Image segmentation and feature extraction method for lung lesion detection in computed tomography images

Abstract

Abstract Lung cancer is a form of cancer that causes uncontrollable cell growth in the lungs. Patients with lung cancer frequently miss a treatment, face higher health care costs, and get the worst outcomes. The detection of the existence of lung cancer can be performed in a variety of ways, such as computed tomography (CT), magnetic resonance imaging (MRI), and radiography. Many researchers have developed ways of automating lung cancer diagnosis using image processing techniques because of the noise and low image quality between the cancer cells, the lung, and the background. This study develops an image processing technique that uses image segmentation algorithms to segment lung nodules in computed tomography images using feature extraction. In the initial phase, it is essential to establish a rigorous image processing framework with the following sequential steps: (i) object edge identification and (ii) lesion boundary recognition. The architecture includes image processing techniques, thresholding, and morphological detections (erosion and dilation). Lesions can have various sizes and shapes, both regular and irregular. The new method has been applied to find the lesions using their roundness size. In addition to learning purely from CT scans, the previously studied lesion characteristics are also integrated. Data was collected from the Advanced Medical and Dental Institute (AMDI), Universiti Sains Malaysia, Penang. The manual segmentation was used image segmented in the MATLAB software function to remove the background of the images. The perimeter evaluates such as accuracy, recall, and F-score. Based on the analysis the performance of lung lesion segmentation of accuracy is 99.95, recall at 45.76%, and the F-score is 60.67%. For lung lesion detection, the results shows it consist of 3-5 slices with the value of roundness. Besides, lesion detection also have continuity for the roundness value. The experiment results found clear support for the next step of this research for classifications of lesions.