Optimistic Diagnosis of Human Lung Cancer on CT using Image Processing

It is highly important to detect the lung cancer in earlier stages with minimum time delay and provide a better solution to reduce the lung cancer. There are few methods available to detect cancerous cells. Diagnosis is mostly based on Computed Tomography (CT) images. The approach presented in this focuses on finding nodules, early symptoms of the diseases, appearing in patient’s lungs. An analysis of medical images by computer was introduced and it includes some steps such as Image Enhancement, Segmentation and feature extraction. In this work median filter, Gabor filter and Histogram is used for Image Enhancement. Image Segmentation implemented by Watershed and morphological operations which has some benefits such as fast processing. Feature extraction stage is an important stage that uses algorithms and techniques to detect and isolate various desired portions of a given image. To predict the probability of lung cancer presence GLCM are used. Image quality and accuracy is the core factors of this project.