Displaying publications 1 - 20 of 103 in total

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  1. Radhiana H, Azian AA, Razali MR, Kamariah CM
    Med J Malaysia, 2010 Dec;65(4):319-25.
    PMID: 21901958
    Computed tomography (CT) is widely used in assessing clinically stable patients with blunt abdominal trauma. In these patients, liver is one of the commonest organs being injured and CT can accurately identify and assess the extent of the injury. The CT features of blunt liver trauma include laceration, subcapsular or parenchymal haematomas, active haemorrhage and vascular injuries. Widespread use of CT has notably influenced the management of blunt liver injury from routine surgical to nonsurgical management. We present pictorial illustrations of various liver injuries depicted on CT in patients with blunt trauma.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  2. Rais NNM, Bradley DA, Hashim A, Osman ND, Noor NM
    Appl Radiat Isot, 2019 Nov;153:108810.
    PMID: 31351374 DOI: 10.1016/j.apradiso.2019.108810
    For a range of doses familiarly incurred in computed tomography (CT), study is made of the performance of Germanium (Ge)-doped fibre dosimeters formed into cylindrical and flat shapes. Indigenously fabricated 2.3 mol% and 6 mol% Ge-dopant concentration preforms have been used to produce flat- and cylindrical-fibres (FF and CF) of various size and diameters; an additional 4 mol% Ge-doped commercial fibre with a core diameter of 50 μm has also been used. The key characteristics examined include the linearity index f(d), dose sensitivity and minimum detectable dose (MDD), the performance of the fibres being compared against that of lithium-fluoride based TLD-100 thermoluminescence (TL) dosimeters. For doses in the range 2-40 milligray (mGy), delivered at constant potential of 120 kilovoltage (kV), both the fabricated and commercial fibres demonstrate supralinear behaviours at doses  4 mGy. In terms of dose sensitivity, all of the fibres show superior TL sensitivity when compared against TLD-100, the 2.3 mol% and 6 mol% Ge-doped FF demonstrating the greatest TL sensitivity at 84 and 87 times that of TLD-100. The TL yields for the novel Ge-doped silica glass render them appealing for use within the present medical imaging dose range, offering linearity at high sensitivity down to less than 2 mGy.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  3. Tan D, Mohamad NA, Wong YH, Yeong CH, Cheah PL, Sulaiman N, et al.
    Int J Hyperthermia, 2019;36(1):554-561.
    PMID: 31132888 DOI: 10.1080/02656736.2019.1610800
    Purpose: This study aimed to evaluate the effects of various computed tomography (CT) acquisition parameters and metal artifacts on CT number measurement for CT thermometry during CT-guided thermal ablation. Methods: The effects of tube voltage (100-140 kVp), tube current (20-250 mAs), pitch (0.6-1.5) and gantry rotation time (0.5, 1.0 s) as well as metal artifacts from a radiofrequency ablation (RFA) needle on CT number were evaluated using liver tissue equivalent polyacrylamide (PAA) phantom. The correlation between CT number and temperature from 37 to 80 °C was studied on PAA phantom using optimum CT acquisition parameters. Results: No statistical significant difference (p > 0.05) was found on CT numbers under the variation of different acquisition parameters for the same temperature setting. On the other hand, the RFA needle has induced metal artifacts on the CT images of up to 8 mm. The CT numbers decreased linearly when the phantom temperature increased from 37 to 80 °C. A linear regression analysis on the CT numbers and temperature suggested that the CT thermal sensitivity was -0.521 ± 0.061 HU/°C (R2 = 0.998). Conclusion: CT thermometry is feasible for temperature assessment during RFA with the current CT technology, which produced a high CT number reproducibility and stable measurement at different CT acquisition parameters. Despite being affected by metal artifacts, the CT-based thermometry could be further developed as a tissue temperature monitoring tool during CT-guided thermal ablation.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  4. Teh V, Sim KS, Wong EK
