Investigation of new psychophysical measures for evaluation of similar images on thoracic computed tomography for distinction between benign and malignant nodules.

We have been developing a computerized scheme to assist radiologists in improving the diagnostic accuracy for lung cancers on low-dose computed tomography (LDCT) scans by use of similar images for malignant nodules and benign nodules. A database of 415 LDCT scans including 73 cases with 76 confirmed cancers and 342 cases with 413 confirmed benign nodules was first collected in an LDCT screening program for early detection of lung cancers in Nagano, Japan. An observer study by use of receiver operating characteristics analysis was first conducted with five radiologists to demonstrate that presenting similar images for malignant nodules and benign nodules can significantly improve radiologists' performance in the diagnosis of unknown nodules. Another observer study was then conducted for obtaining reliable data on subjective similarity ratings by 10 radiologists. Based on the subjective similarity ratings, three important features were selected from a number of nodule features, and four different techniques for the determination of similarity measures, namely, a feature-based technique, a pixel-value-difference based technique, a cross-correlation-based technique, and a neural-network-based technique, were investigated and evaluated in terms of the correlation coefficient with the subjective similarity ratings. The experimental results in this study indicated that the neural-network-based technique can provide a reliable psychophysical similarity measure which is comparable to the subjective similarity ratings for a single radiologist when evaluated by use of correlation with the average similarity ratings for the other nine radiologists.

[1]  C. Metz.  ROC Methodology in Radiologic Imaging , 1986, Investigative radiology.

[2]  S S Sagel,et al.  CT of the pulmonary nodule: a cooperative study. , 1986, Radiology.

[3]  E. Fishman,et al.  Solitary pulmonary nodules: CT assessment. , 1986, Radiology.

[4]  William H. Press,et al.  Numerical Recipes: The Art of Scientific Computing , 1987 .

[5]  W. Press,et al.  Numerical Recipes: The Art of Scientific Computing , 1987 .

[6]  M. Giger,et al.  Image feature analysis and computer-aided diagnosis in digital radiography. 3. Automated detection of nodules in peripheral lung fields. , 1988, Medical physics.

[7]  Hiromitsu Yamada,et al.  Recognition of Kidney Glomerulus by Dynamic Programming Matching Method , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  C E Metz,et al.  Some practical issues of experimental design and data analysis in radiological ROC studies. , 1989, Investigative radiology.

[9]  Ramesh C. Jain,et al.  Using Dynamic Programming for Solving Variational Problems in Vision , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  M L Giger,et al.  Pulmonary nodules: computer-aided detection in digital chest images. , 1990, Radiographics : a review publication of the Radiological Society of North America, Inc.

[11]  N. Müller,et al.  Solitary pulmonary nodule: high-resolution CT and radiologic-pathologic correlation. , 1991, Radiology.

[12]  Christos Faloutsos,et al.  QBIC project: querying images by content, using color, texture, and shape , 1993, Electronic Imaging.

[13]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  J. Gurney,et al.  Solitary pulmonary nodules: determining the likelihood of malignancy with neural network analysis. , 1995, Radiology.

[15]  M. Giger,et al.  Analysis of spiculation in the computerized classification of mammographic masses. , 1995, Medical physics.

[16]  Guy Marchal,et al.  Multi-modality image registration by maximization of mutual information , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[17]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[18]  M. Giger,et al.  Development of an improved CAD scheme for automated detection of lung nodules in digital chest images. , 1997, Medical physics.

[19]  Jill L. King,et al.  Relationship of subjective ratings of image quality and observer performance , 1997, Medical Imaging.

[20]  Noboru Niki,et al.  Classification of pulmonary nodules in thin-section CT images based on shape characterization , 1997, Proceedings of International Conference on Image Processing.

[21]  Feng Li,et al.  Mass screening for lung cancer with mobile spiral computed tomography scanner , 1998, The Lancet.

