The purpose of this research is to develop a method for recognizing shapes of ribs in chest x-rays, which can be utilized
as intelligent assistance to diagnosis to decrease false positives (FPs) due to ribs in chest CAD and automatically
generate a schema in report. Shapes of ribs are manually extracted from several CR images to create a rib shape model
using PDM, in which shapes of anterior/posterior ribs are represented as sets of coordinates and an arbitrary shape of a
rib is expressed only with principle components that have a high contribution ratio to shape variation. Shapes of ribs in a
chest X-ray image are identified as follows: (a) Identify the lung field. (b) Find an allowable range of weights of
principle components in the shape model within which the model aligns to an edge of the lung field (a). (c) Create
several shape model images by applying different weights of principle components. (d) Apply a six-direction Gabor
filter to the X-ray image and each one of the shape model images to create an image containing only rib elements. (e)
From images created in (d), search for a shape model image that shows the highest correlation coefficient with the X-ray
image.We applied the rib shape model to 100 test images while changing weights of principle components. We were
able to identify positions of ribs and anatomical rib numbers with an average margin of error being no more than two
fifths of a rib and a half of a rib in case of anterior ribs.
We have performed a retrospective evaluation to assess the effects of temporal subtraction in chest screening. Nineteen abnormal and 30 normal examples of chest CR screening images were selected, and a data set comprising current, previous, and temporal subtraction (T-sub) images were generated. Abnormal examples were chosen from cases with abnormal findings that were confirmed by subsequent CT scanning to need close examination. In the evaluation experiments, only current and previous chest images were displayed at first. After a radiograph observer judged the existence of any abnormal findings by the 5-step rating method, the same observer judged the case again using the T-sub image. Physicians of three different categories, namely diagnostic radiologists with clinical experience of two years or more, less experienced radiologists, and non-radiologists participated in the experiments. As a result of ROC analysis, it was confirmed that the use of temporal subtraction improves the Az value by about 10% overall. The Az value increase was more significant in the group of less experienced doctors. The ROC curves using T-sub image of these physicians approached those of the well-experienced radiologists. These results strongly indicate the clinical efficacy of the assessed system in chest screening.
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