Noise Removal in Lung LDCT Images by Novel Discrete Wavelet-Based Denoising With Adaptive Thresholding Technique

Noise Removal in Lung LDCT Images by Novel Discrete Wavelet-Based Denoising With Adaptive Thresholding Technique

Shabana R. Ziyad, Radha V., Thavavel Vaiyapuri
Copyright: © 2021 |Pages: 15
DOI: 10.4018/IJEHMC.20210901.oa1
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Abstract

Cancer is presently one of the prominent causes of death in the world. Early cancer detection, which can improve the prognosis and survival of cancer patients, is challenging for radiologists. Low-dose computed tomography, a commonly used imaging test for screening lung cancer, has a risk of exposure of patients to ionizing radiations. Increased radiation exposure can cause lung cancer development. However, reduced radiation dose results in noisy LDCT images. Efficient preprocessing techniques with computer-aided diagnosis tools can remove noise from LDCT images. Such tools can increase the survival of lung cancer patients by an accurate delineation of the lung nodules. This study aims to develop a framework for preprocessing LDCT images. The authors propose a noise removal technique of discrete wavelet transforms with adaptive thresholding by computing the threshold with a genetic algorithm. The performance of the proposed technique is evaluated by comparing with mean, median, and Gaussian noise filters.
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Noises In Ldct Images

The artifacts in the LDCT images can be classified as patient-related artifacts [Hacking & Cuete (n.d.)], machine-related artifacts, and transmission-related artifacts (Boas & Fleischmann, 2012). Patient-related artifacts include slight movement of the patients undergoing the tests and the body temperature of the patient. Machine-related artifacts include device calibration error, reducing the X-ray flux, and partial volume effects. Transmission-related artifacts occur during the transmission through the communication channel. Noise prevalent in LDCT images is found to be Quantum noise or Poisson noise (Manson et al, 2019) and Gaussian noise (Goyal et al., 2018). Quantum noise originates in LDCT images depending upon the photon count that impinges on the surface of the image receptor from the X-ray source. Photons impinging on the image receptor cannot be equally distributed by any technique, as it is a random process. This random distribution generates Quantum noise. The name arises from the fact that a photon is a quantum of energy. The photon count that falls on the detector is inversely proportional to the noise generated in the image. When the photon count reaches zero, the regions of the image are found to be afflicted with Quantum noise.

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