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  1. CT Reconstruction Algorithm and Low Contrast Detectability of Phantom Study: A Systematic Review and Meta-Analysis
    Adibah Yusof NA, Abdul Karim MK, Asikin NM, Paiman S, Awang Kechik MM, Abdul Rahman MA, et al.
    Curr Med Imaging, 2023;19(10):1105-1113.
    PMID: 35975862 DOI: 10.2174/1573405618666220816160544
    BACKGROUND: For almost three decades, computed tomography (CT) has been extensively used in medical diagnosis, which led researchers to conduct linking of CT dose exposure with image quality.

    METHODS: In this study, a systematic review and a meta-analysis study were conducted on CT phantom for resolution study especially based on the low contrast detectability (LCD). Furthermore, the association between the CT parameter such as tube voltage and the type of reconstruction algorithm, the amount of phantom scanning affecting the image quality and the exposure dose were also investigated in this study. We utilize PubMed, ScienceDirect, Google Scholar and Scopus databases to search related published articles from the year 2011 until 2020. The notable keywords comprise "computed tomography", "CT phantom", and "low contrast detectability". Of 52 articles, 20 articles are within the inclusion criteria in this systematic review.

    RESULTS: The dichotomous outcomes were chosen to represent the results in terms of risk ratio as per meta-analysis study. Notably, the noise in iterative reconstruction (IR) reduced by 24%, 33% and 36% with the use of smooth, medium and sharp filters, respectively. Furthermore, adaptive iterative dose reduction (AIDR 3D) improved image quality and the visibility of smaller less dense objects compared to filtered back-projection. Most of the researchers used 120 kVp tube voltage to scan phantom for quality assurance study.

    CONCLUSION: Hence, optimizing primary factors such as tube potential reduces the dose exposure significantly, and the optimized IR technique could substantially reduce the radiation dose while maintaining the image quality.

    Matched MeSH terms: Radiographic Image Interpretation, Computer-Assisted/methods
  2. 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*
  3. Fulltext MRFGRO: a hybrid meta-heuristic feature selection method for screening COVID-19 using deep features
    Dey A, Chattopadhyay S, Singh PK, Ahmadian A, Ferrara M, Senu N, et al.
    Sci Rep, 2021 Dec 15;11(1):24065.
    PMID: 34911977 DOI: 10.1038/s41598-021-02731-z
    COVID-19 is a respiratory disease that causes infection in both lungs and the upper respiratory tract. The World Health Organization (WHO) has declared it a global pandemic because of its rapid spread across the globe. The most common way for COVID-19 diagnosis is real-time reverse transcription-polymerase chain reaction (RT-PCR) which takes a significant amount of time to get the result. Computer based medical image analysis is more beneficial for the diagnosis of such disease as it can give better results in less time. Computed Tomography (CT) scans are used to monitor lung diseases including COVID-19. In this work, a hybrid model for COVID-19 detection has developed which has two key stages. In the first stage, we have fine-tuned the parameters of the pre-trained convolutional neural networks (CNNs) to extract some features from the COVID-19 affected lungs. As pre-trained CNNs, we have used two standard CNNs namely, GoogleNet and ResNet18. Then, we have proposed a hybrid meta-heuristic feature selection (FS) algorithm, named as Manta Ray Foraging based Golden Ratio Optimizer (MRFGRO) to select the most significant feature subset. The proposed model is implemented over three publicly available datasets, namely, COVID-CT dataset, SARS-COV-2 dataset, and MOSMED dataset, and attains state-of-the-art classification accuracies of 99.15%, 99.42% and 95.57% respectively. Obtained results confirm that the proposed approach is quite efficient when compared to the local texture descriptors used for COVID-19 detection from chest CT-scan images.
    Matched MeSH terms: Radiographic Image Interpretation, Computer-Assisted/methods*
  4. Radiation dose and diagnostic image quality associated with iterative reconstruction in coronary CT angiography: A systematic review
    Abdullah KA, McEntee MF, Reed W, Kench PL
    J Med Imaging Radiat Oncol, 2016 Aug;60(4):459-68.
