Displaying publications 1 - 20 of 163 in total

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  1. Fulltext 22q11.2 deletion syndrome in diverse populations
    Kruszka P, Addissie YA, McGinn DE, Porras AR, Biggs E, Share M, et al.
    Am J Med Genet A, 2017 Apr;173(4):879-888.
    PMID: 28328118 DOI: 10.1002/ajmg.a.38199
    22q11.2 deletion syndrome (22q11.2 DS) is the most common microdeletion syndrome and is underdiagnosed in diverse populations. This syndrome has a variable phenotype and affects multiple systems, making early recognition imperative. In this study, individuals from diverse populations with 22q11.2 DS were evaluated clinically and by facial analysis technology. Clinical information from 106 individuals and images from 101 were collected from individuals with 22q11.2 DS from 11 countries; average age was 11.7 and 47% were male. Individuals were grouped into categories of African descent (African), Asian, and Latin American. We found that the phenotype of 22q11.2 DS varied across population groups. Only two findings, congenital heart disease and learning problems, were found in greater than 50% of participants. When comparing the clinical features of 22q11.2 DS in each population, the proportion of individuals within each clinical category was statistically different except for learning problems and ear anomalies (P 
    Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
  2. A Heuristic Automatic and Robust ROI Detection Method for Medical Image Warermarking
    Mousavi SM, Naghsh A, Abu-Bakar SA
    J Digit Imaging, 2015 Aug;28(4):417-27.
    PMID: 25736857 DOI: 10.1007/s10278-015-9770-z
    This paper presents an automatic region of interest (ROI) segmentation method for application of watermarking in medical images. The advantage of using this scheme is that the proposed method is robust against different attacks such as median, Wiener, Gaussian, and sharpening filters. In other words, this technique can produce the same result for the ROI before and after these attacks. The proposed algorithm consists of three main parts; suggesting an automatic ROI detection system, evaluating the robustness of the proposed system against numerous attacks, and finally recommending an enhancement part to increase the strength of the composed system against different attacks. Results obtained from the proposed method demonstrated the promising performance of the method.
    Matched MeSH terms: Image Interpretation, Computer-Assisted*
  3. Fulltext A Robust and Fast Computation Touchless Palm Print Recognition System Using LHEAT and the IFkNCN Classifier
    Jaafar H, Ibrahim S, Ramli DA
    Comput Intell Neurosci, 2015;2015:360217.
    PMID: 26113861 DOI: 10.1155/2015/360217
    Mobile implementation is a current trend in biometric design. This paper proposes a new approach to palm print recognition, in which smart phones are used to capture palm print images at a distance. A touchless system was developed because of public demand for privacy and sanitation. Robust hand tracking, image enhancement, and fast computation processing algorithms are required for effective touchless and mobile-based recognition. In this project, hand tracking and the region of interest (ROI) extraction method were discussed. A sliding neighborhood operation with local histogram equalization, followed by a local adaptive thresholding or LHEAT approach, was proposed in the image enhancement stage to manage low-quality palm print images. To accelerate the recognition process, a new classifier, improved fuzzy-based k nearest centroid neighbor (IFkNCN), was implemented. By removing outliers and reducing the amount of training data, this classifier exhibited faster computation. Our experimental results demonstrate that a touchless palm print system using LHEAT and IFkNCN achieves a promising recognition rate of 98.64%.
    Matched MeSH terms: Image Interpretation, Computer-Assisted
  4. A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images
    Raghavendra U, Gudigar A, Bhandary SV, Rao TN, Ciaccio EJ, Acharya UR
    J Med Syst, 2019 Jul 30;43(9):299.
    PMID: 31359230 DOI: 10.1007/s10916-019-1427-x
    Glaucoma is a type of eye condition which may result in partial or consummate vision loss. Higher intraocular pressure is the leading cause for this condition. Screening for glaucoma and early detection can avert vision loss. Computer aided diagnosis (CAD) is an automated process with the potential to identify glaucoma early through quantitative analysis of digital fundus images. Preparing an effective model for CAD requires a large database. This study presents a CAD tool for the precise detection of glaucoma using a machine learning approach. An autoencoder is trained to determine effective and important features from fundus images. These features are used to develop classes of glaucoma for testing. The method achieved an F - measure value of 0.95 utilizing 1426 digital fundus images (589 control and 837 glaucoma). The efficacy of the system is evident, and is suggestive of its possible utility as an additional tool for verification of clinical decisions.
    Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
  5. 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*
  6. A computer-aided multidisease diagnostic system using MRCP
    Logeswaran R
    J Digit Imaging, 2008 Jun;21(2):235-42.
    PMID: 17345003
    Automated computer analysis of magnetic resonance cholangiopancreatography (MRCP) (a focused magnetic resonance imaging sequence for the pancreatobiliary region of the abdomen) images for biliary diseases is a difficult problem because of the large inter- and intrapatient variations in the images, varying acquisition settings, and characteristics of the images, defeating most attempts to produce computer-aided diagnosis systems. This paper proposes a system capable of automated preliminary diagnosis of several diseases affecting the bile ducts in the liver, namely, dilation, stones, tumor, and cyst. The system first identifies the biliary ductal structure present in the MRCP images, and then proceeds to determine the presence or absence of the diseases. Tested on a database of 593 clinical images, the system, which uses visual-based features, has shown to be successful in delivering good performance of 70-90% even in the presence of multiple diseases, and may be useful in aiding medical practitioners in routine MRCP examinations.
    Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
  7. A decision support system for automatic screening of non-proliferative diabetic retinopathy
    Reza AW, Eswaran C
    J Med Syst, 2011 Feb;35(1):17-24.
    PMID: 20703589 DOI: 10.1007/s10916-009-9337-y
    The increasing number of diabetic retinopathy (DR) cases world wide demands the development of an automated decision support system for quick and cost-effective screening of DR. We present an automatic screening system for detecting the early stage of DR, which is known as non-proliferative diabetic retinopathy (NPDR). The proposed system involves processing of fundus images for extraction of abnormal signs, such as hard exudates, cotton wool spots, and large plaque of hard exudates. A rule based classifier is used for classifying the DR into two classes, namely, normal and abnormal. The abnormal NPDR is further classified into three levels, namely, mild, moderate, and severe. To evaluate the performance of the proposed decision support framework, the algorithms have been tested on the images of STARE database. The results obtained from this study show that the proposed system can detect the bright lesions with an average accuracy of about 97%. The study further shows promising results in classifying the bright lesions correctly according to NPDR severity levels.
    Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
  8. Fulltext A deformable generic 3D model of haptoral anchor of Monogenean
    Teo BG, Dhillon SK, Lim LH
    PLoS One, 2013;8(10):e77650.
    PMID: 24204903 DOI: 10.1371/journal.pone.0077650
    In this paper, a digital 3D model which allows for visualisation in three dimensions and interactive manipulation is explored as a tool to help us understand the structural morphology and elucidate the functions of morphological structures of fragile microorganisms which defy live studies. We developed a deformable generic 3D model of haptoral anchor of dactylogyridean monogeneans that can subsequently be deformed into different desired anchor shapes by using direct manipulation deformation technique. We used point primitives to construct the rectangular building blocks to develop our deformable 3D model. Point primitives are manually marked on a 2D illustration of an anchor on a Cartesian graph paper and a set of Cartesian coordinates for each point primitive is manually extracted from the graph paper. A Python script is then written in Blender to construct 3D rectangular building blocks based on the Cartesian coordinates. The rectangular building blocks are stacked on top or by the side of each other following their respective Cartesian coordinates of point primitive. More point primitives are added at the sites in the 3D model where more structural variations are likely to occur, in order to generate complex anchor structures. We used Catmull-Clark subdivision surface modifier to smoothen the surface and edge of the generic 3D model to obtain a smoother and more natural 3D shape and antialiasing option to reduce the jagged edges of the 3D model. This deformable generic 3D model can be deformed into different desired 3D anchor shapes through direct manipulation deformation technique by aligning the vertices (pilot points) of the newly developed deformable generic 3D model onto the 2D illustrations of the desired shapes and moving the vertices until the desire 3D shapes are formed. In this generic 3D model all the vertices present are deployed for displacement during deformation.
    Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
  9. Fulltext A multi-resolution textural approach to diagnostic neuropathology reporting
    Fauzi MF, Gokozan HN, Elder B, Puduvalli VK, Pierson CR, Otero JJ, et al.