    Scanning, 2016 Nov;38(6):842-856.
    PMID: 27302216 DOI: 10.1002/sca.21334
    According to the statistic from World Health Organization (WHO), stroke is one of the major causes of death globally. Computed tomography (CT) scan is one of the main medical diagnosis system used for diagnosis of ischemic stroke. CT scan provides brain images in Digital Imaging and Communication in Medicine (DICOM) format. The presentation of CT brain images is mainly relied on the window setting (window center and window width), which converts an image from DICOM format into normal grayscale format. Nevertheless, the ordinary window parameter could not deliver a proper contrast on CT brain images for ischemic stroke detection. In this paper, a new proposed method namely gamma correction extreme-level eliminating with weighting distribution (GCELEWD) is implemented to improve the contrast on CT brain images. GCELEWD is capable of highlighting the hypodense region for diagnosis of ischemic stroke. The performance of this new proposed technique, GCELEWD, is compared with four of the existing contrast enhancement technique such as brightness preserving bi-histogram equalization (BBHE), dualistic sub-image histogram equalization (DSIHE), extreme-level eliminating histogram equalization (ELEHE), and adaptive gamma correction with weighting distribution (AGCWD). GCELEWD shows better visualization for ischemic stroke detection and higher values with image quality assessment (IQA) module. SCANNING 38:842-856, 2016. © 2016 Wiley Periodicals, Inc.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  5. Joshi SC, Pant I, Hamzah F, Kumar G, Shukla AN
    Indian J Cancer, 2008 12 30;45(4):137-41.
    PMID: 19112200
    Positron emission tomography (PET) has emerged as an important diagnostic tool in the management of lung cancers. Although PET is sensitive in detection of lung cancer, but FDG (2-deoxy-2- 18 fluro-D-glucose) is not tumor specific and may accumulate in a variety of nonmalignant conditions occasionally giving false positive result. Addition of CT to PET improves specificity foremost, but also sensitivity in tumor imaging. Thus, PET/CT fusion images are a more accurate test than either of its individual components and are probably also better than side-by-side viewing of images from both modalities. PET/CT fusion images are useful in differentiating between malignant and benign disease, fibrosis and recurrence, staging and in changing patient management to more appropriate therapy. With analysis and discussion it appears that PET/ CT fusion images have the potential to dramatically improve our ability to manage the patients with lung cancer and is contributing to our understanding of cancer cell biology and in development of new therapies.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  6. 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: Tomography, X-Ray Computed/methods*
  7. Alirr OI, Rahni AAA, Golkar E
    Int J Comput Assist Radiol Surg, 2018 Aug;13(8):1169-1176.
    PMID: 29860549 DOI: 10.1007/s11548-018-1801-z
    PURPOSE: Segmentation of liver tumours is an important part of the 3D visualisation of the liver anatomy for surgical planning. The spatial relationship between tumours and other structures inside the liver forms the basis of preoperative surgical risk assessment. However, the automatic segmentation of liver tumours from abdominal CT scans is riddled with challenges. Tumours located at the border of the liver impose a big challenge as the surrounding tissues could have similar intensities.

