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Medical Imaging, IEEE Transactions on

Issue 4 • Date April 2006

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Displaying Results 1 - 18 of 18
  • Table of contents

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  • IEEE Transactions on Medical Imaging publication information

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  • Computer analysis of computed tomography scans of the lung: a survey

    Page(s): 385 - 405
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (761 KB) |  | HTML iconHTML  

    Current computed tomography (CT) technology allows for near isotropic, submillimeter resolution acquisition of the complete chest in a single breath hold. These thin-slice chest scans have become indispensable in thoracic radiology, but have also substantially increased the data load for radiologists. Automating the analysis of such data is, therefore, a necessity and this has created a rapidly developing research area in medical imaging. This paper presents a review of the literature on computer analysis of the lungs in CT scans and addresses segmentation of various pulmonary structures, registration of chest scans, and applications aimed at detection, classification and quantification of chest abnormalities. In addition, research trends and challenges are identified and directions for future research are discussed. View full abstract»

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  • Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN)

    Page(s): 406 - 416
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2955 KB) |  | HTML iconHTML  

    When lung nodules overlap with ribs or clavicles in chest radiographs, it can be difficult for radiologists as well as computer-aided diagnostic (CAD) schemes to detect these nodules. In this paper, we developed an image-processing technique for suppressing the contrast of ribs and clavicles in chest radiographs by means of a multiresolution massive training artificial neural network (MTANN). An MTANN is a highly nonlinear filter that can be trained by use of input chest radiographs and the corresponding "teaching" images. We employed "bone" images obtained by use of a dual-energy subtraction technique as the teaching images. For effective suppression of ribs having various spatial frequencies, we developed a multiresolution MTANN consisting of multiresolution decomposition/composition techniques and three MTANNs for three different-resolution images. After training with input chest radiographs and the corresponding dual-energy bone images, the multiresolution MTANN was able to provide "bone-image-like" images which were similar to the teaching bone images. By subtracting the bone-image-like images from the corresponding chest radiographs, we were able to produce "soft-tissue-image-like" images where ribs and clavicles were substantially suppressed. We used a validation test database consisting of 118 chest radiographs with pulmonary nodules and an independent test database consisting of 136 digitized screen-film chest radiographs with 136 solitary pulmonary nodules collected from 14 medical institutions in this study. When our technique was applied to nontraining chest radiographs, ribs and clavicles in the chest radiographs were suppressed substantially, while the visibility of nodules and lung vessels was maintained. Thus, our image-processing technique for rib suppression by means of a multiresolution MTANN would be potentially useful for radiologists as well as for CAD schemes in detection of lung nodules on chest radiographs. View full abstract»

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  • Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans

    Page(s): 417 - 434
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3404 KB) |  | HTML iconHTML  

    Volumetric growth assessment of pulmonary lesions is crucial to both lung cancer screening and oncological therapy monitoring. While several methods for small pulmonary nodules have previously been presented, the segmentation of larger tumors that appear frequently in oncological patients and are more likely to be complexly interconnected with lung morphology has not yet received much attention. We present a fast, automated segmentation method that is based on morphological processing and is suitable for both small and large lesions. In addition, the proposed approach addresses clinical challenges to volume assessment such as variations in imaging protocol or inspiration state by introducing a method of segmentation-based partial volume analysis (SPVA) that follows on the segmentation procedure. Accuracy and reproducibility studies were performed to evaluate the new algorithms. In vivo interobserver and interscan studies on low-dose data from eight clinical metastasis patients revealed that clinically significant volume change can be detected reliably and with negligible computation time by the presented methods. In addition, phantom studies were conducted. Based on the segmentation performed with the proposed method, the performance of the SPVA volumetry method was compared with the conventional technique on a phantom that was scanned with different dosages and reconstructed with varying parameters. Both systematic and absolute errors were shown to be reduced substantially by the SPVA method. The method was especially successful in accounting for slice thickness and reconstruction kernel variations, where the median error was more than halved in comparison to the conventional approach. View full abstract»

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  • On measuring the change in size of pulmonary nodules

    Page(s): 435 - 450
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1900 KB) |  | HTML iconHTML  

    The pulmonary nodule is the most common manifestation of lung cancer, the most deadly of all cancers. Most small pulmonary nodules are benign, however, and currently the growth rate of the nodule provides for one of the most accurate noninvasive methods of determining malignancy. In this paper, we present methods for measuring the change in nodule size from two computed tomography image scans recorded at different times; from this size change the growth rate may be established. The impact of partial voxels for small nodules is evaluated and isotropic resampling is shown to improve measurement accuracy. Methods for nodule location and sizing, pleural segmentation, adaptive thresholding, image registration, and knowledge-based shape matching are presented. The latter three techniques provide for a significant improvement in volume change measurement accuracy by considering both image scans simultaneously. Improvements in segmentation are evaluated by measuring volume changes in benign or slow growing nodules. In the analysis of 50 nodules, the variance in percent volume change was reduced from 11.54% to 9.35% (p=0.03) through the use of registration, adaptive thresholding, and knowledge-based shape matching. View full abstract»