    J Neurooncol, 2015 Sep;124(3):393-402.
    PMID: 26255070 DOI: 10.1007/s11060-015-1872-4
    We present a computer aided diagnostic workflow focusing on two diagnostic branch points in neuropathology (intraoperative consultation and p53 status in tumor biopsy specimens) by means of texture analysis via discrete wavelet frames decomposition. For intraoperative consultation, our methodology is capable of classifying glioblastoma versus metastatic cancer by extracting textural features from the non-nuclei region of cytologic preparations based on the imaging characteristics of glial processes, which appear as anisotropic thin linear structures. For metastasis, these are homogeneous in appearance, thus suitable and extractable texture features distinguish the two tissue types. Experiments on 53 images (29 glioblastomas and 24 metastases) resulted in average accuracy as high as 89.7 % for glioblastoma, 87.5 % for metastasis and 88.7 % overall. For p53 interpretation, we detect and classify p53 status by classifying staining intensity into strong, moderate, weak and negative sub-classes. We achieved this by developing a novel adaptive thresholding for detection, a two-step rule based on weighted color and intensity for the classification of positively and negatively stained nuclei, followed by texture classification to classify the positively stained nuclei into the strong, moderate and weak intensity sub-classes. Our detection method is able to correctly locate and distinguish the four types of cells, at 85 % average precision and 88 % average sensitivity rate. These classification methods on the other hand recorded 81 % accuracy in classifying the positive and negative cells, and 60 % accuracy in further classifying the positive cells into the three intensity groups, which is comparable with neuropathologists' markings.
    Matched MeSH terms: Image Interpretation, Computer-Assisted
  10. Fulltext A new blood vessel extraction technique using edge enhancement and object classification
    Badsha S, Reza AW, Tan KG, Dimyati K
    J Digit Imaging, 2013 Dec;26(6):1107-15.
    PMID: 23515843 DOI: 10.1007/s10278-013-9585-8
    Diabetic retinopathy (DR) is increasing progressively pushing the demand of automatic extraction and classification of severity of diseases. Blood vessel extraction from the fundus image is a vital and challenging task. Therefore, this paper presents a new, computationally simple, and automatic method to extract the retinal blood vessel. The proposed method comprises several basic image processing techniques, namely edge enhancement by standard template, noise removal, thresholding, morphological operation, and object classification. The proposed method has been tested on a set of retinal images. The retinal images were collected from the DRIVE database and we have employed robust performance analysis to evaluate the accuracy. The results obtained from this study reveal that the proposed method offers an average accuracy of about 97 %, sensitivity of 99 %, specificity of 86 %, and predictive value of 98 %, which is superior to various well-known techniques.
    Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
  11. Fulltext A new human heart vessel identification, segmentation and 3D reconstruction mechanism
    Al-Surmi A, Wirza R, Mahmod R, Khalid F, Dimon MZ
    J Cardiothorac Surg, 2014;9:161.
    PMID: 25274253 DOI: 10.1186/s13019-014-0161-1
    The identification and segmentation of inhomogeneous image regions is one of the most challenging issues nowadays. The surface vessels of the human heart are important for the surgeons to locate the region where to perform the surgery and to avoid surgical injuries. In addition, such identification, segmentation, and visualisation helps novice surgeons in the training phase of cardiac surgery.
    Matched MeSH terms: Image Interpretation, Computer-Assisted*
  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. A novel shape representation of the dental arch and its applications in some dentistry problems
    Rijal OM, Abdullah NA, Isa ZM, Davaei FA, Noor NM, Tawfiq OF
    Annu Int Conf IEEE Eng Med Biol Soc, 2011;2011:5092-5.
    PMID: 22255484 DOI: 10.1109/IEMBS.2011.6091261
    Standardized digital images of maxillary dental casts of 47 subjects were analyzed using MATLAB software whereby the two hamular notches and the incisive papilla defines the Cartesian vertical and horizontal axes, as well as the origin. The angle and length of the midpoints of the anterior teeth, mesiobuccal and distobuccal cusp of the posterior teeth were measured from the origin and denoted as θ(1), …, θ(18) and l(1), …, l(18) respectively. These values were collectively used to represent the shape of each dental cast. Clustering and principal component analyses were employed to find possible groups of dental arches using the above measure of shape. The main result of this study is that the 3 groups of dental arch shape may be represented by the novel feature vector v(k) = (θ(k)(1), l(k)(1), θ(k)(3), l(k)(3), θ(k)(5), l(k)(5), θ(k)(13), l(k)(13)), k = 1, 2, 3. Knowledge of v(k) implies three impression trays should be sufficient in a particular prosthetic dentistry application for Malaysian patients. Further, given that v(k) are accurately measured they may be potential candidates as evidence in specific application of forensic dentistry.
    Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
  14. A system of microcalcifications detection and evaluation of the radiologist: comparative study of the three main races in Malaysia
    Al-Qdah M, Ramli AR, Mahmud R
    Comput Biol Med, 2005 Dec;35(10):905-14.
    PMID: 16310014
    This paper uses wavelets in the detection comparison of breast cancer among the three main races in Malaysia: Chinese, Malays, and Indians followed by a system that evaluates the radiologist's findings over a period of time to gauge the radiologist's skills in confirming breast cancer cases. The db4 wavelet has been utilized to detect microcalcifications in mammogram-digitized images obtained from Malaysian women sample. The wavelet filter's detection evaluation was done by visual inspection by an expert radiologist to confirm the detection results of those pixels that corresponded to microcalcifications. Detection was counted if the wavelet-detected pixels corresponded to the radiologist's identified microcalcification pixels. After the radiologist's detection confirmation a new client-server radiologist recording and evaluation system is designed to evaluate the findings of the radiologist over some period of cancer detection working time. It is a system that records the findings of the Malaysian radiologist for the presence of breast cancer in Malaysian patients and provides a way of registering the progress of detecting breast cancer of the radiologist by tracking certain metric values such as the sensitivity, specificity, and receiver operator curve (ROC). The initial findings suggest that no single race mammograms are easier for wavelets' detections of microcalcifications and for the radiologist confirmation even though for this study the Chinese race samples detection average were a few percentages less than the other two races, namely the Malay and Indian races.
    Matched MeSH terms: Radiographic Image Interpretation, Computer-Assisted/methods*
  15. A three-dimensional computed tomographic analysis of the cervical spine in unoperated infants with cleft lip and palate
    Rajion ZA, Townsend GC, Netherway DJ, Anderson PJ, Yusof A, Hughes T, et al.
    Cleft Palate Craniofac J, 2006 Sep;43(5):513-8.
    PMID: 16986980
    To investigate anatomical variations and abnormalities of cervical spine morphology in unoperated infants with cleft lip and palate.
    Matched MeSH terms: Image Interpretation, Computer-Assisted
  16. A videoendoscopic evaluation of the upper airway in South East Asian adults with obstructive sleep apnea
    Abdullah B, Rajet KA, Abd Hamid SS, Mohammad WM
    Sleep Breath, 2011 Dec;15(4):747-54.
    PMID: 20957444 DOI: 10.1007/s11325-010-0431-7
    OBJECTIVES: We aimed to evaluate the severity of upper airway obstruction at the retropalatal and retroglossal regions in obstructive sleep apnea (OSA) patients.

    METHODOLOGY: This is a descriptive cross-sectional study at the Sleep Clinic, Department of Otorhinolaryngology-Head and Neck Surgery. Flexible nasopharyngolaryngoscopy was performed in seated erect and supine position. Retropalatal and retroglossal regions were continuously recorded during quiet breathing and Mueller's maneuver in both positions. Captured images were measured using Scion Image software and narrowing rate was calculated. Level of each site was classified based on Fujita classification and severity of obstruction using Sher scoring system for Mueller's maneuver.

    RESULTS: A total of 59 patients participated in this study. Twenty-nine (49.2%) participants had type 1 (retropalatal) obstruction, 23 (38.9%) had type 2 (retropalatal and retroglossal), and seven (11.9%) in type 3 (retroglossal) obstruction. Fifty (84.7%) of the patients have severe obstruction at the retropalatal region in supine position (SRP) followed by 35 (59.3%) at retropalatal region in erect position (ERP), 27 (45.8%) at retroglossal region in supine position (SRG) and eight (13.5%) at retroglossal region in erect position (ERG). The average oxygen saturation showed significant association in ERP (P = 0.012) and SRP (P < 0.001), but not significant in ERG and SRG.

