Detection of Lung Cancer Through Image Processing Methods

Abstract

The purpose of this work is to present an automated Computer Aided Diagnosis (CAD) system for the identification of lung cancer via the analysis of computed tomography images. In order to develop a computer-aided diagnosis system that is effective, it is necessary to find solutions to a number of issues. For the aim of improving early diagnosis and treatment stages, image processing mechanisms have been extensively applied in a range of medical sectors in recent years. This has been done for the purpose of boosting the overall quality of care. In these sectors, the time element is of the biggest significance in order to identify the sickness in the patient as fast as possible. This is especially true in the case of different kind of cancer tumors, such as breast cancer and lung cancer. When it comes to the majority of instances, this procedure will first segment the region of interest, which is the lung, and then continue to analyze the individually obtained area for the identification of nodules in order to arrive at a diagnosis of the illness. Image processing methods that are considered to be standard are used to the CT scan pictures at the beginning of the procedure in order to detect the position of the lung area. These techniques include erosion, median filter, dilation, outlining, and lung border extraction. For the purpose of determining whether or not the lung region is present at the specific place, this operation is carried out. Following that, the segmentation algorithm is performed in order to appropriately identify the malignant nodules that are observed in the lung picture that has been gathered. The cancer nodules are classified using a rule-based method once the segmentation process has been completed. In conclusion, a collection of diagnostic guidelines is arrived at by using the attributes that were retrieved. The NIH/NCI Lung Image Database Consortium (LIDC) is responsible for maintaining a dataset that contains CT images that are utilized for evaluating the suggested approach. These CT pictures are obtained from the LIDC. The research that is advised may be carried out with the help of this dataset to provide the possibility. The DICOM (Digital Imaging and Communications in Medicine) standard has successfully established itself as the industry standard in the area of medical imaging. Standardizing digital medical imaging and data in order to facilitate simple access and sharing is the goal of this initiative. There are a great number of commercial readers that are able to read metadata and support the DICOM image format and format. The major objective of the project is to develop a computer-aided diagnostic (CAD) system that is capable of identifying early lung cancer nodules via the use of computed tomography (CT) images of the lungs. If successful, the system will then classify the nodules as either benign or malignant.