Abstract:
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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: s-armato@uchicago.edu
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.
http://archive.rsna.org/2003/3102942.html