    METHODS: In this work, we introduce a fully automated liver tumour segmentation approach in contrast-enhanced CT datasets. The method is a multi-stage technique which starts with contrast enhancement of the tumours using anisotropic filtering, followed by adaptive thresholding to extract the initial mask of the tumours from an identified liver region of interest. Localised level set-based active contours are used to extend the mask to the tumour boundaries.

    RESULTS: The proposed method is validated on the IRCAD database with pathologies that offer highly variable and complex liver tumours. The results are compared quantitatively to the ground truth, which is delineated by experts. We achieved an average dice similarity coefficient of 75% over all patients with liver tumours in the database with overall absolute relative volume difference of 11%. This is comparable to other recent works, which include semiautomated methods, although they were validated on different datasets.

    CONCLUSIONS: The proposed approach aims to segment tumours inside the liver envelope automatically with a level of accuracy adequate for its use as a tool for surgical planning using abdominal CT images. The approach will be validated on larger datasets in the future.

    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  8. Sabrina B, Tan KL, Johann FK, Andre D
    Med J Malaysia, 2018 08;73(4):255-256.
    PMID: 30121691 MyJurnal
    Ureteric and bladder injuries are uncommon, difficult to diagnose and rarely occur in isolation. Diagnosis is often delayed or missed at presentation. Therefore, high clinical suspicion and appropriate timing of computed tomography (CT) are of paramount importance. We report two cases (ureteropelvic junction avulsion and ruptured dome of bladder) whereby the presentations were subtle and would have been missed if not for high clinical suspicion. This article discusses the problems associated with these urologic injuries, as well as how to develop a high index of suspicion based on the pattern of anatomical disruption, mechanism of injury, physiological abnormality and comorbidity.
    Matched MeSH terms: Tomography, X-Ray Computed/methods
  9. Sulong S, Alias A, Johanabas F, Yap Abdullah J, Idris B
    J Craniofac Surg, 2019 8 14;31(1):46-50.
    PMID: 31403510 DOI: 10.1097/SCS.0000000000005810
    BACKGROUND: Craniosynostosis is a congenital defect that causes ≥1 suture to fuse prematurely. Cranial expansion surgery which consists of cranial vault reshaping with or without fronto-orbital advancement (FOA) is done to correct the skull to a more normal shape of the head as well as to increase the intracranial volume (ICV). Therefore, it is important to evaluate the changes of ICV after the surgery and the effect of surgery both clinically and radiologically.

    OBJECTIVE: The aim of this study is to evaluate the ICV in primary craniosynostosis patients after the cranial vault reshaping with or without FOA and to compare between syndromic and nonsyndromic synostosis group, to determine factors that associated with significant changes in the ICV postoperative, and to evaluate the resolution of copper beaten sign and improvement in neurodevelopmental delay after the surgery.

    METHODS: This is a prospective observational study of all primary craniosynostosis patients who underwent operation cranial vault reshaping with or without FOA in Hospital Kuala Lumpur from January 2017 until Jun 2018. The ICV preoperative and postoperative was measured using the 3D computed tomography (CT) imaging and analyzed. The demographic data, clinical and radiological findings were identified and analyzed.

    RESULTS: A total of 14 cases (6 males and 8 females) with 28 3D CT scans were identified. The mean age of patients was 23 months. Seven patients were having syndromic synostosis (4 Crouzon syndromes and 3 Apert syndromes) and 7 nonsyndromic synostosis. The mean preoperative ICV was 880 mL (range, 641-1234 mL), whereas the mean postoperative ICV was 1081 mL (range, 811-1385 mL). The difference was 201 mL which was statistically significant (P  1.0). However, there was 100% (n = 13) improvement of this copper beaten sign. However, the neurodevelopmental delay showed no improvement which was statistically not significant (P > 1.0).

    CONCLUSION: Surgery in craniosynostosis patient increases the ICV besides it improves the shape of the head. From this study, the syndromic synostosis had better increment of ICV compared to nonsyndromic synostosis.

    Matched MeSH terms: Tomography, X-Ray Computed/methods
  10. Ng BH, Nuratiqah NA, Andrea YLB, Faisal AH, Soo CI, Najma K, et al.
    Med J Malaysia, 2020 07;75(4):368-371.
    PMID: 32723996
    BACKGROUND AND OBJECTIVE: Coronavirus Disease 2019 (COVID- 19) was first reported in Malaysia in March 2020. We describe here the clinical characteristics and computed tomography (CT) patterns in asymptomatic young patients who had laboratory-confirmed COVID-19.

    METHODS: This is a retrospective observational study where 25 male in-patients with laboratory-confirmed COVID-19 in Hospital Canselor Tuanku Muhriz. Demographics, clinical data and CT images of these patients were reviewed by 2 senior radiologists.

