Lung Nodule in CT Image Quantification by Using Wavelet Transform

R. Asharani; M. V. Chidananda Murthy

doi:10.57159/jcmm.5.3.26664

Authors

R. Asharani Sri Siddhartha Academy of Higher Education, Tumkur, Karnataka, India https://orcid.org/0009-0001-3168-3196
M. V. Chidananda Murthy Sri Siddhartha Academy of Higher Education, Tumkur, Karnataka, India https://orcid.org/0000-0001-6180-6444

DOI:

https://doi.org/10.57159/jcmm.5.3.26664

Keywords:

Lung Nodule, CT Image, Wavelet Transform, Segmentation, Synthetic Nodule

Abstract

Pulmonary nodule growth rate is a key indicator for lung cancer progression. Doubling time defines the type of nodule and, subsequently, the malignancy rate of lung cancer. Lung nodule diameter measurement contributes to assessing the lung nodule growth rate. The lung computed tomography (CT) image is used to identify pulmonary nodules, which are indicators for lung cancer. Hence, this work addresses lung cancer by segmenting and quantifying lung nodules to inform a therapeutic regimen. The proposed methodology consists of a multistage image processing pipeline designed to improve lung nodule detection accuracy. Initially, the CT image undergoes contrast enhancement using Contrast Limited Adaptive Histogram Equalization (CLAHE) and wavelet-based denoising to suppress noise while reducing false positives. Lung parenchyma is segmented through Otsu thresholding and morphological operations. The parenchyma is subjected to wavelet-based quantization for feature representation. Gaussian smoothing and the Canny edge detector emphasize the lung nodule boundaries. Edge linking and shape-based filtering eliminate the non-nodule structures. Final boundary-extracted nodules are subjected to mean diameter measurement. The performance of the proposed segmentation framework is evaluated on the LIDC-IDRI dataset using standard metrics against traditional segmentation methods. The quantified nodules are tested against synthetic pulmonary nodules for reliable measurement. Overall, the framework demonstrated promising performance as an image analysis tool for CT image processing applications in nodule detection.

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