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      Development of CAD based on ANN analysis of power spectra for pneumoconiosis in chest radiographs: effect of three new enhancement methods

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          Abstract

          We have been developing a computer-aided detection (CAD) scheme for pneumoconiosis based on a rule-based plus artificial neural network (ANN) analysis of power spectra. In this study, we have developed three enhancement methods for the abnormal patterns to reduce false-positive and false-negative values. The image database consisted of 2 normal and 15 abnormal chest radiographs. The International Labour Organization standard chest radiographs with pneumoconiosis were categorized as subcategory, size, and shape of pneumoconiosis. Regions of interest (ROIs) with a matrix size of 32 × 32 were selected from normal and abnormal lungs. Three new enhanced methods were obtained by window function, top-hat transformation, and gray-level co-occurrence matrix analysis. We calculated the power spectrum (PS) of all ROIs by Fourier transform. For the classification between normal and abnormal ROIs, we applied a combined analysis using the ruled-based plus the ANN method. To evaluate the overall performance of this CAD scheme, we employed ROC analysis for distinguishing between normal and abnormal ROIs. On the chest radiographs of the highest categories (severe pneumoconiosis) and the lowest categories (early pneumoconiosis), this CAD scheme achieved area under the curve (AUC) values of 0.93 ± 0.02 and 0.72 ± 0.03. The combined rule-based plus ANN method with the three new enhanced methods obtained the highest classification performance for distinguishing between abnormal and normal ROIs. Our CAD system based on the three new enhanced methods would be useful in assisting radiologists in the classification of pneumoconiosis.

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          Most cited references 27

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          Pneumoconiosis: comparison of imaging and pathologic findings.

          Pneumoconiosis may be classified as either fibrotic or nonfibrotic, according to the presence or absence of fibrosis. Silicosis, coal worker pneumoconiosis, asbestosis, berylliosis, and talcosis are examples of fibrotic pneumoconiosis. Siderosis, stannosis, and baritosis are nonfibrotic forms of pneumoconiosis that result from inhalation of iron oxide, tin oxide, and barium sulfate particles, respectively. In an individual who has a history of exposure to silica or coal dust, a finding of nodular or reticulonodular lesions at chest radiography or small nodules with a perilymphatic distribution at thin-section computed tomography (CT), with or without eggshell calcifications, is suggestive of silicosis or coal worker pneumoconiosis. Magnetic resonance imaging is helpful for distinguishing between progressive massive fibrosis and lung cancer. CT and histopathologic findings in asbestosis are similar to those in idiopathic pulmonary fibrosis, but the presence of asbestos bodies in histopathologic specimens is specific for the diagnosis of asbestosis. Giant cell interstitial pneumonia due to exposure to hard metals is classified as a fibrotic form of pneumoconiosis and appears on CT images as mixed ground-glass opacities and reticulation. Berylliosis simulates pulmonary sarcoidosis on CT images. CT findings in talcosis include small centrilobular and subpleural nodules or heterogeneous conglomerate masses that contain foci of high attenuation indicating talc deposition. Siderosis is nonfibrotic and is indicated by a CT finding of poorly defined centrilobular nodules or ground-glass opacities. (c) RSNA, 2006.
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            Computer recognition of regional lung disease patterns.

            We have developed an objective, reproducible, and automated means for the regional evaluation of the pulmonary parenchyma from computed tomography (CT) scans. This method, known as the Adaptive Multiple Feature Method (AMFM) assesses as many as 22 independent texture features in order to classify a tissue pattern. In this study, the six tissue patterns characterized were: honeycombing, ground glass, bronchovascular, nodular, emphysemalike, and normal. The lung slices were evaluated regionally using 31 x 31 pixel regions of interest. In each region of interest, an optimal subset of texture features was evaluated to determine which of the six patterns the region could be characterized as. The computer output was validated against experienced observers in three settings. In the first two readings, when the observers were blinded to the primary diagnosis of the subject, the average computer versus observer agreement was 44.4 +/- 8.7% and 47.3 +/- 9.0%, respectively. The average interobserver agreement for the same two readings were 48.8 +/- 9.1% and 52.2 +/- 10.0%, respectively. In the third reading, when the observers were provided the primary diagnosis, the average computer versus observer agreement was 51.7 +/- 2.9% where as the average interobserver agreement was 53.9 +/- 6.2%. The kappa statistic of agreement between the regions, for which the majority of the observers agreed on a pattern type, versus the computer was found to be 0.62. For regional tissue characterization, the AMFM is 100% reproducible and performs as well as experienced human observers who have been told the patient diagnosis.
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              Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography.