    RESULTS: In total there were 25 patients (all males; mean age [±SD], 21.64±2.40 years; range, 18-27 years). Patients with abnormal chest CT showed a relatively low normal absolute lymphocytes count (median: 2.2 x 109/L) and absolute monocyte count (median: 0.5 x 109/L). Lactate dehydrogenase was elevated in 5 (20%) of the patients. The procalcitonin level was normal while elevated levels of alanine aminotransferase, total bilirubin, platelet and C-reactive protein were common. Baseline chest CT showed abnormalities in 6 patients. The distribution of the lesions were; upper lobe 3 (12%) lower lobe 3 (12%) with peripheral distribution 4 (16%). Of the 25 patients included, 4 (16%) had ground glass opacification (GGO), 1 (4%) had a small peripheral subpleural nodule, and 1 (4%) had a dense solitary granuloma. Four patients had typical CT features of COVID-19.

    CONCLUSION: We found that the CT imaging showed peripheral GGO in our patients. They remained clinically stable with no deterioration of their respiratory symptoms suggesting stability in lung involvement. We postulate that rapid changes in CT imaging may not be present in young, asymptomatic, non-smoking COVID-19 patients. Thus the use of CT thoraxfor early diagnosis may be reserved for patients in the older agegroups, and not in younger patients.

    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  11. Al-Shabi M, Lan BL, Chan WY, Ng KH, Tan M
    Int J Comput Assist Radiol Surg, 2019 Oct;14(10):1815-1819.
    PMID: 31020576 DOI: 10.1007/s11548-019-01981-7
    PURPOSE: Lung nodules have very diverse shapes and sizes, which makes classifying them as benign/malignant a challenging problem. In this paper, we propose a novel method to predict the malignancy of nodules that have the capability to analyze the shape and size of a nodule using a global feature extractor, as well as the density and structure of the nodule using a local feature extractor.

    METHODS: We propose to use Residual Blocks with a 3 × 3 kernel size for local feature extraction and Non-Local Blocks to extract the global features. The Non-Local Block has the ability to extract global features without using a huge number of parameters. The key idea behind the Non-Local Block is to apply matrix multiplications between features on the same feature maps.

    RESULTS: We trained and validated the proposed method on the LIDC-IDRI dataset which contains 1018 computed tomography scans. We followed a rigorous procedure for experimental setup, namely tenfold cross-validation, and ignored the nodules that had been annotated by

    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  12. Nazimi AJ, Khoo SC, Nabil S, Nordin R, Lan TH, Rajandram RK, et al.
    J Craniofac Surg, 2019 Oct;30(7):2159-2162.
    PMID: 31232997 DOI: 10.1097/SCS.0000000000005667
    Orbital fractures pose specific challenge in its surgical management. One of the greatest challenges is to obtain satisfactory reconstruction by correct positioning of orbital implant. Intraoperative computed tomography (CT) scan may facilitate this procedure. The aim of this study was to describe the early use of intraoperative CT in orbital fractures repair in our center. The authors assessed the revision types and rates that have occurred with this technique. With the use of pre-surgical planning, optical intraoperative navigation, and intraoperative CT, the impact of intraoperative CT on the management of 5 cases involving a total number of 14 orbital wall fractures were described. There were 6 pure orbital blowout wall fractures reconstructed, involving both medial and inferior wall of the orbit fracturing the transition zone and 8 impure orbital wall fractures in orbitozygomaticomaxillary complex fracture. 4 patients underwent primary and 1 had delayed orbital reconstruction. Intraoperative CT resulted in intraoperative orbital implant revision, following final navigation planning position, in 40% (2/5) of patients or 14% (2/14) of the fractures. In revised cases, both implant repositioning was conducted at posterior ledge of orbit. Intraoperative CT confirmed true to original reconstruction of medial wall, inferior wall and transition zone of the orbit. Two selected cases were illustrated. In conclusion, intraoperative CT allows real-time assessment of fracture reduction and immediate orbital implant revision, especially at posterior ledge. As a result, no postoperative imaging was indicated in any of the patients. Long-term follow-ups for orbital fracture patients managed with intraoperative CT is suggested.
    Matched MeSH terms: Tomography, X-Ray Computed/methods
  13. Dong X, Xu S, Liu Y, Wang A, Saripan MI, Li L, et al.
    Cancer Imaging, 2020 Aug 01;20(1):53.
    PMID: 32738913 DOI: 10.1186/s40644-020-00331-0
    BACKGROUND: Convolutional neural networks (CNNs) have been extensively applied to two-dimensional (2D) medical image segmentation, yielding excellent performance. However, their application to three-dimensional (3D) nodule segmentation remains a challenge.

