Automatic Gleason Grading of Prostate Cancer Using Shearlet Transform and Multiple Kernel Learning
The Gleason grading system is generally used for histological grading of prostate cancer. In this paper, we first introduce using the Shearlet transform and its coefficients as texture features for automatic Gleason grading. The Shearlet transform is a mathematical tool defined based on affine syste...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2016-09-01
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| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2313-433X/2/3/25 |
| _version_ | 1819154790423199744 |
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| author | Hadi Rezaeilouyeh Mohammad H. Mahoor |
| author_facet | Hadi Rezaeilouyeh Mohammad H. Mahoor |
| author_sort | Hadi Rezaeilouyeh |
| collection | DOAJ |
| description | The Gleason grading system is generally used for histological grading of prostate cancer. In this paper, we first introduce using the Shearlet transform and its coefficients as texture features for automatic Gleason grading. The Shearlet transform is a mathematical tool defined based on affine systems and can analyze signals at various orientations and scales and detect singularities, such as image edges. These properties make the Shearlet transform more suitable for Gleason grading compared to the other transform-based feature extraction methods, such as Fourier transform, wavelet transform, etc. We also extract color channel histograms and morphological features. These features are the essential building blocks of what pathologists consider when they perform Gleason grading. Then, we use the multiple kernel learning (MKL) algorithm for fusing all three different types of extracted features. We use support vector machines (SVM) equipped with MKL for the classification of prostate slides with different Gleason grades. Using the proposed method, we achieved high classification accuracy in a dataset containing 100 prostate cancer sample images of Gleason Grades 2–5. |
| first_indexed | 2024-12-22T15:26:41Z |
| format | Article |
| id | doaj.art-952b1f723e8d4641a0dc77fc30172335 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-12-22T15:26:41Z |
| publishDate | 2016-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-952b1f723e8d4641a0dc77fc301723352022-12-21T18:21:28ZengMDPI AGJournal of Imaging2313-433X2016-09-01232510.3390/jimaging2030025jimaging2030025Automatic Gleason Grading of Prostate Cancer Using Shearlet Transform and Multiple Kernel LearningHadi Rezaeilouyeh0Mohammad H. Mahoor1Department of Electrical and Computer Engineering, University of Denver, Denver, CO 80208, USADepartment of Electrical and Computer Engineering, University of Denver, Denver, CO 80208, USAThe Gleason grading system is generally used for histological grading of prostate cancer. In this paper, we first introduce using the Shearlet transform and its coefficients as texture features for automatic Gleason grading. The Shearlet transform is a mathematical tool defined based on affine systems and can analyze signals at various orientations and scales and detect singularities, such as image edges. These properties make the Shearlet transform more suitable for Gleason grading compared to the other transform-based feature extraction methods, such as Fourier transform, wavelet transform, etc. We also extract color channel histograms and morphological features. These features are the essential building blocks of what pathologists consider when they perform Gleason grading. Then, we use the multiple kernel learning (MKL) algorithm for fusing all three different types of extracted features. We use support vector machines (SVM) equipped with MKL for the classification of prostate slides with different Gleason grades. Using the proposed method, we achieved high classification accuracy in a dataset containing 100 prostate cancer sample images of Gleason Grades 2–5.http://www.mdpi.com/2313-433X/2/3/25Gleason gradingmultiple kernel learningprostate cancerShearlet transformtexture analysis |
| spellingShingle | Hadi Rezaeilouyeh Mohammad H. Mahoor Automatic Gleason Grading of Prostate Cancer Using Shearlet Transform and Multiple Kernel Learning Journal of Imaging Gleason grading multiple kernel learning prostate cancer Shearlet transform texture analysis |
| title | Automatic Gleason Grading of Prostate Cancer Using Shearlet Transform and Multiple Kernel Learning |
| title_full | Automatic Gleason Grading of Prostate Cancer Using Shearlet Transform and Multiple Kernel Learning |
| title_fullStr | Automatic Gleason Grading of Prostate Cancer Using Shearlet Transform and Multiple Kernel Learning |
| title_full_unstemmed | Automatic Gleason Grading of Prostate Cancer Using Shearlet Transform and Multiple Kernel Learning |
| title_short | Automatic Gleason Grading of Prostate Cancer Using Shearlet Transform and Multiple Kernel Learning |
| title_sort | automatic gleason grading of prostate cancer using shearlet transform and multiple kernel learning |
| topic | Gleason grading multiple kernel learning prostate cancer Shearlet transform texture analysis |
| url | http://www.mdpi.com/2313-433X/2/3/25 |
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