## 1 Introduction  
Lung cancer is a disease that mainly affects the lungs, which is the principal organ of the respiratory system and performs vital activities for human survival. It usually starts once the epithelial cells of the bronchioles or alveoli grow abnormally. If the lungs do not carry out their functions properly, due to lung cancer, other parts of the body can also be affected, which can eventually lead to death. This fact makes the mortality rate of lung cancer higher than those of other types of cancer. The global cancer data by the World Health Organization (WHO) stated that lung cancer reached the most significant number of deaths, 1.8 million, which was 18.4% of the total cancer deaths in 2018 [1]. Effective ways to reduce the mortality of lung cancer include preventions, early detections, and precise treatments. However, most patients usually notice lung cancer at the advanced and uncurable stages because lung cancer is asymptomatic at the immature stage of the disease. Importantly, since the advent of medical imaging technologies, computed tomography (CT) became an effective modality for the early diagnosis of lung cancer. According to the National Lung Screening Trial (NLST), the mortality rate of lung cancer reduced up to 15%-20% in patients who performed low-dose CT screening [2]. CT can provide detail morphologic information about lung cancer by detecting abnormal lesions inside the lungs, called pulmonary nodules. They are the round or oval-shaped opacities on the CT scans that have well-defined or irregular margins. Nodules are generally regarded as an early signal of lung cancer. They form due to the inflammation of pulmonary structures, and often react to infection and diseases. However, not all the nodules discovered in the lungs are cancerous. They can be categorized into two types, namely, benign and malignant. Benign nodules are commonly found in almost all CT examinations, but they are not cancer. However, malignant nodules are cancerous and can even metastasize to other parts of the body. There are several clinical factors to distinguish benign and malignant pulmonary nodules, such as size, location, calcification, margin-types, and texture [3]. With the increasing number of lung cancer cases, manual detection of nodules has become a challenging task. It is effort-intensive and error-prone because the interpreter has to read the massive amount of CT scans slice-by-slice. Moreover, this task requires a high proficiency by the interpreters as an incorrect or miss-detection of the potential malignant nodule is highly-risky for patients. For these reasons, automated nodule detection schemes were strongly demanded as supportive tools. They were developed with aim of providing second options for better, more confident, and reliable diagnosis results. Additionally, they can make the interpretation process faster and more convenient by stacking the multiple sequential CT slices together as volumetric images. Different research on automated nodule detections have emerged over the years. Generally, most of these works are based on two fundamental steps: (i) suspicious nodule detection and (ii) false reduction. The first step aims to extract the lesions that have a high possibility of being a nodule. In this step, the lung regions from the input CT scan are initially segmented. Afterwards, potential nodules scattered in the segmented lungs are extracted. The existing segmentation methods for this step can be categorized into four major groups: intensity-based, region-based, template-based, and machine-learning-based methods. Intensity-based segmentations are the fastest and simplest methods. They segment the region of interest (ROI) by grouping image pixels based on one or more predefined grey-value. Nodule detections using fixed thresholding [4; 5], [PERSON]'s thresholding [6], and optimal thresholding [7; 8; 9] are examples of the intensity-based methods. While they are easy to use, the effectiveness of these methods strongly depends on the predefined or initial threshold values.