Detection of lung cancer using image processing techniques

The diagnosis of lung cancer at an early stage is of utmost importance if it is meant to degrade high mortality rate. The global lung screening program points to visualise positron emission tomography (PET) and computed tomography (CT) examinations amongst most aged groups at risk to enhance the early detection rate. Although use of invasive techniques, symptoms hardly appear until disease is advanced making it difficult for radiologist to identify lesions. Unfortunately, most lung cancer patients suffering at advanced stages result in dismal with five-year survival rate of 17.8% and for distant tumours, being only 4%. Genuine and precise information is the basis of disease control initiatives. More than 85% of the disease is related to tobacco consumption. In addition, genetic factors, exposure to environmental pollutants, second hand smoking inflate disease rapidly. Remedies including chemotherapy, radiotherapy, surgery, epidermal receptive drugs escalate survival rate and quality of life. This method is more about diagnosing at early and crucial stages with intelligent computational techniques with various distortion removals by segmentation techniques and algorithms which is the root concept of image processing. Detection of CT images obtained from cancer institutes is analysed using MATLAB.