: 10.1016/j.compbiomed.2023.106790
    Structural magnetic resonance imaging (sMRI) is a popular technique that is widely applied in Alzheimer's disease (AD) diagnosis. However, only a few structural atrophy areas in sMRI scans are highly associated with AD. The degree of atrophy in patients' brain tissues and the distribution of lesion areas differ among patients. Therefore, a key challenge in sMRI-based AD diagnosis is identifying discriminating atrophy features. Hence, we propose a multiplane and multiscale feature-level fusion attention (MPS-FFA) model. The model has three components, (1) A feature encoder uses a multiscale feature extractor with hybrid attention layers to simultaneously capture and fuse multiple pathological features in the sagittal, coronal, and axial planes. (2) A global attention classifier combines clinical scores and two global attention layers to evaluate the feature impact scores and balance the relative contributions of different feature blocks. (3) A feature similarity discriminator minimizes the feature similarities among heterogeneous labels to enhance the ability of the network to discriminate atrophy features. The MPS-FFA model provides improved interpretability for identifying discriminating features using feature visualization. The experimental results on the baseline sMRI scans from two databases confirm the effectiveness (e.g., accuracy and generalizability) of our method in locating pathological locations. The source code is available at https://github.com/LiuFei-AHU/MPSFFA.
    Matched MeSH terms: Image Interpretation, Computer-Assisted/methods
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