# 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. CT scan; image processing; lung cancer; nodule detection +
Footnote †: journal: Medical  
exercising regularly to maintain a healthy weight [6]. On the other hand, early detection can help those who have the condition by enabling them to obtain the best care as soon as possible. Scientists struggle to identify minute nodules because the malignancy is so small at this stage, but it is treatable also. To this end, computer-based systems and devices have been proposed to help radiologists who analyze computed tomography (CT) images to detect lung cancer. In order to design efficient computer-aided diagnosis (CAD) systems for the identification of lung cancer cells, image processing, machine learning, and deep learning have been extensively explored. Most image-processing-based methods use some form of thresholding to identify the nodules in lung CT images, and they have garnered a lot of interest since they allow for the modification of image pixels for more effective analysis. For instance, [PERSON] et al. [7] proposed an automated lung nodule detector that is based on thresholding. Six different features, including the effective diameter, degree of compactness, and irregularity, were computed from detected nodule candidates and used to filter the nodule and non-nodule regions. The method presented in [8] utilizes a watershed for lung segmentation. It uses a marker control watershed and region growing approach to segment potential nodule regions from CT images. Different features are then computed from the binarized segmented lung to identify nodules. A similar method is presented in [9], which uses a simple thresholding-based segmentation technique to extract the lung nodule region. Several features, e.g., area, perimeter, and eccentricity, are then computed to separate nodules. The method in [10] uses morphological procedures as a preprocessing approach; subsequently, they extracted the nodule by subtracting the thresholded image from an object removal mask. A technique presented in [11] denoises CT images using image enhancement techniques and uses histogram equalization to enhance the image quality. The images are then segmented using the watershed approach. Finally, the lung nodule candidates are categorized according to their area, perimeter, and eccentricity. Machine learning (ML) systems can adapt from data, detect patterns, and make different choices without the need for human interaction.