Local curvature analysis for classifying breast tumors: Preliminary analysis in dedicated breast CT. Issue 9 (24th August 2015)

• Record Type: Journal Article
• Authors: Lee, Juhun; Nishikawa, Robert M.; Reiser, Ingrid; Boone, John M.; Lindfors, Karen K.
• Is Part Of: Medical physics. Volume 42:Issue 9(2015)
• Page Start: 5479
• Page End: 5489
• Publication Date: 2015-08-24
• Subjects: computerised tomography -- image segmentation -- medical image processing -- regression analysis -- tumours; Segmentation -- Computed tomography -- Probability theory, stochastic processes, and statistics; Computerised tomographs -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; breast CT -- classification -- CADx -- curvature -- image feature analysis; Cancer -- Medical image segmentation -- Three dimensional image processing -- Image analysis -- Computed tomography -- Computer aided diagnosis -- Mammography -- Medical image reconstruction -- Three dimensional sensing; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4928479
• Languages: English
• ISSNs: 0094-2405

Abstract

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 ( CT ) 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 CT 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 CT were conducted. The results showed that CT 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.