    PMID: 27241506 DOI: 10.1111/1754-9485.12473
    The aim of this systematic review is to evaluate the radiation dose reduction achieved using iterative reconstruction (IR) compared to filtered back projection (FBP) in coronary CT angiography (CCTA) and assess the impact on diagnostic image quality. A systematic search of seven electronic databases was performed to identify all studies using a developed keywords strategy. A total of 14 studies met the criteria and were included in a review analysis. The results showed that there was a significant reduction in radiation dose when using IR compared to FBP (P  0.05). The mean ± SD difference of image noise, signal-noise ratio (SNR) and contrast-noise ratio (CNR) were 1.05 ± 1.29 HU, 0.88 ± 0.56 and 0.63 ± 1.83 respectively. The mean ± SD percentages of overall image quality scores were 71.79 ± 12.29% (FBP) and 67.31 ± 22.96% (IR). The mean ± SD percentages of coronary segment analysis were 95.43 ± 2.57% (FBP) and 97.19 ± 2.62% (IR). In conclusion, this review analysis shows that CCTA with the use of IR leads to a significant reduction in radiation dose as compared to the use of FBP. Diagnostic image quality of IR at reduced dose (30-41%) is comparable to FBP at standard dose in the diagnosis of CAD.
    Matched MeSH terms: Radiographic Image Interpretation, Computer-Assisted/methods*
  5. Vision 20/20: Mammographic breast density and its clinical applications
    Ng KH, Lau S
    Med Phys, 2015 Dec;42(12):7059-77.
    PMID: 26632060 DOI: 10.1118/1.4935141
    Breast density is a strong predictor of the failure of mammography screening to detect breast cancer and is a strong predictor of the risk of developing breast cancer. The many imaging options that are now available for imaging dense breasts show great promise, but there is still the question of determining which women are "dense" and what imaging modality is suitable for individual women. To date, mammographic breast density has been classified according to the Breast Imaging-Reporting and Data System (BI-RADS) categories from visual assessment, but this is known to be very subjective. Despite many research reports, the authors believe there has been a lack of physics-led and evidence-based arguments about what breast density actually is, how it should be measured, and how it should be used. In this paper, the authors attempt to start correcting this situation by reviewing the history of breast density research and the debates generated by the advocacy movement. The authors review the development of breast density estimation from pattern analysis to area-based analysis, and the current automated volumetric breast density (VBD) analysis. This is followed by a discussion on seeking the ground truth of VBD and mapping volumetric methods to BI-RADS density categories. The authors expect great improvement in VBD measurements that will satisfy the needs of radiologists, epidemiologists, surgeons, and physicists. The authors believe that they are now witnessing a paradigm shift toward personalized breast screening, which is going to see many more cancers being detected early, with the use of automated density measurement tools as an important component.
    Matched MeSH terms: Radiographic Image Interpretation, Computer-Assisted/methods*
  6. Automatic lung segmentation using control feedback system: morphology and texture paradigm
    Noor NM, Than JC, Rijal OM, Kassim RM, Yunus A, Zeki AA, et al.
    J Med Syst, 2015 Mar;39(3):22.