[22]  N. Okun,et al.  Randomised trial to assess safety and fetal outcome of early and midtrimester amniocentesis , 1998, The Lancet.

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

[24]  Y. Kawata,et al.  Computer-aided diagnosis for pulmonary nodules based on helical CT images , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[25]  S. Armato,et al.  Computerized detection of pulmonary nodules on CT scans. , 1999, Radiographics : a review publication of the Radiological Society of North America, Inc.

[26]  C J Vyborny,et al.  Artificial neural networks in chest radiography: application to the differential diagnosis of interstitial lung disease. , 1999, Academic radiology.

[27]  Jill L. King,et al.  Observer performance assessment of JPEG-compressed high-resolution chest images , 1999, Medical Imaging.

[28]  Shih-Fu Chang,et al.  Image Retrieval: Current Techniques, Promising Directions, and Open Issues , 1999, J. Vis. Commun. Image Represent..

[29]  M L Giger,et al.  Effect of dominant features on neural network performance in the classification of mammographic lesions. , 1999, Physics in medicine and biology.

[30]  Noboru Niki,et al.  Computer aided differential diagnosis of pulmonary nodules using curvature based analysis , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[31]  M. McNitt-Gray,et al.  A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution CT: preliminary results. , 1999, Medical physics.

[32]  O. Miettinen,et al.  Early Lung Cancer Action Project: overall design and findings from baseline screening , 1999, The Lancet.

[33]  Michael F. McNitt-Gray,et al.  Classification of solitary pulmonary nodules (SPNs) imaged on high-resolution CT using contrast enhancement and three-dimensional quantitative image features , 2000, Medical Imaging: Image Processing.

[34]  Binsheng Zhao,et al.  Small pulmonary nodules: volumetrically determined growth rates based on CT evaluation. , 2000, Radiology.

[35]  Eric Y. Tao,et al.  Computer-aided, case-based diagnosis of mammographic regions of interest containing microcalcifications. , 2000, Academic radiology.

[36]  K Nakamura,et al.  Computerized analysis of the likelihood of malignancy in solitary pulmonary nodules with use of artificial neural networks. , 2000, Radiology.

[37]  Taylor Murray,et al.  Cancer statistics, 2000 , 2000, CA: a cancer journal for clinicians.

[38]  S. Sone,et al.  Characteristics of small lung cancers invisible on conventional chest radiography and detected by population based screening using spiral CT. , 2000, The British journal of radiology.

[39]  M. McNitt-Gray,et al.  Patient-specific models for lung nodule detection and surveillance in CT images , 2001, IEEE Transactions on Medical Imaging.

[40]  S. Sone,et al.  Results of three-year mass screening programme for lung cancer using mobile low-dose spiral computed tomography scanner , 2001, British Journal of Cancer.

[41]  Erik Hjelmås,et al.  Face Detection: A Survey , 2001, Comput. Vis. Image Underst..

[42]  Michael F. McNitt-Gray,et al.  Computer-aided diagnosis of the solitary pulmonary nodule imaged on CT: 2D, 3D, and contrast enhancement features , 2001, SPIE Medical Imaging.

[43]  Hiroshi Fujita,et al.  Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique , 2001, IEEE Transactions on Medical Imaging.

[44]  K. Doi,et al.  Usefulness of an artificial neural network for differentiating benign from malignant pulmonary nodules on high-resolution CT: evaluation with receiver operating characteristic analysis. , 2002, AJR. American journal of roentgenology.

[45]  K. Doi,et al.  Automated computerized scheme for distinction between benign and malignant solitary pulmonary nodules on chest images. , 2002, Medical physics.

[46]  Berkman Sahiner,et al.  Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system. , 2002, Medical physics.

[47]  K. Doi,et al.  Computerized scheme for determination of the likelihood measure of malignancy for pulmonary nodules on low-dose CT images. , 2003, Medical physics.

[48]  Alex Pentland,et al.  Photobook: Content-based manipulation of image databases , 1996, International Journal of Computer Vision.

[49]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.