Nodule detection in lung using multi-threshold segmentation

Abstract

Presence of nodules in lung images can be an indication of multiple types of diseases such as tumor, cancer, etc. Detection of nodules for lung images is a ubiquitous task, which requires lot of computations for pre-processing, tissue detection, removal of non-nodule regions and finally nodule segmentation. In this paper we propose a multi-threshold descriptor based algorithm which applies multiple levels of thresholds to the image, in order to detect and remove all the non-nodule regions and finally uses KNN algorithm in order to classify the input image into benign or malignant. The training and testing sets are carefully selected in order to obtain optimal accuracy for the system. In this work, we obtain 82.65% accuracy, sensitivity and specificity is 85.71% and 80.35% respectively for classification of the input medical image.