    PMID: 25666926 DOI: 10.1007/s10916-015-0214-6
    Interstitial Lung Disease (ILD) encompasses a wide array of diseases that share some common radiologic characteristics. When diagnosing such diseases, radiologists can be affected by heavy workload and fatigue thus decreasing diagnostic accuracy. Automatic segmentation is the first step in implementing a Computer Aided Diagnosis (CAD) that will help radiologists to improve diagnostic accuracy thereby reducing manual interpretation. Automatic segmentation proposed uses an initial thresholding and morphology based segmentation coupled with feedback that detects large deviations with a corrective segmentation. This feedback is analogous to a control system which allows detection of abnormal or severe lung disease and provides a feedback to an online segmentation improving the overall performance of the system. This feedback system encompasses a texture paradigm. In this study we studied 48 males and 48 female patients consisting of 15 normal and 81 abnormal patients. A senior radiologist chose the five levels needed for ILD diagnosis. The results of segmentation were displayed by showing the comparison of the automated and ground truth boundaries (courtesy of ImgTracer™ 1.0, AtheroPoint™ LLC, Roseville, CA, USA). The left lung's performance of segmentation was 96.52% for Jaccard Index and 98.21% for Dice Similarity, 0.61 mm for Polyline Distance Metric (PDM), -1.15% for Relative Area Error and 4.09% Area Overlap Error. The right lung's performance of segmentation was 97.24% for Jaccard Index, 98.58% for Dice Similarity, 0.61 mm for PDM, -0.03% for Relative Area Error and 3.53% for Area Overlap Error. The segmentation overall has an overall similarity of 98.4%. The segmentation proposed is an accurate and fully automated system.
    Matched MeSH terms: Radiographic Image Interpretation, Computer-Assisted/methods*
  7. Fulltext Automatic medical X-ray image classification using annotation
    Zare MR, Mueen A, Seng WC
    J Digit Imaging, 2014 Feb;27(1):77-89.
    PMID: 24092327 DOI: 10.1007/s10278-013-9637-0
    The demand for automatically classification of medical X-ray images is rising faster than ever. In this paper, an approach is presented to gain high accuracy rate for those classes of medical database with high ratio of intraclass variability and interclass similarities. The classification framework was constructed via annotation using the following three techniques: annotation by binary classification, annotation by probabilistic latent semantic analysis, and annotation using top similar images. Next, final annotation was constructed by applying ranking similarity on annotated keywords made by each technique. The final annotation keywords were then divided into three levels according to the body region, specific bone structure in body region as well as imaging direction. Different weights were given to each level of the keywords; they are then used to calculate the weightage for each category of medical images based on their ground truth annotation. The weightage computed from the generated annotation of query image was compared with the weightage of each category of medical images, and then the query image would be assigned to the category with closest weightage to the query image. The average accuracy rate reported is 87.5 %.
    Matched MeSH terms: Radiographic Image Interpretation, Computer-Assisted/methods*
  8. Fulltext Computer aided detection of breast density and mass, and visualization of other breast anatomical regions on mammograms using graph cuts
    Saidin N, Mat Sakim HA, Ngah UK, Shuaib IL
    Comput Math Methods Med, 2013;2013:205384.
    PMID: 24106523 DOI: 10.1155/2013/205384
    Breast cancer mostly arises from the glandular (dense) region of the breast. Consequently, breast density has been found to be a strong indicator for breast cancer risk. Therefore, there is a need to develop a system which can segment or classify dense breast areas. In a dense breast, the sensitivity of mammography for the early detection of breast cancer is reduced. It is difficult to detect a mass in a breast that is dense. Therefore, a computerized method to separate the existence of a mass from the glandular tissues becomes an important task. Moreover, if the segmentation results provide more precise demarcation enabling the visualization of the breast anatomical regions, it could also assist in the detection of architectural distortion or asymmetry. This study attempts to segment the dense areas of the breast and the existence of a mass and to visualize other breast regions (skin-air interface, uncompressed fat, compressed fat, and glandular) in a system. The graph cuts (GC) segmentation technique is proposed. Multiselection of seed labels has been chosen to provide the hard constraint for segmentation of the different parts. The results are promising. A strong correlation (r = 0.93) was observed between the segmented dense breast areas detected and radiological ground truth.
    Matched MeSH terms: Radiographic Image Interpretation, Computer-Assisted/methods*
  9. Using autoencoders for mammogram compression
    Tan CC, Eswaran C
    J Med Syst, 2011 Feb;35(1):49-58.
