Image registration methods for contralateral subtraction of chest radiographs

Contralateral subtraction (C-sub) is a computer-aided diagnosis technique for detecting pulmonary nodules in chest radiographs. This technique enhances nodules in a chest image by subtracting its right / left reversed mirror image from the original image. In this paper, we propose a C-sub scheme which uses global and a local registration methods that are newly proposed in this paper. We evaluated the subtraction images obtained by the proposed C-sub scheme using 107 images with nodules in JSRT database, whose subtlety levels are ‘very subtle’, ‘subtle’, and ‘relatively obvious’. First, the quality of rib elimination in subtraction images was evaluated by a radiologist using a three-point rating of 3 (adequate or better), 2 (poor), and 1 (very poor). As the result, 92.5% cases were scored as 3. Next, a radiologist checked whether or not nodules were clearly depicted at the notified locations in subtraction images. And, the quality of nodule depiction was scored as 3 (clearly depicted), 2 (subtly depicted), or 1 (not depicted). As the result, 73.8% cases were scored as 3 and 7.5% cases were scored as 2. The time needed by the proposed scheme was 19.4 seconds for each image on the average of 107 images by a 2.2GHz Intel PC.

[1]  Kunio Doi,et al.  Application of temporal subtraction for detection of interval changes on chest radiographs: Improvement of subtraction images using automated initial image matching , 1999, Journal of Digital Imaging.

[2]  Tsuyoshi Kawaguchi,et al.  Accurate Determination of Lung Boundary from Lung Apex to Costophrenic Angle in Chest Radiographs , 2009, 2009 2nd International Conference on Biomedical Engineering and Informatics.

[3]  Kuni Ohtomo,et al.  Clinical effectiveness of improved temporal subtraction for digital chest radiographs , 2002, SPIE Medical Imaging.

[4]  Paul Suetens,et al.  Temporal subtraction of thorax CR images using a statistical deformation model , 2003, IEEE Transactions on Medical Imaging.

[5]  K. Doi,et al.  Computer-aided diagnostic scheme for the detection of lung nodules on chest radiographs: localized search method based on anatomical classification. , 2006, Medical physics.

[6]  K. Doi,et al.  Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists' detection of pulmonary nodules. , 2000, AJR. American journal of roentgenology.

[7]  H Yoshida,et al.  Contralateral subtraction: a novel technique for detection of asymmetric abnormalities on digital chest radiographs. , 2000, Medical physics.

[8]  S Katsuragawa,et al.  Improved contralateral subtraction images by use of elastic matching technique. , 2000, Medical physics.

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

[10]  K. Doi,et al.  Image feature analysis for computer-aided diagnosis: accurate determination of ribcage boundary in chest radiographs. , 1995, Medical physics.

[11]  Max A. Viergever,et al.  Computer-aided diagnosis in chest radiography: a survey , 2001, IEEE Transactions on Medical Imaging.

[12]  David F. Rogers,et al.  Mathematical elements for computer graphics , 1976 .

[13]  K. Doi,et al.  Iterative image warping technique for temporal subtraction of sequential chest radiographs to detect interval change. , 1999, Medical physics.