Detection & Quantification of Lung Nodules Using 3D CT images

In computer vision image detection and quantification play an important role. Image Detection and quantification is the process of identifying nodule position and the amount of covered area. The dataset which we have used for this research contains 3D CT lung images. In our proposed work we havetaken 3D images and those arehigh-resolution images. We have compared the accuracy of the existing mask and our segmented images. The segmentation method that we have appliedtothese images is Sparse Field Method localized region-based segmentation and for Nodule detection,I have used ray projection. The ray projection method is efficient for making the point more visible byitsx,y,and z components. likea parametric equation where the line crossing through a targeted point by that nodule is more dominated. The Frangi filter was to give a geometric shapetothe noduleandwegot90%accuratedetection.The high mortality rate associated with lung cancer makes it imperative that it be detected at an early stage.The application of computerized image processing methods has the potential to improve both the efficiency and reliability of lung cancer screening. Computerized tomography (CT) pictures are frequently used in medical image processing because of their excellent resolution and low noise. Computer-aided detection systems, including preprocessing and segmentation methods, as well as data analysis approaches, have been investigated in this research for their potential use in the detection and diagnosis of lung cancer. The primary objective was to research cutting-edge methods for creating computational diagnostic tools to aid in the collection, processing, and interpretation of medical imaging data. Nonetheless, there are still areas that need more work, such asimproving sensitivity, decreasing false positives, and optimizing the identification of each type of nodule, even those of varying size and form.