    PMID: 20703586 DOI: 10.1007/s10916-009-9340-3
    This paper presents the results obtained for medical image compression using autoencoder neural networks. Since mammograms (medical images) are usually of big sizes, training of autoencoders becomes extremely tedious and difficult if the whole image is used for training. We show in this paper that the autoencoders can be trained successfully by using image patches instead of the whole image. The compression performances of different types of autoencoders are compared based on two parameters, namely mean square error and structural similarity index. It is found from the experimental results that the autoencoder which does not use Restricted Boltzmann Machine pre-training yields better results than those which use this pre-training method.
    Matched MeSH terms: Radiographic Image Interpretation, Computer-Assisted/methods*
  10. Breast cancer diagnosis in digital mammogram using multiscale curvelet transform
    Eltoukhy MM, Faye I, Samir BB
    Comput Med Imaging Graph, 2010 Jun;34(4):269-76.
    PMID: 20004076 DOI: 10.1016/j.compmedimag.2009.11.002
    This paper presents an approach for breast cancer diagnosis in digital mammogram using curvelet transform. After decomposing the mammogram images in curvelet basis, a special set of the biggest coefficients is extracted as feature vector. The Euclidean distance is then used to construct a supervised classifier. The experimental results gave a 98.59% classification accuracy rate, which indicate that curvelet transformation is a promising tool for analysis and classification of digital mammograms.
    Matched MeSH terms: Radiographic Image Interpretation, Computer-Assisted/methods*
  11. Automatic multilevel medical image annotation and retrieval
    Mueen A, Zainuddin R, Baba MS
    J Digit Imaging, 2008 Sep;21(3):290-5.
    PMID: 17846834
    Image retrieval at the semantic level mostly depends on image annotation or image classification. Image annotation performance largely depends on three issues: (1) automatic image feature extraction; (2) a semantic image concept modeling; (3) algorithm for semantic image annotation. To address first issue, multilevel features are extracted to construct the feature vector, which represents the contents of the image. To address second issue, domain-dependent concept hierarchy is constructed for interpretation of image semantic concepts. To address third issue, automatic multilevel code generation is proposed for image classification and multilevel image annotation. We make use of the existing image annotation to address second and third issues. Our experiments on a specific domain of X-ray images have given encouraging results.
    Matched MeSH terms: Radiographic Image Interpretation, Computer-Assisted/methods*
  12. 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*
  13. Fulltext Diagnostic Efficacy of Synthesized 2D Digital Breast Tomosynthesis in Multi-ethnic Malaysian Population
    Mumin NA, Rahmat K, Fadzli F, Ramli MT, Westerhout CJ, Ramli N, et al.
    Sci Rep, 2019 02 06;9(1):1459.
    PMID: 30728394 DOI: 10.1038/s41598-018-37451-4
    Synthesized 2D images can be reconstructed from tomosynthesis images in breast imaging. This study aims to investigate the diagnostic efficacy of synthesized 2D images (C-View) in comparison to full field digital mammography (FFDM) when used with digital breast tomosynthesis (DBT) in multi-ethnic Malaysian population. FFDM and C-View images (n = 380) were independently evaluated by three readers through Breast Imaging Reporting and Data System (BI-RADS) categorisation, breast density and lesion characterisation. Statistical analysis was done comparing sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of C-View + DBT with FFDM + DBT as standard of reference. Very good interreader agreement in BI-RADS category and density assessment between C-View + DBT and FFDM + DBT, with high sensitivity, specificity, PPV and NPV of C-View + DBT when compared with FFDM + DBT. There was comparable PPV between C-View + DBT and FFDM + DBT, with histopathology as gold standard. High level of interreader agreement in BI-RADS category and density assessment for FFDM + DBT and C-View + DBT. There was good agreement between FFDM + DBT and C-View + DBT in mass characterization, and almost perfect agreement in calcification and asymmetric density. 52.2% lower radiation dose incurred when using C-View + DBT. Hence, synthesized 2D images are comparable to FFDM with reduction in radiation dose within the limits of Malaysian multi-ethnic population.
