In this study, we applied image processing of combined contrast limited adaptive histogram equalization (CLAHE) and wavelet de-noise processing in Faster R-CNN with the aim of improving the detection accuracy of nodules in images obtained from chest radiographs. The CNN network was selected between VGG-16 and ResNet50 based on the accuracy of image evaluation. Gradient-weighted class activation mapping (Grad-CAM) was used to verify the observation areas that contributed to the network classification. We then verified the detection accuracy for 53 new images of clinically confirmed nodules. In the image evaluation, the detection accuracy was higher with ResNet50. However, verification of the area of interest in the original images by Grad-CAM revealed that 36.0% of the images focused on areas other than lesions. Lesion detection was then attempted using Faster R-CNN in 104 clinical images. When the number of anchors in the verification was set to 30, the highest detection accuracy was 65.5%. Image processing performed with combined CLAHE and wavelet de-noise processing with Faster R-CNN achieved an accuracy of 76.4% for detecting of nodules in images obtained from chest radiographs.
In Storage Phosphor (SP) used for Computed Radiography (CR), the quite stable latent image remains due to impurities
and the lattice imperfections by the existence of trapped electron and hole. The quite stable latent image appears again
(Ghosting image) by the passage of time etc, is recognized as image, and becomes an artifact in a clinical CR image.
This study verified the influence of Ghosting image on a clinical image by a physical characteristic and the subjective
evaluation, and examined the method to delete this artifact by the exposure of ultraviolet light as a method of improving
image. As a result, Ghosting image can be confirmed by the dose used by the clinical diagnosis study, and it is taken as
deterioration of the granularity on a physical characteristic. The decrease of the granularity of about 15% (by winner
spectrum) was admitted by the frequency band of 2cycle/mm in SP that had been used for a long term.
As the method of improving these, Ghosting image was erased with the ultraviolet light lamp with the peak
wavelength at 310nm, and has band from 290 nm to 320 nm, and is useful for the improvement of the image quality.
In this study, we examine the influence of Ghosting image on a clinical image, and report on the method to delete
them by the exposure to ultraviolet light radiation for the image quality improvement plan that uses the x-ray used for
usual clinical diagnosis study.
The influence of quantum mottle appears as degradation of graininess with reduction of the amount of incidence X-rays. The results of a Wiener spectrum study showed that graininess increased as the dose was reduced, and noise affected all frequencies. However, in clinical images, these effects are seen only in the high-frequency domain above 0.3 cycle/mm. Moreover, the effects of a grid are restricted to a parallel component or a perpendicular component based on its structure. And the influence appears in the decomposition wavelet image of H or V. From these result, the decomposed wavelet coefficients at complete binary tree are divided into seven frequency-coefficient bands. About the noise processing method, we tried to reduce noise by applying modification of Wavelet Transform Modules Maxima method proposed by Mallet, et al. Then we tried to the adaptive nonlinear threshold based on wiener spectrum study and power spectrum study. Based on the above considerations, evaluation was performed using clinical radiographs obtained at a standard dose and reduced dose with the noise reduction processing applied. The results showed that noise caused by quantum mottle and the grid can be reduced by this method without the need for threshold processing based on clinical experience.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.