              In this study, we investigated a pattern-recognition technique based on an artificial neural network (ANN), which is called a massive training artificial neural network (MTANN), for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography (CT) images. The MTANN consists of a modified multilayer ANN, which is capable of operating on image data directly. The MTANN is trained by use of a large number of subregions extracted from input images together with the teacher images containing the distribution for the "likelihood of being a nodule." The output image is obtained by scanning an input image with the MTANN. The distinction between a nodule and a non-nodule is made by use of a score which is defined from the output image of the trained MTANN. In order to eliminate various types of non-nodules, we extended the capability of a single MTANN, and developed a multiple MTANN (Multi-MTANN). The Multi-MTANN consists of plural MTANNs that are arranged in parallel. Each MTANN is trained by using the same nodules, but with a different type of non-nodule. Each MTANN acts as an expert for a specific type of non-nodule, e.g., five different MTANNs were trained to distinguish nodules from various-sized vessels; four other MTANNs were applied to eliminate some other opacities. The outputs of the MTANNs were combined by using the logical AND operation such that each of the trained MTANNs eliminated none of the nodules, but removed the specific type of non-nodule with which the MTANN was trained, and thus removed various types of non-nodules. The Multi-MTANN consisting of nine MTANNs was trained with 10 typical nodules and 10 non-nodules representing each of nine different non-nodule types (90 training non-nodules overall) in a training set. The trained Multi-MTANN was applied to the reduction of false positives reported by our current computerized scheme for lung nodule detection based on a database of 63 low-dose CT scans (1765 sections), which contained 71 confirmed nodules including 66 biopsy-confirmed primary cancers, from a lung cancer screening program. The Multi-MTANN was applied to 58 true positives (nodules from 54 patients) and 1726 false positives (non-nodules) reported by our current scheme in a validation test; these were different from the training set. The results indicated that 83% (1424/1726) of non-nodules were removed with a reduction of one true positive (nodule), i.e., a classification sensitivity of 98.3% (57 of 58 nodules). By using the Multi-MTANN, the false-positive rate of our current scheme was improved from 0.98 to 0.18 false positives per section (from 27.4 to 4.8 per patient) at an overall sensitivity of 80.3% (57/71).
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                Author and article information

                Affiliations
                [ ]Department of Medical Radiological Technology, Kagoshima Medical Technology College, 5417-1, Hirakawa, Kagoshima 891-0133 Japan
                [ ]Department of Clinical Radiology, Hiroshima International University, 555-36, Kurosegakuendai, Higashihiroshima, Hiroshima 739-2695 Japan
                [ ]Division of Health Sciences, Graduate School of Medicine, Osaka University, 1-7, Yamadaoka, Suita, 565-0871 Japan
                Contributors
                +99-261-6161 , +99-262-5252 , rt.okumura@harada-gakuen.ac.jp
                Journal
                Radiol Phys Technol
                Radiol Phys Technol
                Radiological Physics and Technology
                Springer Japan (Tokyo )
                1865-0333
                1865-0341
                12 January 2014
                12 January 2014
                2014
                : 7
                : 217-227
                24414539 4098051 255 10.1007/s12194-013-0255-9
                © The Author(s) 2014

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

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                © Japanese Society of Radiological Technology and Japan Society of Medical Physics 2014

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