    Matched MeSH terms: Radiographic Image Interpretation, Computer-Assisted/methods*
  14. Effect of iterative reconstruction algorithm levels on noise index and figure-of-merit in CT pulmonary angiography examinations
    Harun HH, Karim MKA, Abbas Z, Sabarudin A, Muniandy SC, Ibahim MJ
    J Xray Sci Technol, 2020;28(5):893-903.
    PMID: 32741801 DOI: 10.3233/XST-200699
    PURPOSE: To evaluate the influence of iterative reconstruction (IR) levels on Computed Tomography (CT) image quality and to establish Figure of Merit (FOM) value for CT Pulmonary Angiography (CTPA) examinations.

    METHODS: Images of 31 adult patients who underwent CTPA examinations in our institution from March to April 2019 were retrospectively collected. Other data, such as scanning parameters, radiation dose and body habitus information from the subjects were also recorded. Six different levels of IR were applied to the volume data of the subjects. Five circles of the region of interest (ROI) were drawn in five different arteries namely, pulmonary trunk, right pulmonary artery, left pulmonary artery, ascending aorta and descending aorta. The mean Signal-to-noise ratio (SNR) was obtained, and the FOM was calculated in a fraction of the SNR2 divided by volume-weighted CT dose index (CTDIvol) and SNR2 divided by the size-specific dose estimates (SSDE).

    RESULTS: Overall, we observed that the mean value of CTDIvol and SSDE were 13.79±7.72 mGy and 17.25±8.92 mGy, respectively. Notably, SNR values significantly increase with increase of the IR level (p 

    Matched MeSH terms: Radiographic Image Interpretation, Computer-Assisted/methods*
  15. Fulltext Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions
    Tan M, Aghaei F, Wang Y, Zheng B
    Phys Med Biol, 2017 01 21;62(2):358-376.
    PMID: 27997380 DOI: 10.1088/1361-6560/aa5081
    The purpose of this study is to evaluate a new method to improve performance of computer-aided detection (CAD) schemes of screening mammograms with two approaches. In the first approach, we developed a new case based CAD scheme using a set of optimally selected global mammographic density, texture, spiculation, and structural similarity features computed from all four full-field digital mammography images of the craniocaudal (CC) and mediolateral oblique (MLO) views by using a modified fast and accurate sequential floating forward selection feature selection algorithm. Selected features were then applied to a 'scoring fusion' artificial neural network classification scheme to produce a final case based risk score. In the second approach, we combined the case based risk score with the conventional lesion based scores of a conventional lesion based CAD scheme using a new adaptive cueing method that is integrated with the case based risk scores. We evaluated our methods using a ten-fold cross-validation scheme on 924 cases (476 cancer and 448 recalled or negative), whereby each case had all four images from the CC and MLO views. The area under the receiver operating characteristic curve was AUC  =  0.793  ±  0.015 and the odds ratio monotonically increased from 1 to 37.21 as CAD-generated case based detection scores increased. Using the new adaptive cueing method, the region based and case based sensitivities of the conventional CAD scheme at a false positive rate of 0.71 per image increased by 2.4% and 0.8%, respectively. The study demonstrated that supplementary information can be derived by computing global mammographic density image features to improve CAD-cueing performance on the suspicious mammographic lesions.
    Matched MeSH terms: Radiographic Image Interpretation, Computer-Assisted/methods*
  16. Benefits of 3D rotational DSA compared with 2D DSA in the evaluation of intracranial aneurysm
    Wong SC, Nawawi O, Ramli N, Abd Kadir KA
    Acad Radiol, 2012 Jun;19(6):701-7.
