Affiliations: [a] School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| [b] Department of Medical Image, Shanghai Pulmonary Hospital, Shanghai, China
Correspondence:
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Corresponding author: Shengdong Nie, School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Room 206, Complex building C block, No.516, Jungong Road, Yangpu District, Shanghai, 200093 China. E-mail: nsd4647@163.com.
Note: [1] These authors contributed equally to this work and should be considered co-first authors.
Abstract: BACKGROUND:Low dose computed tomography (LDCT) reduces radiation damage to patients. However, with the decrease of radiation dose, LDCT images of the lung often appear some serious problems such as poor contrast, noise and streak artifacts. OBJECTIVE:To improve the quality of lung LDCT images, this study proposed and investigated a new denoising method based on classification training structure combined dictionary for lung LDCT images. METHODS:First, top-hat transform and anisotropic diffusion with a shock filter (ADSF) algorithm are used to enhance the image contrast and image details. Second, an adaptive dictionary is trained and used for noise reduction. Third, more image details are extracted from the residual image by using the atom activity measurement. The final result is obtained by combining the dictionary denoising result with the extracted detail information. The proposed method is then validated by both simulated and clinical lung LDCT images. Four metrics including Contrast-to-Noise Ratio (CNR), Noise Suppression Index (NSI), Edge Preserving Index (EPI), and Blurring Index (BI) are computed to quantitatively evaluate image quality. RESULTS:The results showed that the CNR, NSI, EPI, and BI of our method reached 8.953, 0.9500, 0.7230 and 0.0170, respectively. Noise and streak artifacts can be removed from lung LDCT images while keeping and retaining more details. CONCLUSIONS:Comparing with the results of other methods tested using the same dataset, this study demonstrated that our new method significantly improved quality of the lung LDCT images.
