Identifying Lung Cancer Using Image Processing Techniques

The automated Computer Aided Diagnosing (CAD) system is proposed in this paper for detection of lung cancer form the analysis of computed tomography images. To produce a successful Computer Aided Diagnosis system, several problems has to be resolved. In recent years the image processing mechanisms are used widely in several medical areas for improving earlier detection and treatment stages, in which the time factor is very important to discover the disease in the patient as possible as fast, especially in various cancer tumors such as the lung cancer, breast cancer. This system generally first segments the area of interest (lung) and then analyzes the separately obtained area for nodule detection in order to diagnosis the disease. Initially, the basic image processing techniques such as Erosion, Median Filter, Dilation, Outlining, and Lung Border Extraction are applied to the CT scan image in order to detect the lung region. Then the segmentation algorithm is applied in order to detect the cancer nodules from the extracted lung image. After segmentation, rule based technique is applied to classify the cancer nodules. Finally, a set of diagnosis rules are generated from the extracted features. For experimentation of the proposed technique, the CT images are obtained from a NIH/NCI Lung Image Database Consortium (LIDC) dataset that provides the chance to do the suggested research. DICOM (9) (Digital Imaging and Communications in Medicine) has become a standard for medical imaging. Its purpose is to standardize digital medical imaging and data for easy access and sharing. There are many commercial viewers that support DICOM image format and can read metadata. The main objective of the project is to develop a CAD (Computer Aided Diagnosis) system for finding the early lung cancer nodules using the lung CT images and classify the nodules as Benign or Malignant.

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