Detection and description of pulmonary nodules through 2D and 3D clustering

Precise 3D automated detection, description and classification of pulmonary nodules offer the potential for early diagnosis of cancer and greater efficiency in the reading of computerised tomography (CT) images. CT scan centres are currently experiencing high loads and experts shortage, especially in developing countries such as Iraq where the results of the current research will be used. This motivates the researchers to address these problems and challenges by developing automated processes for the early detection and efficient description of cancer cases. This research attempts to reduce workloads, enhance the patient throughput and improve the diagnosis performance. To achieve this goal, the study selects techniques for segmentation, classification, detection and implements the best candidates alongside a novel automated approach. Techniques for each stage in the process are quantitatively evaluated to select the best performance against standard data for lung cancer. In addition, the ideal approach is identified by comparing them against other works in detecting and describing pulmonary nodules. This work detects and describes the nodules and their characteristics in several stages: automated lung segmentation from the background, automated 2D and 3D clustering of vessels and nodules, applying shape and textures features, classification and automatic measurement of nodule characteristics. This work is tested on standard CT lung image data and shows promising results, matching or close to experts’ diagnosis in the nodules number and their features (size/volume, location) and in terms the accuracy and automation. It also achieved a classification accuracy of 98% and efficient results in measuring the nodules’ volume automatically

Describing Pulmonary Nodules Using 3D Clustering

After detecting a node (tumor) on medical images, it is necessary to determine its shape, localization and type. This is important for the choice of the type of clinical intervention and other aspects of the work of radiologists. Computed detection systems effectively locate nodes using 2D computed tomography (CT) imaging of the lungs. However, a more detailed description of the node (tumor) is still a big problem. In the framework of this work, three-dimensional clustering was performed on volumetric CT images, which give an idea of ​​the node and its structure. These materials were used to describe the development of the node in successive sections of the lung. Combined algorithms for clustering and determining the characteristics of nodes in 3D visualization. Some 3D features were applied to objects grouped by K-means CT lung imaging. This approach provides a visual study of the three-dimensional shape and location of the node. This study is mainly focused on clustering in 3D in order to obtain complex information missing from the radiologist's report. In addition, to evaluate the proposed system, we used a 3D density clustering algorithm for spatial data with the presence of noise and another 3D application - a graph. The proposed method detected a difficult case and automatically provided information about the types of nodes (globular, juxtapleural, and pleural-caudal). The algorithm is tested on standard data, Based on the proposed model, it is possible to cluster lung nodes in 3D CT and determine a set of characteristics such as shape, location, and type