Lung Nodule Segmentation on CT Scan Images Using Patchwise Iterative Graph Clustering.

One of the most important steps in lung nodule diagnosis is the automatic segmentation of nodules irrespective of their position and size in the lung parenchyma. In this paper, we propose a new way of applying graph clustering to nodule segmentation. Firstly, the image is preprocessed to extract the lung parenchyma from the CT scan image and identify the region of interest. This is followed by the application of Patchwise Iterative Graph Clustering to spilt the patches and generate superpixels. Next, a region adjacency graph is generated, and agglomerative hierarchical clustering is used to merge the superpixels into different structures such as nodules, and blood vessels. A thresholding algorithm is then used to extract the nodules from the clusters. The proposed method has shown good performance in segmentation with an average dice score of 0.88, an intersection over union score of 0.81, and a high average sensitivity of 89.32 %. Furthermore, the proposed method has been compared to several state-of-the-art methods in the field and has shown an increase in performance in terms of the evaluation metrics.