Detection & Quantification of Lung Nodules Using 3D CT images
Open Access
- 29 January 2023
- journal article
- Published by 50Sea in International Journal of Innovations in Science & Technology
- Vol. 5 (1), 68-81
- https://doi.org/10.33411/ijist/2023050105
Abstract
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 have taken 3D images and those are high-resolution images. We have compared the accuracy of the existing mask and our segmented images. The segmentation method that we have applied to these 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 by its x, y, and z components. like a parametric equation where the line crossing through a targeted point by that nodule is more dominated. The Frangi filter was to give a geometric shape to the nodule and we got 90% accurate detection. 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 as improving sensitivity, decreasing false positives, and optimizing the identification of each type of nodule, even those of varying size and form.Keywords
This publication has 30 references indexed in Scilit:
- Lungs Cancer Nodules Detection from CT Scan Images with Convolutional Neural NetworksPublished by Springer Science and Business Media LLC ,2019
- Multi-resolution convolutional networks for chest X-ray radiograph based lung nodule detectionArtificial Intelligence in Medicine, 2019
- 3D deep learning for detecting pulmonary nodules in CT scansJournal of the American Medical Informatics Association, 2018
- Multistage segmentation model and SVM-ensemble for precise lung nodule detectionInternational Journal of Computer Assisted Radiology and Surgery, 2018
- Small-Cell Lung Cancer Detection Using a Supervised Machine Learning AlgorithmPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2017
- Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017Radiology, 2017
- Segmentation of lung nodule in CT data using active contour model and Fuzzy C-mean clusteringAlexandria Engineering Journal, 2016
- Segmentation and Analysis of CT Chest Images for Early Lung Cancer DetectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Lung Cancer detection and Classification by using Machine Learning & Multinomial BayesianIOSR Journal of Electronics and Communication Engineering, 2014
- Feature Subset Selection for Improving the Performance of False Positive Reduction in Lung Nodule CADIEEE Transactions on Information Technology in Biomedicine, 2006