Abstract
Among the different types of cancers, lung cancer is one of the widespread diseases which causes the highest number of deaths every year. The early detection of lung cancer is very essential for increasing the survival rate in patients. Although computed tomography (CT) is the preferred choice for lungs imaging, sometimes CT images may produce less tumor visibility regions and unconstructive rates in tumor portions. Hence, the development of an efficient segmentation technique is necessary. In this paper, water cycle bat algorithm- (WCBA-) based deformable model approach is proposed for lung tumor segmentation. In the preprocessing stage, a median filter is used to remove the noise from the input image and to segment the lung lobe regions, and Bayesian fuzzy clustering is applied. In the proposed method, deformable model is modified by the dictionary-based algorithm to segment the lung tumor accurately. In the dictionary-based algorithm, the update equation is modified by the proposed WCBA and is designed by integrating water cycle algorithm (WCA) and bat algorithm (BA).
Highlights
Lung cancer is considered as the second most common kind of cancer for both male and females worldwide
As per the World Health Organization (WHO) statistics, 1.3 million deaths are happening because of lung cancer. It is calculated in the United States (US) that every year, approximately 228,820 people are newly affected by lung cancer in which 112,520 are women and 116,300 are men
The experiment is conducted on three sample Computed tomography (CT) images
Summary
Lung cancer is considered as the second most common kind of cancer for both male and females worldwide. As per the World Health Organization (WHO) statistics, 1.3 million deaths are happening because of lung cancer. Computed tomography (CT) is a basic imaging modality which effectively helps for the detection of lung cancers. The initial process of lung cancer detection is manual detection of lung regions in CT images by specialists, which is a more challenging and tedious process for computer vision models. The number of deaths due to lung cancer can be considerably decreased, when the lung CT screening is effective. It is a challenging process for radiologists to make effective and precise detection for large scale of CT images. Segmentation of CT images plays a very significant role in lung cancer detection [5].
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