    METHODS: In this study, we propose a multi-view secondary input residual (MV-SIR) convolutional neural network model for 3D lung nodule segmentation using the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset of chest computed tomography (CT) images. Lung nodule cubes are prepared from the sample CT images. Further, from the axial, coronal, and sagittal perspectives, multi-view patches are generated with randomly selected voxels in the lung nodule cubes as centers. Our model consists of six submodels, which enable learning of 3D lung nodules sliced into three views of features; each submodel extracts voxel heterogeneity and shape heterogeneity features. We convert the segmentation of 3D lung nodules into voxel classification by inputting the multi-view patches into the model and determine whether the voxel points belong to the nodule. The structure of the secondary input residual submodel comprises a residual block followed by a secondary input module. We integrate the six submodels to classify whether voxel points belong to nodules, and then reconstruct the segmentation image.

    RESULTS: The results of tests conducted using our model and comparison with other existing CNN models indicate that the MV-SIR model achieves excellent results in the 3D segmentation of pulmonary nodules, with a Dice coefficient of 0.926 and an average surface distance of 0.072.

    CONCLUSION: our MV-SIR model can accurately perform 3D segmentation of lung nodules with the same segmentation accuracy as the U-net model.

    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  14. Kundu R, Basak H, Singh PK, Ahmadian A, Ferrara M, Sarkar R
    Sci Rep, 2021 07 08;11(1):14133.
    PMID: 34238992 DOI: 10.1038/s41598-021-93658-y
    COVID-19 has crippled the world's healthcare systems, setting back the economy and taking the lives of several people. Although potential vaccines are being tested and supplied around the world, it will take a long time to reach every human being, more so with new variants of the virus emerging, enforcing a lockdown-like situation on parts of the world. Thus, there is a dire need for early and accurate detection of COVID-19 to prevent the spread of the disease, even more. The current gold-standard RT-PCR test is only 71% sensitive and is a laborious test to perform, leading to the incapability of conducting the population-wide screening. To this end, in this paper, we propose an automated COVID-19 detection system that uses CT-scan images of the lungs for classifying the same into COVID and Non-COVID cases. The proposed method applies an ensemble strategy that generates fuzzy ranks of the base classification models using the Gompertz function and fuses the decision scores of the base models adaptively to make the final predictions on the test cases. Three transfer learning-based convolutional neural network models are used, namely VGG-11, Wide ResNet-50-2, and Inception v3, to generate the decision scores to be fused by the proposed ensemble model. The framework has been evaluated on two publicly available chest CT scan datasets achieving state-of-the-art performance, justifying the reliability of the model. The relevant source codes related to the present work is available in: GitHub.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  15. Tawfiq Zyoud TY, Abdul Rashid SN, Suppiah S, Abdul Rahim E, Mahmud R
    Med J Malaysia, 2020 07;75(4):411-418.
    PMID: 32724006
    INTRODUCTION: Autopsy is one of the most important approaches to identify clearly the exact cause of death, whether it was due to natural causes, sudden death, or traumatic. Various studies have been done in different countries regarding ways to improve the diagnosis during autopsy. The imaging approach is one of the methods that has been used to complement autopsy findings and to enhance the diagnosis for achieving the most accurate post-mortem diagnosis. The aim of this study is to identify the role of imaging modalities that complement routine autopsy and correlate the findings of diagnostic imaging that can help improve the accuracy of diagnosing the cause of death.

