Local contralateral subtraction based on bilateral symmetry of lung for reduction of false positives in computerized detection of pulmonary nodules

A novel method called local contralateral subtraction has been developed for the removal of normal anatomic structures in chest radiographs based on the symmetry between the left and right lung regions. The method was oriented to the reduction of false positives reported by a computer-aided diagnosis (CAD) scheme for detection of lung nodules in chest radiographs. In our method, two regions of interest (ROIs) are extracted, one from the position where a nodule candidate is located, and the other from the anatomically corresponding location in the opposite lung, which contains similar normal structures. A wavelet-based, multiresolution image registration method is employed for matching the two ROIs, and subtraction is performed. If no structure remains in the subtracted ROI, then the original ROI is identified as negative (i.e., it contains only normal structures); otherwise, it is regarded as positive (i.e., it contains a nodule). A measure that quantifies the remaining structures was developed to distinguish between nodules and false positives. Application of the method to clinical chest radiographs showed that it was effective in eliminating normal anatomic structures and reducing the number of false detections in the CAD scheme for detection of lung nodules.

[1]  P. Friedman,et al.  Radiologic errors in patients with lung cancer. , 1981, The Western journal of medicine.

[2]  K. Doi,et al.  Image feature analysis for computer-aided diagnosis: detection of right and left hemidiaphragm edges and delineation of lung field in chest radiographs. , 1996, Medical physics.

[3]  K. Doi,et al.  Computer-aided diagnosis of pulmonary nodules: results of a large-scale observer test. , 1999, Radiology.

[4]  M. Fitzpatrick A Review of Medical Image Registration , 1993 .

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

[6]  G. Casella,et al.  Statistical Inference , 2003, Encyclopedia of Social Network Analysis and Mining.

[7]  Hiroyuki Yoshida,et al.  Bayesian wavelet snake for computer-aided diagnosis of lung nodules , 2000, Integr. Comput. Aided Eng..

[8]  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.

[9]  Manuel G. Penedo,et al.  Computer-aided diagnosis: a neural-network-based approach to lung nodule detection , 1998, IEEE Transactions on Medical Imaging.

[10]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[11]  M. J. Carreira,et al.  Computer-aided diagnoses: automatic detection of lung nodules. , 1998, Medical physics.

[12]  M L Giger,et al.  Computerized detection of abnormal asymmetry in digital chest radiographs. , 1994, Medical physics.

[13]  C. Floyd,et al.  Fractal texture analysis in computer-aided diagnosis of solitary pulmonary nodules. , 1997, Academic radiology.

[14]  Hiroyuki Yoshida Multiscale edge-guided wavelet snake model for delineation of pulmonary nodules in chest radiographs , 2003, J. Electronic Imaging.

[15]  Yali Amit,et al.  A Nonlinear Variational Problem for Image Matching , 1994, SIAM J. Sci. Comput..

[16]  Darshana Mistry,et al.  Survey of Image Registration techniques for Satellite Images , 2022 .

[17]  Hiroyuki Yoshida,et al.  Computerized detection of pulmonary nodules in chest radiographs based on morphological features and wavelet snake model , 2002, Medical Image Anal..

[18]  K Doi,et al.  Digital chest radiography: effect of temporal subtraction images on detection accuracy. , 1997, Radiology.

[19]  Matthew T. Freedman,et al.  Artificial convolution neural network techniques and applications for lung nodule detection , 1995, IEEE Trans. Medical Imaging.

[20]  I. Daubechies Ten Lectures on Wavelets , 1992 .

[21]  K. Doi,et al.  Effect of a computer-aided diagnosis scheme on radiologists' performance in detection of lung nodules on radiographs. , 1996, Radiology.

[22]  M L Giger,et al.  Image feature analysis of false-positive diagnoses produced by automated detection of lung nodules. , 1992, Investigative radiology.

[23]  Ma Bin-rong,et al.  A review of medical image registration , 1999 .

[24]  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.

[25]  C. Metz,et al.  Maximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data. , 1998, Statistics in medicine.

[26]  M. Giger,et al.  Digital image subtraction of temporally sequential chest images for detection of interval change. , 1994, Medical physics.

[27]  J C Wandtke,et al.  Computerized search of chest radiographs for nodules. , 1986, Investigative radiology.