The Impact of Pixel Resolution, Integration Scale, Preprocessing, and Feature Normalization on Texture Analysis for Mass Classification in Mammograms
Texture analysis methods are widely used to characterize breast masses in mammograms. Texture gives information about the spatial arrangement of the intensities in the region of interest. This information has been used in mammogram analysis applications such as mass detection, mass classification, a...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-01-01
|
| Series: | International Journal of Optics |
| Online Access: | http://dx.doi.org/10.1155/2016/1370259 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832552593544445952 |
|---|---|
| author | Mohamed Abdel-Nasser Jaime Melendez Antonio Moreno Domenec Puig |
| author_facet | Mohamed Abdel-Nasser Jaime Melendez Antonio Moreno Domenec Puig |
| author_sort | Mohamed Abdel-Nasser |
| collection | DOAJ |
| description | Texture analysis methods are widely used to characterize breast masses in mammograms. Texture gives information about the spatial arrangement of the intensities in the region of interest. This information has been used in mammogram analysis applications such as mass detection, mass classification, and breast density estimation. In this paper, we study the effect of factors such as pixel resolution, integration scale, preprocessing, and feature normalization on the performance of those texture methods for mass classification. The classification performance was assessed considering linear and nonlinear support vector machine classifiers. To find the best combination among the studied factors, we used three approaches: greedy, sequential forward selection (SFS), and exhaustive search. On the basis of our study, we conclude that the factors studied affect the performance of texture methods, so the best combination of these factors should be determined to achieve the best performance with each texture method. SFS can be an appropriate way to approach the factor combination problem because it is less computationally intensive than the other methods. |
| format | Article |
| id | doaj-art-c84ba7bee8464c5cbcd884a08fa1e178 |
| institution | Kabale University |
| issn | 1687-9384 1687-9392 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Optics |
| spelling | doaj-art-c84ba7bee8464c5cbcd884a08fa1e1782025-02-03T05:58:20ZengWileyInternational Journal of Optics1687-93841687-93922016-01-01201610.1155/2016/13702591370259The Impact of Pixel Resolution, Integration Scale, Preprocessing, and Feature Normalization on Texture Analysis for Mass Classification in MammogramsMohamed Abdel-Nasser0Jaime Melendez1Antonio Moreno2Domenec Puig3Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Avinguda Paisos Catalans 26, 43007 Tarragona, SpainDepartment of Radiology, Radboud University Medical Center, 6525 GA Nijmegen, NetherlandsDepartament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Avinguda Paisos Catalans 26, 43007 Tarragona, SpainDepartament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Avinguda Paisos Catalans 26, 43007 Tarragona, SpainTexture analysis methods are widely used to characterize breast masses in mammograms. Texture gives information about the spatial arrangement of the intensities in the region of interest. This information has been used in mammogram analysis applications such as mass detection, mass classification, and breast density estimation. In this paper, we study the effect of factors such as pixel resolution, integration scale, preprocessing, and feature normalization on the performance of those texture methods for mass classification. The classification performance was assessed considering linear and nonlinear support vector machine classifiers. To find the best combination among the studied factors, we used three approaches: greedy, sequential forward selection (SFS), and exhaustive search. On the basis of our study, we conclude that the factors studied affect the performance of texture methods, so the best combination of these factors should be determined to achieve the best performance with each texture method. SFS can be an appropriate way to approach the factor combination problem because it is less computationally intensive than the other methods.http://dx.doi.org/10.1155/2016/1370259 |
| spellingShingle | Mohamed Abdel-Nasser Jaime Melendez Antonio Moreno Domenec Puig The Impact of Pixel Resolution, Integration Scale, Preprocessing, and Feature Normalization on Texture Analysis for Mass Classification in Mammograms International Journal of Optics |
| title | The Impact of Pixel Resolution, Integration Scale, Preprocessing, and Feature Normalization on Texture Analysis for Mass Classification in Mammograms |
| title_full | The Impact of Pixel Resolution, Integration Scale, Preprocessing, and Feature Normalization on Texture Analysis for Mass Classification in Mammograms |
| title_fullStr | The Impact of Pixel Resolution, Integration Scale, Preprocessing, and Feature Normalization on Texture Analysis for Mass Classification in Mammograms |
| title_full_unstemmed | The Impact of Pixel Resolution, Integration Scale, Preprocessing, and Feature Normalization on Texture Analysis for Mass Classification in Mammograms |
| title_short | The Impact of Pixel Resolution, Integration Scale, Preprocessing, and Feature Normalization on Texture Analysis for Mass Classification in Mammograms |
| title_sort | impact of pixel resolution integration scale preprocessing and feature normalization on texture analysis for mass classification in mammograms |
| url | http://dx.doi.org/10.1155/2016/1370259 |
| work_keys_str_mv | AT mohamedabdelnasser theimpactofpixelresolutionintegrationscalepreprocessingandfeaturenormalizationontextureanalysisformassclassificationinmammograms AT jaimemelendez theimpactofpixelresolutionintegrationscalepreprocessingandfeaturenormalizationontextureanalysisformassclassificationinmammograms AT antoniomoreno theimpactofpixelresolutionintegrationscalepreprocessingandfeaturenormalizationontextureanalysisformassclassificationinmammograms AT domenecpuig theimpactofpixelresolutionintegrationscalepreprocessingandfeaturenormalizationontextureanalysisformassclassificationinmammograms AT mohamedabdelnasser impactofpixelresolutionintegrationscalepreprocessingandfeaturenormalizationontextureanalysisformassclassificationinmammograms AT jaimemelendez impactofpixelresolutionintegrationscalepreprocessingandfeaturenormalizationontextureanalysisformassclassificationinmammograms AT antoniomoreno impactofpixelresolutionintegrationscalepreprocessingandfeaturenormalizationontextureanalysisformassclassificationinmammograms AT domenecpuig impactofpixelresolutionintegrationscalepreprocessingandfeaturenormalizationontextureanalysisformassclassificationinmammograms |