Zone-based analysis for automated detection of abnormalities in chest radiographs.

PURPOSE The aim of this study was to develop an automated method for detection of local texture-based and density-based abnormalities in chest radiographs. METHODS The method was based on profile analysis to detect abnormalities in chest radiographs. In the method, one density-based feature, Density Symmetry Index, and two texture-based features, Roughness Maximum Index and Roughness Symmetry Index, were used to detect abnormalities in the lung fields. In each chest radiograph, the lung fields were divided into four zones initially and then the method was applied to each zone separately. For each zone, Density Symmetry Index was obtained from the projection profile of each zone, and Roughness Maximum Index and Roughness Symmetry Index were obtained by measuring the roughness of the horizontal profiles via moving average technique. Linear discriminant analysis was used to classify normal and abnormal cases based on the three indices. The discriminant performance of the method was evaluated using ROC analysis. RESULTS The method was evaluated on a database of 250 normal and 250 abnormal chest images. In the optimized conditions, the zone-based performance Az of the method for zones 1, 2, 3, and 4 were 0.917, 0.897, 0.892, and 0.814, respectively, and the case-based performance Az of the method was 0.842. Our previous method for detection of gross abnormalities was also evaluated on the same database. The case-based performance of our previous method was 0.689. CONCLUSIONS In comparing the previous method and the new method proposed in this study, there was a great improvement by the new method for detection of local texture-based and density-based abnormalities. The new method combined with the previous one has potential for screening abnormalities in chest radiographs.

[1]  S Katsuragawa,et al.  Image feature analysis and computer-aided diagnosis in digital radiography: classification of normal and abnormal lungs with interstitial disease in chest images. , 1989, Medical physics.

[2]  K. Paton,et al.  Reading chest radiographs for pneumoconiosis by computer. , 1975, British journal of industrial medicine.

[3]  Kunio Doi,et al.  Application of artificial neural networks for quantitative analysis of image data in chest radiographs for detection of interstitial lung disease , 2009, Journal of Digital Imaging.

[4]  T Kobayashi,et al.  Computerized analysis of interstitial disease in chest radiographs: improvement of geometric-pattern feature analysis. , 1997, Medical physics.

[5]  J. Boone,et al.  A fully automated algorithm for the segmentation of lung fields on digital chest radiographic images. , 1995, Medical physics.

[6]  Ewa Pietka,et al.  Lung segmentation in digital radiographs , 1994, Journal of Digital Imaging.

[7]  N Sezaki,et al.  Automatic computation of the cardiothoracic ratio with application to mass screening. , 1973, IEEE transactions on bio-medical engineering.

[8]  J Ikezoe,et al.  Fractal analysis of interstitial lung abnormalities in chest radiography. , 1995, Radiographics : a review publication of the Radiological Society of North America, Inc.

[9]  C. Metz.  Basic principles of ROC analysis. , 1978, Seminars in nuclear medicine.

[10]  K Doi,et al.  The nature and subtlety of abnormal findings in chest radiographs. , 1991, Medical physics.

[11]  M L Giger,et al.  Automated lung segmentation in digital lateral chest radiographs. , 1998, Medical physics.

[12]  N. Petrick,et al.  Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces. , 1998, Medical physics.

[13]  Charles A. Laszlo,et al.  The Measurement of Total Lung Capacity Based on a Computer Analysis of Anterior and Lateral Radiographic Chest Images , 1974 .

[14]  W. Miller.  Chest radiographic evaluation of diffuse infiltrative lung disease: review of a dying art. , 2002, European journal of radiology.

[15]  E. Hall,et al.  Computer Classification of Pneumoconiosis from Radiographs of Coal Workers , 1975, IEEE Transactions on Biomedical Engineering.

[16]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[17]  Bram van Ginneken,et al.  Dissimilarity-based classification in the absence of local ground truth: Application to the diagnostic interpretation of chest radiographs , 2009, Pattern Recognit..

[18]  M L Giger,et al.  Computerized delineation and analysis of costophrenic angles in digital chest radiographs. , 1998, Academic radiology.

[19]  K Shinoda,et al.  Computer analysis of radiographic images. , 1968, The Journal of Nihon University School of Dentistry.

[20]  S Katsuragawa,et al.  Image feature analysis and computer-aided diagnosis in digital radiography: detection and characterization of interstitial lung disease in digital chest radiographs. , 1988, Medical physics.

[21]  Kunio Doi,et al.  Automatic detection of abnormalities in chest radiographs using local texture analysis , 2002, IEEE Transactions on Medical Imaging.

[22]  S. Dwyer,et al.  Automated radiographic diagnosis via feature extraction and classification of cardiac size and shape descriptors. , 1972, IEEE transactions on bio-medical engineering.

[23]  Yulia Arzhaeva,et al.  Computer-aided detection of interstitial abnormalities in chest radiographs using a reference standard based on computed tomography. , 2007, Medical physics.

[24]  S. Armato,et al.  Automated lung segmentation in digitized posteroanterior chest radiographs. , 1998, Academic radiology.

[25]  W J NETTLETON,et al.  DIGITAL COMPUTER DETERMINATION OF A MEDICAL DIAGNOSTIC INDEX DIRECTLY FROM CHEST X-RAY IMAGES. , 1964, IEEE transactions on bio-medical engineering.

[26]  Mads Nielsen,et al.  Detection of interstitial lung disease in PA chest radiographs , 2004, SPIE Medical Imaging.

[27]  Kunio Doi,et al.  Classification of normal and abnormal lungs with interstitial diseases by rule-based method and artificial neural networks , 1997, Journal of Digital Imaging.

[28]  Charles A. Harlow,et al.  Computer analysis of chest radiographs , 1973, Comput. Graph. Image Process..

[29]  Ernest L. Hall,et al.  Texture Measures for Automatic Classification of Pulmonary Disease , 1972, IEEE Transactions on Computers.

[30]  G. Liu,et al.  Projection profile analysis for automated detection of abnormalities in chest radiographs. , 2005, Medical physics.