An ensemble shape gradient features descriptor based nodule detection paradigm: a novel model to augment complex diagnostic decisions assistance

Abstract

Primarily, there are three basic operational constituents of Nodule Detection Systems namely nodule candidate detection, classification of nodule and extraction of features. Thresholding is one of the most important factor for nodule detection. To segment the lungs and nodules, Gaussian approximation based Particle Swarm Optimization (PSO) is used to determine the optimal threshold value. After extracting lungs part, 2D and 3D region of interests (ROI’s) are used to detect nodules with area and volume information of nodules and then distinguish between wall and vessels by using fuzzy C-mean. There are three key objects namely wall, nodule and vessel in the lugs volume with specific shape. Shape-based features with Histogram of Oriented Surface Normal Vectors (HOSNV) are used as a feature descriptor. A scaled and rotation invariant multi-coordinate histogram of thegradient is used to identify nodules with different sizes and directionless shapes. So, a Novel Ensemble Shape Gradient Features (NESGF) descriptor for pulmonary nodule classification is proposed using the Histogram of Oriented Surface Normal Vectors and Multi-Coordinate Histogram of Gradient descriptor. The random forest has been used to classify the nodules through intelligent usage of the ensemble concepts to learn weak classifiers. A standard benchmark database Lung Image Consortium Database (LICD) is used for testing and validation purposes. In order to show the performance of segmentation quality, the proposed model is compared through three quantitative measures inclusive of Variation of Information (VoI), Probabilistic Rand Index (PRI) and Jaccard Measure. The methods Area Under Curve, Sensitivity, Specificity and Sensitivity have are used for classification. For classification, accuracy, sensitivity, specificity and Area under curve (AUC) has been used.