A parameterized logarithmic image processing method with Laplacian of Gaussian filtering for lung nodule enhancement in chest radiographs

The enhancement of lung nodules in chest radiographs (CXRs) plays an important role in the manual as well as computer-aided detection (CADe) lung cancer. In this paper, we proposed a parameterized logarithmic image processing (PLIP) method combined with the Laplacian of a Gaussian (LoG) filter to en...

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Vydáno v:Medical & biological engineering & computing Ročník 54; číslo 11; s. 1793 - 1806
Hlavní autoři: Chen, Sheng, Yao, Liping, Chen, Bao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2016
Springer Nature B.V
Témata:
Adult
Aged
Algorithms
Analysis
Biomedical and Life Sciences
Biomedical Engineering and Bioengineering
Biomedicine
Chest
Computer Applications
Databases as Topic
Female
Filled plastics
Human Physiology
Humans
Image contrast
Image processing
Image processing systems
Imaging
Lung - diagnostic imaging
Lung cancer
Lungs
Male
Medical imaging
Middle Aged
Neural networks
Nodules
Normal Distribution
Original Article
Radiographic Image Interpretation, Computer-Assisted
Radiographs
Radiography
Radiography, Thoracic
Radiology
ROC Curve
Studies
X-rays
Young Adult
Chest radiographs (CXRs)
Parameterized logarithmic image processing (PLIP)
Laplacian of Gaussian (LoG)
Lung nodules
Image enhancement
ISSN:0140-0118, 1741-0444, 1741-0444
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Shrnutí:The enhancement of lung nodules in chest radiographs (CXRs) plays an important role in the manual as well as computer-aided detection (CADe) lung cancer. In this paper, we proposed a parameterized logarithmic image processing (PLIP) method combined with the Laplacian of a Gaussian (LoG) filter to enhance lung nodules in CXRs. We first applied several LoG filters with varying parameters to an original CXR to enhance the nodule-like structures as well as the edges in the image. We then applied the PLIP model, which can enhance lung nodule images with high contrast and was beneficial in extracting effective features for nodule detection in the CADe scheme. Our method combined the advantages of both the PLIP algorithm and the LoG algorithm, which can enhance lung nodules in chest radiographs with high contrast. To test our nodule enhancement method, we tested a CADe scheme, with a relatively high performance in nodule detection, using a publically available database containing 140 nodules in 140 CXRs enhanced through our nodule enhancement method. The CADe scheme attained a sensitivity of 81 and 70 % with an average of 5.0 frame rate (FP) and 2.0 FP, respectively, in a leave-one-out cross-validation test. By contrast, the CADe scheme based on the original image recorded a sensitivity of 77 and 63 % at 5.0 FP and 2.0 FP, respectively. We introduced the measurement of enhancement by entropy evaluation to objectively assess our method. Experimental results show that the proposed method obtains an effective enhancement of lung nodules in CXRs for both radiologists and CADe schemes.
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ISSN:0140-0118
1741-0444
1741-0444
DOI:10.1007/s11517-016-1469-x