    PMID: 22578227 DOI: 10.1016/j.acra.2012.02.012
    The aim of this study was to compare conventional two-dimensional (2D) digital subtraction angiography (DSA) with three-dimensional (3D) rotational DSA in the investigation of intracranial aneurysm in terms of detection, size measurement, neck diameter, neck delineation, and relationship with surrounding vessels. A further aim was to compare radiation dose, contrast volume, and procedural time between the two protocols.
    Matched MeSH terms: Radiographic Image Interpretation, Computer-Assisted/methods*
  17. Profiling the features of pre-segmented healthy liver CT scans: towards fast detection of liver lesions in emergency scenario
    Pasha MF, Hong KS, Rajeswari M
    Annu Int Conf IEEE Eng Med Biol Soc, 2011;2011:5169-73.
    PMID: 22255503 DOI: 10.1109/IEMBS.2011.6091280
    Automating the detection of lesions in liver CT scans requires a high performance and robust solution. With CT-scan start to become the norm in emergency department, the need for a fast and efficient liver lesions detection method is arising. In this paper, we propose a fast and evolvable method to profile the features of pre-segmented healthy liver and use it to detect the presence of liver lesions in emergency scenario. Our preliminary experiment with the MICCAI 2007 grand challenge datasets shows promising results of a fast training time, ability to evolve the produced healthy liver profiles, and accurate detection of the liver lesions. Lastly, the future work directions are also presented.
    Matched MeSH terms: Radiographic Image Interpretation, Computer-Assisted/methods*
  18. Applying a statistical PTB detection procedure to complement the gold standard
    Noor NM, Yunus A, Bakar SA, Hussin A, Rijal OM
    Comput Med Imaging Graph, 2011 Apr;35(3):186-94.
    PMID: 21036539 DOI: 10.1016/j.compmedimag.2010.10.002
    This paper investigates a novel statistical discrimination procedure to detect PTB when the gold standard requirement is taken into consideration. Archived data were used to establish two groups of patients which are the control and test group. The control group was used to develop the statistical discrimination procedure using four vectors of wavelet coefficients as feature vectors for the detection of pulmonary tuberculosis (PTB), lung cancer (LC), and normal lung (NL). This discrimination procedure was investigated using the test group where the number of sputum positive and sputum negative cases that were correctly classified as PTB cases were noted. The proposed statistical discrimination method is able to detect PTB patients and LC with high true positive fraction. The method is also able to detect PTB patients that are sputum negative and therefore may be used as a complement to the gold standard.
    Matched MeSH terms: Radiographic Image Interpretation, Computer-Assisted/methods*
  19. Quantitative assessment of breast density from digitized mammograms into Tabar's patterns
    Jamal N, Ng KH, Looi LM, McLean D, Zulfiqar A, Tan SP, et al.
    Phys Med Biol, 2006 Nov 21;51(22):5843-57.
    PMID: 17068368
    We describe a semi-automated technique for the quantitative assessment of breast density from digitized mammograms in comparison with patterns suggested by Tabar. It was developed using the MATLAB-based graphical user interface applications. It is based on an interactive thresholding method, after a short automated method that shows the fibroglandular tissue area, breast area and breast density each time new thresholds are placed on the image. The breast density is taken as a percentage of the fibroglandular tissue to the breast tissue areas. It was tested in four different ways, namely by examining: (i) correlation of the quantitative assessment results with subjective classification, (ii) classification performance using the quantitative assessment technique, (iii) interobserver agreement and (iv) intraobserver agreement. The results of the quantitative assessment correlated well (r2 = 0.92) with the subjective Tabar patterns classified by the radiologist (correctly classified 83% of digitized mammograms). The average kappa coefficient for the agreement between the readers was 0.63. This indicated moderate agreement between the three observers in classifying breast density using the quantitative assessment technique. The kappa coefficient of 0.75 for intraobserver agreement reflected good agreement between two sets of readings. The technique may be useful as a supplement to the radiologist's assessment in classifying mammograms into Tabar's pattern associated with breast cancer risk.
    Matched MeSH terms: Radiographic Image Interpretation, Computer-Assisted/methods*
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