    METHODS: We sourced articles from Scopus, Ovid and PubMed databases for journal publications related to post-mortem diagnostic imaging. We highlight the most relevant full articles in English that explain the type of modality that was utilised and the added value it provided for diagnosing the cause of death.

    RESULTS: Minimally invasive autopsies assisted by imaging modalities added a great benefit to forensic medicine, and supported conventional autopsy. In particular the role of post mortem computed tomography (PMCT), post mortem computed tomography angiography (PMMR) and positron emission tomography computed tomography (PMCTA) that have incremental benefits in diagnosing traumatic death, fractures, tissue injuries, as well as the assessment of body height or weight for corpse identification.

    CONCLUSION: PMCT and PMMR, with particular emphasis on PMCTA, can provide higher accuracy than the other modalities. They can be regarded as indispensable methods that should be applied to the routine autopsy protocol, thus improving the findings and accuracy of diagnosing the cause of death.

    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  16. Saha P, Mukherjee D, Singh PK, Ahmadian A, Ferrara M, Sarkar R
    Sci Rep, 2021 04 15;11(1):8304.
    PMID: 33859222 DOI: 10.1038/s41598-021-87523-1
    COVID-19, a viral infection originated from Wuhan, China has spread across the world and it has currently affected over 115 million people. Although vaccination process has already started, reaching sufficient availability will take time. Considering the impact of this widespread disease, many research attempts have been made by the computer scientists to screen the COVID-19 from Chest X-Rays (CXRs) or Computed Tomography (CT) scans. To this end, we have proposed GraphCovidNet, a Graph Isomorphic Network (GIN) based model which is used to detect COVID-19 from CT-scans and CXRs of the affected patients. Our proposed model only accepts input data in the form of graph as we follow a GIN based architecture. Initially, pre-processing is performed to convert an image data into an undirected graph to consider only the edges instead of the whole image. Our proposed GraphCovidNet model is evaluated on four standard datasets: SARS-COV-2 Ct-Scan dataset, COVID-CT dataset, combination of covid-chestxray-dataset, Chest X-Ray Images (Pneumonia) dataset and CMSC-678-ML-Project dataset. The model shows an impressive accuracy of 99% for all the datasets and its prediction capability becomes 100% accurate for the binary classification problem of detecting COVID-19 scans. Source code of this work can be found at GitHub-link .
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  17. Chen Z, Rajamanickam L, Cao J, Zhao A, Hu X
    PLoS One, 2021;16(12):e0260758.
    PMID: 34879097 DOI: 10.1371/journal.pone.0260758
    This study aims to solve the overfitting problem caused by insufficient labeled images in the automatic image annotation field. We propose a transfer learning model called CNN-2L that incorporates the label localization strategy described in this study. The model consists of an InceptionV3 network pretrained on the ImageNet dataset and a label localization algorithm. First, the pretrained InceptionV3 network extracts features from the target dataset that are used to train a specific classifier and fine-tune the entire network to obtain an optimal model. Then, the obtained model is used to derive the probabilities of the predicted labels. For this purpose, we introduce a squeeze and excitation (SE) module into the network architecture that augments the useful feature information, inhibits useless feature information, and conducts feature reweighting. Next, we perform label localization to obtain the label probabilities and determine the final label set for each image. During this process, the number of labels must be determined. The optimal K value is obtained experimentally and used to determine the number of predicted labels, thereby solving the empty label set problem that occurs when the predicted label values of images are below a fixed threshold. Experiments on the Corel5k multilabel image dataset verify that CNN-2L improves the labeling precision by 18% and 15% compared with the traditional multiple-Bernoulli relevance model (MBRM) and joint equal contribution (JEC) algorithms, respectively, and it improves the recall by 6% compared with JEC. Additionally, it improves the precision by 20% and 11% compared with the deep learning methods Weight-KNN and adaptive hypergraph learning (AHL), respectively. Although CNN-2L fails to improve the recall compared with the semantic extension model (SEM), it improves the comprehensive index of the F1 value by 1%. The experimental results reveal that the proposed transfer learning model based on a label localization strategy is effective for automatic image annotation and substantially boosts the multilabel image annotation performance.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  18. Liew TS, Schilthuizen M
