A quality improvement method for lung LDCT images

Autor:	Xi-Wen Sun, Yang Chen, Hui-Hong Duan, Shengdong Nie, Gao Lei, Dai Xiaoting
Rok vydání:	2020
Předmět:	Lung Neoplasms Computer science Image quality Anisotropic diffusion Noise reduction Streak Top-hat transform Residual Radiation Dosage 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Humans Radiology Nuclear Medicine and imaging Electrical and Electronic Engineering Instrumentation Lung Radiation business.industry Pattern recognition Filter (signal processing) Condensed Matter Physics Quality Improvement Noise 030220 oncology & carcinogenesis Radiographic Image Interpretation Computer-Assisted Artificial intelligence business Tomography X-Ray Computed
Zdroj:	Journal of X-ray science and technology. 28(2)
ISSN:	1095-9114
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5f42e3a98fa8e7f6172d52b58f8e2f3d https://pubmed.ncbi.nlm.nih.gov/32039881 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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