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  • Local noise weighted filtering for emphysema scoring of low-dose CT images

    Page(s): 451 - 463
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3823 KB)  

    Computed tomography (CT) has become the new reference standard for quantification of emphysema. The most popular measure of emphysema derived from CT is the pixel index (PI), which expresses the fraction of the lung volume with abnormally low intensity values. As PI is calculated from a single, fixed threshold on intensity, this measure is strongly influenced by noise. This effect shows up clearly when comparing the PI score of a high-dose scan to the PI score of a low-dose (i.e., noisy) scan of the same subject. In this paper, the noise variance (NOVA) filter is presented: a general framework for (iterative) nonlinear filtering, which uses an estimate of the spatially dependent noise variance in an image. The NOVA filter iteratively estimates the local image noise and filters the image. For the specific purpose of emphysema quantification of low-dose CT images, a dedicated, noniterative NOVA filter is constructed by using prior knowledge of the data to obtain a good estimate of the spatially dependent noise in an image. The performance of the NOVA filter is assessed by comparing characteristics of pairs of high-dose and low-dose scans. The compared characteristics are the PI scores for different thresholds and the size distributions of emphysema bullae. After filtering, the PI scores of high-dose and low-dose images agree to within 2%-3%points. The reproducibility of the high-dose bullae size distribution is also strongly improved. NOVA filtering of a CT image of typically 400×512×512 voxels takes only a couple of minutes which makes it suitable for routine use in clinical practice. View full abstract»

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  • MDCT-based 3-D texture classification of emphysema and early smoking related lung pathologies

    Page(s): 464 - 475
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1078 KB) |  | HTML iconHTML  

    Our goal is to enhance the ability to differentiate normal lung from subtle pathologies via multidetector row CT (MDCT) by extending a two-dimensional (2-D) texturebased tissue classification [adaptive multiple feature method (AMFM)] to use three-dimensional (3-D) texture features. We performed MDCT on 34 humans and classified volumes of interest (VOIs) in the MDCT images into five categories: EC, emphysema in severe chronic obstructive pulmonary disease (COPD); MC, mild emphysema in mild COPD; NC, normal appearing lung in mild COPD; NN, normal appearing lung in normal nonsmokers; and NS, normal appearing lung in normal smokers. COPD severity was based upon pulmonary function tests (PFTs). Airways and vessels were excluded from VOIs; 24 3-D texture features were calculated; and a Bayesian classifier was used for discrimination. A leave-one-out method was employed for validation. Sensitivity of the four-class classification in the form of 3-D/2-D was: EC: 85%/71%, MC: 90%/82%; NC: 88%/50%; NN: 100%/60%. Sensitivity and specificity for NN using a two-class classification of NN and NS in the form of 3-D/2-D were: 99%/72% and 100%/75%, respectively. We conclude that 3-D AMFM analysis of lung parenchyma improves discrimination compared to 2-D AMFM of the same VOIs. Furthermore, our results suggest that the 3-D AMFM may provide a means of discriminating subtle differences between smokers and nonsmokers both with normal PFTs. View full abstract»

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  • Lung motion correction on respiratory gated 3-D PET/CT images

    Page(s): 476 - 485
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2488 KB) |  | HTML iconHTML  

    Motion is a source of degradation in positron emission tomography (PET)/computed tomography (CT) images. As the PET images represent the sum of information over the whole respiratory cycle, attenuation correction with the help of CT images may lead to false staging or quantification of the radioactive uptake especially in the case of small tumors. We present an approach avoiding these difficulties by respiratory-gating the PET data and correcting it for motion with optical flow algorithms. The resulting dataset contains all the PET information and minimal motion and, thus, allows more accurate attenuation correction and quantification. View full abstract»

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  • Nonlinear motion correction of respiratory-gated lung SPECT images

    Page(s): 486 - 495
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2052 KB) |  | HTML iconHTML  

