Characterizing pulmonary nodule shape using a boundary-region approach

Abstract

Using computer-calculated features to characterize the shape of suspicious lesions aims to assist the diagnosis of pulmonary nodules; moreover, these computerized features have to be in agreement with radiologists' ratings measuring their human perception of the nodules' shape. In the Lung Image Database Consortium (LIDC), there exists strong disagreement among the radiologists on the ratings of the shape diagnostic characteristics as well as on their drawn outlines of the extent of the nodules. Since shape is often considered a property of the object boundary and the manual boundaries are not consistent among radiologists, new methods are necessary to, first, define regionbased boundaries that use radiologists' outlines as guides and, second, adapt computer-based shape measurements to use regions rather than the traditional nodule segmentation outlines. This paper introduces a method for defining a boundary region of interest by combining radiologist-drawn outlines (the pixel-set difference between the union and intersection of all radiologist-drawn outlines for a specific nodule), then adapts a radial gradient indexing method for use within image regions, and lastly predicts several composite ratings of sets of radiologists for shape-based characteristics: spiculation, lobulation, and sphericity. The prediction of the majority (mode) rating significantly outperforms earlier work on predicting the ratings of individual radiologists. The prediction of spiculation improves to 53% from 41%, lobulation increases to 44% from 38%, and sphericity improves to 58% from 43%. A binary version of the rating has high accuracy but poor Kappa agreement for all three shape characteristics.

Isolated Pulmonary Nodules Characteristics Detection Based on CT Images

“…3D algorithm [6] analyzed pulmonary nodule characteristics in three-dimensional angle, the algorithm has high algorithm complexity and slow processing time, and it did not establish a unified three-dimensional detection model. GRAD (gradient) algorithm [7] used gradient directionality to discriminate pulmonary nodule characteristics, which has faster speed. RETR (retrieval) algorithm [16] established a retrieval model from the aspect of image information to identify pulmonary nodule characteristics, which has certain discriminating effects.…”

“…Raicu et al [6] constructed a three-dimensional model and then qualitatively judged the characteristics. Horsthemke et al [7] distinguished pulmonary nodules characteristics from gradient directionality. Dhara et al [8] used Gaussian and mean curvature to discriminate characteristics based on differential theory.…”

Quantitative evaluation of margin sharpness of pulmonary nodules in lung CT images

“…Radiologists consider several diagnostic characteristics (namely spiculation, lobulation, sphericity, margin sharpness, and texture etc.) for differentiation of benign and malignant nodules [4–6]. Dhara et al [7] developed several differential geometry‐based techniques for computation of several shape‐based features such as spiculation, lobulation, and sphericity.…”

Toward Understanding the Size Dependence of Shape Features for Predicting Spiculation in Lung Nodules for Computer-Aided Diagnosis

“…New shape features that included Fourier-based descriptors, a variant of the radial gradient index, and a radial normal index were used to predict semantic ratings, including spiculation [11,25]. The authors reported no improvement in predicting semantic ratings, also citing the extent of radiologist disagreement as the primary cause of the poor classification performance.…”

“…Since high inter-observer variation was the primary cause of poor classifier performance cited in several of the studies [6,7,10,11,24,25,28], we propose a new approach of first filtering out inter-observer variation in order to reveal fundamental relationships between shape features and the semantic concept of spiculation. In this context, unlike studies that have as their goal demonstrating the complete performance of a CAD system, we are looking into gaining fundamental insight into the shape feature calculations.…”