A novel lung extraction approach for LDCT images using discrete wavelet transform with adaptive thresholding and Fuzzy C-means clustering enhanced by genetic algorithm

Ziyad, Shabana R.; Radha, V.; Vayyapuri, Thavavel

doi:10.1007/s42600-022-00210-6

Cited by 3 publications

(3 citation statements)

References 26 publications

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“…Peng et al [21] proposed a semi-supervised lung contour detection algorithm, which used a few points of region of interest (ROI) as an approximate initialization, and the approximate contours of lungs were extracted by combining a closed polygonal line and a back propagation neural network model. In reference [22], the lung region was segmented by genetic algorithm-enhanced k-means clustering and genetic algorithm-enhanced FCM clustering. Liu et al [23] proposed a clustering method based on matrix grey incidence.…”

Section: Related Workmentioning

confidence: 99%

Segmenting lung parenchyma from CT images with gray correlation‐based clustering

Liu

Xie

Zhao

et al. 2023

IET Image Processing

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Lung segmentation, a prerequisite step of lung disease detection in computer‐aided diagnosis system, is a challenging task because of noises, complex structures, as well as large individual differences of lung CT scans. Here, an automatic algorithm for segmenting lungs from thoracic CT images accurately is presented. This scheme consists of three principal steps: image preprocessing, lung extracting and contour correcting. To cope with inhomogeneous intensities of CT images, a novel preprocessing approach based on empirical mode decomposition and bilateral filter is proposed, which has abilities of denoising, smoothing and edge keeping. Lung region is then extracted with a novel gray correlation‐based clustering approach. A new lung contour correction technology is finally employed to repair the concave regions caused by pulmonary nodules, vessels and so on. Experimental results show that the preprocessing approach outperforms other methods on image denoising and smoothing. Meanwhile, the lung segmentation algorithm is tested on a group of lung CT images affected with interstitial lung diseases and achieves a high segmentation accuracy. Compared with several existing lung segmentation methods, this algorithm exhibits a better performance on lung segmentation.

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Section: Related Workmentioning

confidence: 99%

Segmenting lung parenchyma from CT images with gray correlation‐based clustering

Liu

Xie

Zhao

et al. 2023

IET Image Processing

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“…where a > 0 is the scale factor and b is the shift factor. For music signals, a type of digital signal, the discrete wavelet transform 15 is required in the noise reduction process, and the relevant formula is:…”

Section: Wavelet Transformmentioning

confidence: 99%

“…For music signals, a type of digital signal, the discrete wavelet transform 15 is required in the noise reduction process, and the relevant formula is:where 〈f,Ψnormalm,normaln(t)〉 represents the inner product of f and normalΨm,n(normalt).…”

Section: Several Music Signal Noise Reduction Algorithmsmentioning

confidence: 99%

A comparative study of multiple music signal noise reduction algorithms in music processing

2023

Noise & Vibration Worldwide

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Electronic music is susceptible to noise in production, which needs to be processed. This paper analyzed several commonly used noise reduction algorithms, including wiener filtering, wavelet transform, spectral subtraction, and improved spectral subtraction, and then compared the noise reduction performance of several algorithms by producing noisy music datasets in the audio analysis tool librosa. It was found from the experimental results that the wavelet transform algorithm performed best when sym3 was used as the wavelet basis function, and the number of decomposition layers was 7. The comparison of different algorithms showed that the performance of the wiener filtering algorithm was poor in reducing noise, and the signal-to-noise ratio (SNR) and signal distortion ratio (SDR) was low; the performance of the improved spectral subtraction algorithm was the best, and the SNR and SDR were 20.36 dB and 17.94 dB, respectively, when the SNR was −8 dB, which were better than the other algorithms. The experimental results demonstrate the reliability of the improved spectral subtraction method in music signal noise reduction. The algorithm can be applied in practical music processing.

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A Dual-Channel Network Based GAN for Low-Dose CT Image Denoising

Zhao

Guo

Han

et al. 2022

2022 China Automation Congress (CAC)

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A novel lung extraction approach for LDCT images using discrete wavelet transform with adaptive thresholding and Fuzzy C-means clustering enhanced by genetic algorithm

Cited by 3 publications

References 26 publications

Segmenting lung parenchyma from CT images with gray correlation‐based clustering

Segmenting lung parenchyma from CT images with gray correlation‐based clustering

A comparative study of multiple music signal noise reduction algorithms in music processing

A Dual-Channel Network Based GAN for Low-Dose CT Image Denoising

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