    PLoS One, 2016;11(6):e0157069.
    PMID: 27280463 DOI: 10.1371/journal.pone.0157069
    Quantitative analysis of organismal form is an important component for almost every branch of biology. Although generally considered an easily-measurable structure, the quantification of gastropod shell form is still a challenge because many shells lack homologous structures and have a spiral form that is difficult to capture with linear measurements. In view of this, we adopt the idea of theoretical modelling of shell form, in which the shell form is the product of aperture ontogeny profiles in terms of aperture growth trajectory that is quantified as curvature and torsion, and of aperture form that is represented by size and shape. We develop a workflow for the analysis of shell forms based on the aperture ontogeny profile, starting from the procedure of data preparation (retopologising the shell model), via data acquisition (calculation of aperture growth trajectory, aperture form and ontogeny axis), and data presentation (qualitative comparison between shell forms) and ending with data analysis (quantitative comparison between shell forms). We evaluate our methods on representative shells of the genera Opisthostoma and Plectostoma, which exhibit great variability in shell form. The outcome suggests that our method is a robust, reproducible, and versatile approach for the analysis of shell form. Finally, we propose several potential applications of our methods in functional morphology, theoretical modelling, taxonomy, and evolutionary biology.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  19. Anderson PJ, Yong R, Surman TL, Rajion ZA, Ranjitkar S
    Aust Dent J, 2014 Jun;59 Suppl 1:174-85.
    PMID: 24611727 DOI: 10.1111/adj.12154
    Following the invention of the first computed tomography (CT) scanner in the early 1970s, many innovations in three-dimensional (3D) diagnostic imaging technology have occurred, leading to a wide range of applications in craniofacial clinical practice and research. Three-dimensional image analysis provides superior and more detailed information compared with conventional plain two-dimensional (2D) radiography, with the added benefit of 3D printing for preoperative treatment planning and regenerative therapy. Current state-of-the-art multidetector CT (MDCT), also known as medical CT, has an important role in the diagnosis and management of craniofacial injuries and pathology. Three-dimensional cone beam CT (CBCT), pioneered in the 1990s, is gaining increasing popularity in dental and craniofacial clinical practice because of its faster image acquisition at a lower radiation dose, but sound guidelines are needed to ensure its optimal clinical use. Recent innovations in micro-computed tomography (micro-CT) have revolutionized craniofacial biology research by enabling higher resolution scanning of teeth beyond the capabilities of MDCT and CBCT, presenting new prospects for translational clinical research. Even after four decades of refinement, CT technology continues to advance and broaden the horizons of craniofacial clinical practice and phenomics research.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  20. Sabarudin A, Yusof MZ, Mohamad M, Sun Z
    Radiat Prot Dosimetry, 2014 Dec;162(3):316-21.
    PMID: 24255172 DOI: 10.1093/rpd/nct280
    A study on the radiation dose associated with cerebral CT angiography (CTA) and CT perfusion (CTP) was conducted on an anthropomorphic phantom with the aim of estimating the effective dose (E) and entrance skin dose (ESD) in the eyes and thyroid gland during different CTA and CTP protocols. The E was calculated to be 0.61 and 0.28 mSv in CTA with 100 and 80 kV(p), respectively. In contrast, CTP resulted in an estimated E of 2.74 and 2.07 mSv corresponding to 40 and 30 s protocols, respectively. The eyes received a higher ESD than the thyroid gland in all of these protocols. The results of this study indicate that combining both CTA and CTP procedures are not recommended in the stroke evaluation due to high radiation dose. Application of modified techniques in CTA (80 kV(p)) and CTP (30 s) is highly recommended in clinical practice for further radiation dose reduction.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
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