Local curvature analysis for classifying breast tumors: Preliminary analysis in dedicated breast CT.
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| Title: | Local curvature analysis for classifying breast tumors: Preliminary analysis in dedicated breast CT. |
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| Authors: | Lee, Juhun1, Nishikawa, Robert M.1, Reiser, Ingrid2, Boone, John M.3, Lindfors, Karen K.3 |
| Source: | Medical Physics. Sep2015, Vol. 42 Issue 9, p5479-5489. 11p. |
| Subjects: | Breast tumors, Tumor classification, Cancer tomography, Cancer in women, Medical imaging systems, Image segmentation |
| 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 109330552 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Local curvature analysis for classifying breast tumors: Preliminary analysis in dedicated breast CT. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lee%2C+Juhun%22">Lee, Juhun</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Nishikawa%2C+Robert+M%2E%22">Nishikawa, Robert M.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Reiser%2C+Ingrid%22">Reiser, Ingrid</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Boone%2C+John+M%2E%22">Boone, John M.</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Lindfors%2C+Karen+K%2E%22">Lindfors, Karen K.</searchLink><relatesTo>3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Sep2015, Vol. 42 Issue 9, p5479-5489. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Breast+tumors%22">Breast tumors</searchLink><br /><searchLink fieldCode="DE" term="%22Tumor+classification%22">Tumor classification</searchLink><br /><searchLink fieldCode="DE" term="%22Cancer+tomography%22">Cancer tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Cancer+in+women%22">Cancer in women</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+imaging+systems%22">Medical imaging systems</searchLink><br /><searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1118/1.4928479 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 5479 Subjects: – SubjectFull: Breast tumors Type: general – SubjectFull: Tumor classification Type: general – SubjectFull: Cancer tomography Type: general – SubjectFull: Cancer in women Type: general – SubjectFull: Medical imaging systems Type: general – SubjectFull: Image segmentation Type: general Titles: – TitleFull: Local curvature analysis for classifying breast tumors: Preliminary analysis in dedicated breast CT. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lee, Juhun – PersonEntity: Name: NameFull: Nishikawa, Robert M. – PersonEntity: Name: NameFull: Reiser, Ingrid – PersonEntity: Name: NameFull: Boone, John M. – PersonEntity: Name: NameFull: Lindfors, Karen K. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2015 Type: published Y: 2015 Identifiers: – Type: issn-print Value: 00942405 Numbering: – Type: volume Value: 42 – Type: issue Value: 9 Titles: – TitleFull: Medical Physics Type: main |
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