    CONCLUSIONS: Videoendoscopy utilizing flexible nasopharyngolaryngoscopy and Scion Image software is reliable, minimally invasive, and useful as an office procedure in evaluating the multilevel obstruction of upper airway in OSA patients. The retropalatal region has more severe obstruction compared with retroglossal region either in erect or supine position.

    Matched MeSH terms: Image Interpretation, Computer-Assisted/instrumentation
  17. Fulltext Acne analysis, grading and computational assessment methods: an overview
    Ramli R, Malik AS, Hani AF, Jamil A
    Skin Res Technol, 2012 Feb;18(1):1-14.
    PMID: 21605170 DOI: 10.1111/j.1600-0846.2011.00542.x
    INTRODUCTION: This paper presents a comprehensive review of acne grading and measurement. Acne is a chronic disorder of the pilosebaceous units, with excess sebum production, follicular epidermal hyperproliferation, inflammation and Propionibacterium acnes activity. Most patients are affected with acne vulgaris, which is the prevalent type of acne. Acne vulgaris consists of comedones (whitehead and blackhead), papules, pustules, nodules and cysts.
    OBJECTIVES: To review and identify the issues for acne vulgaris grading and computational assessment methods. To determine the future direction for addressing the identified issues.
    METHODS: There are two main methods of assessment for acne severity grading, namely, lesion counting and comparison of patient with a photographic standard. For the computational assessment method, the emphasis is on computational imaging techniques.
    RESULTS: Current acne grading methods are very time consuming and tedious. Generally, they rely on approximation for counting lesions and hence the assessment is quite subjective, with both inter and intra-observer variability. It is important to accurately assess acne grade to evaluate its severity as this influences treatment selection and assessment of response to therapy. This will further help in better disease management and more efficacious treatment.
    CONCLUSION: Semi-automated or automated methods based on computational imaging techniques should be devised for acne grade assessment.
    Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
  18. Fulltext An automated blood vessel segmentation algorithm using histogram equalization and automatic threshold selection
    Saleh MD, Eswaran C, Mueen A
    J Digit Imaging, 2011 Aug;24(4):564-72.
    PMID: 20524139 DOI: 10.1007/s10278-010-9302-9
    This paper focuses on the detection of retinal blood vessels which play a vital role in reducing the proliferative diabetic retinopathy and for preventing the loss of visual capability. The proposed algorithm which takes advantage of the powerful preprocessing techniques such as the contrast enhancement and thresholding offers an automated segmentation procedure for retinal blood vessels. To evaluate the performance of the new algorithm, experiments are conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm performs better than the other known algorithms in terms of accuracy. Furthermore, the proposed algorithm being simple and easy to implement, is best suited for fast processing applications.
    Matched MeSH terms: Image Interpretation, Computer-Assisted/methods
  19. An automated technique for carotid far wall classification using grayscale features and wall thickness variability
    Acharya UR, Sree SV, Molinari F, Saba L, Nicolaides A, Suri JS
    J Clin Ultrasound, 2015 Jun;43(5):302-11.
    PMID: 24909942 DOI: 10.1002/jcu.22183
    To test a computer-aided diagnostic method for differentiating symptomatic from asymptomatic carotid B-mode ultrasonographic images.
    Matched MeSH terms: Image Interpretation, Computer-Assisted/methods*
  20. An efficient algorithm for retinal blood vessel segmentation using h-maxima transform and multilevel thresholding
    Saleh MD, Eswaran C
    Comput Methods Biomech Biomed Engin, 2012;15(5):517-25.
    PMID: 21331960 DOI: 10.1080/10255842.2010.545949
    Retinal blood vessel detection and analysis play vital roles in early diagnosis and prevention of several diseases, such as hypertension, diabetes, arteriosclerosis, cardiovascular disease and stroke. This paper presents an automated algorithm for retinal blood vessel segmentation. The proposed algorithm takes advantage of powerful image processing techniques such as contrast enhancement, filtration and thresholding for more efficient segmentation. To evaluate the performance of the proposed algorithm, experiments were conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm yields an accuracy rate of 96.5%, which is higher than the results achieved by other known algorithms.
    Matched MeSH terms: Image Interpretation, Computer-Assisted/methods
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