RSNA 2003 

Abstract Archives of the RSNA, 2003


Automated Detection of Pathologic Change from Temporal Subtraction Images of the Chest

Scientific Papers

Presented on December 3, 2003
Presented as part of K19: Physics (Image Processing: CAD V--Lung)


Samuel Armato PhD, PRESENTER: Nothing to Disclose

Abstract: HTML Purpose: We have developed a computerized method for the automated detection of change in pulmonary pathology as demonstrated in temporal subtraction images created from radiographic chest image pairs. Methods and Materials: The lung regions, as segmented during the performance of temporal subtraction, are extracted from the subtraction image. A gray-level histogram is obtained from the pixels within the lung regions. The mode of this histogram is identified, and a gray-level threshold is established at a predefined fraction of this modal value. All pixels with gray levels less than this threshold that lie within the lung regions of the temporal subtraction image remain "on," while all other pixels are set to zero. Area and circularity requirements are imposed on the regions that remain to eliminate false-positive regions. Areas of pathologic change identified in this manner may be presented as outlines in the subtraction image or as highlighted regions in the original radiographic image so that, in effect, temporal subtraction becomes a "background" process for computer-aided diagnosis. Results: As a pilot study, this method was applied to a preliminary database of 12 temporal subtraction images. Six of these images demonstrated no pathologic change between the constituent radiographic images and were considered normal. The other six temporal subtraction images were considered positive for change by an experienced chest radiologist and demonstrated a range of pathology including pleural effusion, interstitial disease, and lung cancer. The method correctly identified six of the eight foci of pathologic change (75%) in the six positive cases and generated no false positives in any of the 12 subtraction images. Conclusion: With a fully automated method for the detection of pathologic change in temporal subtraction images of the chest, 75% of regions demonstrating pathologic change in a pilot database of 12 temporal subtraction images was detected correctly with no false positives. Such a computerized method may help radiologists assimilate the results of temporal subtraction in an intuitive way and aid in the identification of disease development and progression. (S. A., H.M. are shareholders in R2 Technology, Inc., Sunnyvale, CA.) Questions about this event email:

Cite This Abstract

Armato PhD, S, Automated Detection of Pathologic Change from Temporal Subtraction Images of the Chest.  Radiological Society of North America 2003 Scientific Assembly and Annual Meeting, November 30 - December 5, 2003 ,Chicago IL.