Optimal Thresholding for Multi-Window Computed Tomography (CT) to Predict Lung Cancer

Abstract

Lung cancer is the world’s second-largest cause of cancer mortality. Patients’ lives can be saved if this malignancy is detected early. Doctors, however, encounter difficulties in detecting cancer in computed tomography (CT) images. In recent years, significant research has been devoted to producing automated lung nodule detection methods that can help radiologists. Most of them use only the lung window in their analysis and generally do not consider the mediastinal windows, which, according to recent research, carry important information. In this paper, we propose a simple yet effective algorithm to analyze multi-window CT images for lung nodules. The algorithm works in three steps. First, the CT image is preprocessed to suppress any noise and improve the image quality. Second, the lungs are extracted from the preprocessed image. Based on the histogram analysis of the lung windows, we propose a multi-Otsu-based approach for lung segmentation in lung windows. The case of mediastinal windows is rather difficult due to irregular patterns in the histograms. To this end, we propose a global–local-mean-based thresholding technique for lung detection. In the final step, the nodule candidates are extracted from the segmented lungs using simple intensity-based thresholding. The radius of the extracted objects is computed to separate the nodule from the bronchioles and blood vessels. The proposed algorithm is evaluated on the benchmark LUNA16 dataset and achieves accuracy of over 94% for lung tumor detection, surpassing that of existing similar methods.

Enhanced Lung Nodule Segmentation using Dung Beetle Optimization based LNS-DualMAGNet Model

“…They are also classifiable:The terms "circumscribed" (W), "vascularized" (V), "juxta-pleural" (J), "pleural-tail" (P), "pleural surface" (J), "pleuralized" (V), "situated in the very centre of the lung and not connected to any other tissues," and "GGO" (G) refer to the various anatomical regions of the lung related to blood vessels [7], which is identical to W but differs in that it has a nonsolid CT value distribution. Computer Tomography (CT) is the gold standard for diagnosis in clinical practise since it can obtain fine-grained data for lung lesions and surrounding structures [8,9]. patterns and textures in lung images, which facilitates more precise identification of potential nodules.…”