    We propose a method for correcting the motion of the lungs between different phase images obtained by respiratory-gated single photon emission computed tomography (SPECT). This method is applied to SPECT images that show a preserved activity distribution in the lungs such as 99m-Tc macro aggregated albumin (99m-Tc-MAA) perfusion images and 99m-Tc-Technegas ventilation images. In the proposed method, an objective function, which consists of both the degree of similarity between a reference image and a deformed image, and the smoothness of deformation is defined and optimized using a simulated annealing algorithm. For the degree of similarity term in the objective function, an expansion ratio, defined as the ratio of change in local volume due to deformation, is introduced to preserve the total activity during the motion correction process. This method was applied to data simulated from computer phantoms, data acquired from a physical phantom, and 17 sets of clinical data. In all cases, the motion correction between inspiration and expiration phase images was successfully achieved. View full abstract»

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  • An alternate line erasure and readout (ALER) method for implementing slot-scan imaging technique with a flat-panel detector-initial experiences

    Page(s): 496 - 502
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (571 KB) |  | HTML iconHTML  

    This paper describes and demonstrates an electronic collimation method, referred to as the alternate line erasure and readout (ALER) technique, for implementing slot-scan digital radiography technique with an amorphous silicon (a-Si) thin-film transistor (TFT) array based flat-panel detector. An amorphus selenium (a-Se) flat-panel detector was modified to implement the ALER technique for slot-scan imaging. A stepping-motor driven fore-collimator was mounted in front of an X-ray tube to generate a scanning X-ray fan beam. The scanning speed and magnification were adjusted to synchronize the fan beam motion with the image line readout rate. The image lines on the leading and trailing edges of the fan beam were tracked and alternately reset and read out, respectively. The former operation resulted in the erasure of the scatter signals accumulated in the leading edge image line prior to the arrival of the fan beam. The latter operation resulted in the acquisition of fan beam exposure data integrated in the trailing edge image line right after the fan beam passed. To demonstrate the scatter rejection capability of this technique, an anthropomorphic chest phantom was placed in PA position and scanned at a speed of 576 lines (8.0 cm)/s at 117 kVp and 32 mA. A tungsten bar is placed at the entrance side of the chest phantom to measure the scatter-to-primary ratio (SPR), scatter reduction factor (SRF), and contrast-to-noise ratio degradation factor (CNRDF) in the slot-scan images to evaluate the effectiveness of scatter rejection and the resultant improvement of image quality. SPR and CNRDF in the open-field images were also measured and used as the reference for comparison. A scatter reduction by 86.4 to 95.4% across lower lung and heart regions has been observed with slot-scan imaging. The CNRs have been found to be improved by a factor of 2 in the mediastinum areas over the open-field image as well. View full abstract»

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  • Patient-specific bronchoscopy visualization through BRDF estimation and disocclusion correction

    Page(s): 503 - 513
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (450 KB) |  | HTML iconHTML  

    This paper presents an image-based method for virtual bronchoscope with photo-realistic rendering. The technique is based on recovering bidirectional reflectance distribution function (BRDF) parameters in an environment where the choice of viewing positions, directions, and illumination conditions are restricted. Video images of bronchoscopy examinations are combined with patient-specific three-dimensional (3-D) computed tomography data through two-dimensional (2-D)/3-D registration and shading model parameters are then recovered by exploiting the restricted lighting configurations imposed by the bronchoscope. With the proposed technique, the recovered BRDF is used to predict the expected shading intensity, allowing a texture map independent of lighting conditions to be extracted from each video frame. To correct for disocclusion artefacts, statistical texture synthesis was used to recreate the missing areas. New views not present in the original bronchoscopy video are rendered by evaluating the BRDF with different viewing and illumination parameters. This allows free navigation of the acquired 3-D model with enhanced photo-realism. To assess the practical value of the proposed technique, a detailed visual scoring that involves both real and rendered bronchoscope images is conducted. View full abstract»

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  • IEEE San Diego 2006

    Page(s): 514
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  • Special issue on computational neuroanatomy

    Page(s): 515
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  • 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering Revolution in BioMedicine

    Page(s): 516
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  • IEEE Transactions on Medical Imaging Information for authors

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  • Blank page [back cover]

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Aims & Scope

IEEE Transactions on Medical Imaging (T-MI) encourages the submission of manuscripts on imaging of body structures, morphology and function, and imaging of microscopic biological entities. The journal publishes original contributions on medical imaging achieved by various modalities, such as ultrasound, X-rays (including CT) magnetic resonance, radionuclides, microwaves, and light, as well as medical image processing and analysis, visualization, pattern recognition, and related methods. Studies involving highly technical perspectives are most welcome. The journal focuses on a unified common ground where instrumentation, systems, components, hardware and software, mathematics and physics contribute to the studies.

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Meet Our Editors

Editor-in-Chief
Milan Sonka
Iowa Institute for Biomedical Imaging
3016B SC, Department of Electrical and Computer Engineering
The University of Iowa
Iowa City, IA  52242  52242  USA
milan-sonka@uiowa.edu