Local curvature analysis for classifying breast tumors: Preliminary analysis in dedicated breast CT

Abstract

Purpose: The purpose of this study is to measure the effectiveness of local curvature measures as novel image features for classifying breast tumors. Methods: A total of 119 breast lesions from 104 noncontrast dedicated breast computed tomography images of women were used in this study. Volumetric segmentation was done using a seed-based segmentation algorithm and then a triangulated surface was extracted from the resulting segmentation. Total, mean, and Gaussian curvatures were then computed. Normalized curvatures were used as classification features. In addition, traditional image features were also extracted and a forward feature selection scheme was used to select the optimal feature set. Logistic regression was used as a classifier and leave-one-out cross-validation was utilized to evaluate the classification performances of the features. The area under the receiver operating characteristic curve (AUC, area under curve) was used as a figure of merit. Results: Among curvature measures, the normalized total curvature (C T ) showed the best classification performance (AUC of 0.74), while the others showed no classification power individually. Five traditional image features (two shape, two margin, and one texture descriptors) were selected via the feature selection scheme and its resulting classifier achieved an AUC of 0.83. Among those five features, the radial gradient index (RGI), which is a margin descriptor, showed the best classification performance (AUC of 0.73). A classifier combining RGI and C T yielded an AUC of 0.81, which showed similar performance (i.e., no statistically significant difference) to the classifier with the above five traditional image features. Additional comparisons in AUC values between classifiers using different combinations of traditional image features and C T were conducted. The results showed that C T was able to replace the other four image features for the classification task. Conclusions: The normalized curvature measure contains useful information in classifying breast tumors. Using this, one can reduce the number of features in a classifier, which may result in more robust classifiers for different datasets. C 2015 American Association of Physicists in Medicine.

Optimal reconstruction and quantitative image features for computer‐aided diagnosis tools for breast CT

“…Since bCT is a relatively new imaging modality, there are only a few preliminary studies on CADx algorithms for bCT 7–11 . Ray et al 7 trained and tested artificial neural networks (ANN) using morphologic and texture features extracted from lesions in pre- and post-contrasted bCT images.…”

Relationship between computer segmentation performance and computer classification performance in breast CT: A simulation study using RGI segmentation and LDA classification

“…We considered a total of 23 quantitative image features from the segmentation results (Table , adopted from). Previous studies utilized these features for lesion detection and classification …”

“…Previous studies utilized these features for lesion detection and classification. 5,10,[14][15][16][17][18][19] The 23 quantitative image features included four histogram (feature #1-#4), seven shape (feature #5-#11), five margin (feature #12-#16), four texture (feature #17-#20), and three surface curvature descriptors (feature #21-#23). Histogram descriptors characterize the gray-scale information within the lesion and its relationship to the surrounding background.…”

Galaxy structural analysis with the curvature of the brightness profile

“…This circle is called of osculating circle. The measure of curvature of the path at point P is inversely proportional to the osculating circle of radius R bypassing through that same point whose surface normal vector points towards to the centre of the circle, that is κ ∝ 1/R (see also additional schematics in Crane et al 2013; Lee et al 2015). Along the path, we can understand how changes the curvature of different points from P to P drawing many osculating circles with radii R and R as we move along the path.…”