{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:21:05Z","timestamp":1760242865313,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2016,9,9]],"date-time":"2016-09-09T00:00:00Z","timestamp":1473379200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1230556"],"award-info":[{"award-number":["1230556"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>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\u20135.<\/jats:p>","DOI":"10.3390\/jimaging2030025","type":"journal-article","created":{"date-parts":[[2016,9,9]],"date-time":"2016-09-09T10:36:06Z","timestamp":1473417366000},"page":"25","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Automatic Gleason Grading of Prostate Cancer Using Shearlet Transform and Multiple Kernel Learning"],"prefix":"10.3390","volume":"2","author":[{"given":"Hadi","family":"Rezaeilouyeh","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Denver, Denver, CO 80208, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Mahoor","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Denver, Denver, CO 80208, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,9,9]]},"reference":[{"key":"ref_1","unstructured":"American Cancer Society (2016). Cancer Facts & Figures 2016, American Cancer Society."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/S0022-5347(17)59889-4","article-title":"Prediction of prognosis for prostatic adenocarcinoma by combined histological grading and clinical staging","volume":"111","author":"Gleason","year":"1974","journal-title":"J. Urol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/0046-8177(92)90108-F","article-title":"Histologic grading of prostate cancer: A perspective","volume":"23","author":"Gleason","year":"1992","journal-title":"Hum. Pathol."},{"key":"ref_4","unstructured":"Morphology & Grade, Available online: https:\/\/training.seer.cancer.gov\/prostate\/abstract-code-stage\/morphology.html."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1109\/TBME.2003.812194","article-title":"Multiwavelet grading of pathological images of prostate","volume":"50","year":"2003","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_6","unstructured":"Demir, C., and Yener, B. (2005). Automated Cancer Diagnosis Based on Histopathological Images: A Systematic Survey, Rensselaer Polytechnic Institute."},{"key":"ref_7","unstructured":"Farjam, R., Soltanian-Zadeh, H., Zoroofi, R.A., and Jafari-Khouzani, K. (2005). Medical Imaging, International Society for Optics and Photonics."},{"key":"ref_8","first-page":"295","article-title":"A machine learning approach to identify prostate cancer areas in complex histological images","volume":"Volume 3","author":"Salman","year":"2014","journal-title":"Information Technologies in Biomedicine"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.compmedimag.2015.08.002","article-title":"Machine learning approaches to analyze histological images of tissues from radical prostatectomies","volume":"46","author":"Gertych","year":"2015","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Khurd, P., Bahlmann, C., Maday, P., Kamen, A., Gibbs-Strauss, S., Genega, E.M., and Frangioni, J.V. (2010, January 14\u201317). Computer-aided Gleason grading of prostate cancer histopathological images using texton forests. Proceedings of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Rotterdam, The Netherlands.","DOI":"10.1109\/ISBI.2010.5490096"},{"key":"ref_11","unstructured":"Kuse, M., Sharma, T., and Gupta, S. (2010). Recognizing Patterns in Signals, Speech, Images and Videos, Springer."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1366","DOI":"10.1109\/TMI.2007.898536","article-title":"Multifeature prostate cancer diagnosis and Gleason grading of histological images","volume":"26","author":"Tabesh","year":"2007","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Rezaeilouyeh, H., Mahoor, M.H., Mavadati, S.M., and Zhang, J.J. (2013, January 9\u201311). A microscopic image classification method using shearlet transform. Proceedings of the IEEE International Conference on Healthcare Informatics (ICHI), Philadelphia, PA, USA.","DOI":"10.1109\/ICHI.2013.53"},{"key":"ref_14","unstructured":"Bio-Segmentation. Available online: http:\/\/bioimage.ucsb.edu\/research\/bio-segmentation."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Rezaeilouyeh, H., Mahoor, M.H., La Rosa, F.G., and Zhang, J.J. (2013, January 3\u20136). Prostate cancer detection and gleason grading of histological images using shearlet transform. Proceedings of the Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA.","DOI":"10.1109\/ACSSC.2013.6810274"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Rezaeilouyeh, H., Mahoor, M.H., Zhang, J.J., La Rosa, F.G., Chang, S., and Werahera, P.N. (2014, January 26\u201330). Diagnosis of prostatic carcinoma on multiparametric magnetic resonance imaging using shearlet transform. Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA.","DOI":"10.1109\/EMBC.2014.6945103"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Schwartz, W.R., Da Silva, R.D., Davis, L.S., and Pedrini, H. (2011, January 11\u201314). A novel feature descriptor based on the shearlet transform. 18th IEEE International Conference on Image Processing, Brussels, Belgium.","DOI":"10.1109\/ICIP.2011.6115600"},{"key":"ref_18","first-page":"2491","article-title":"SimpleMKL","volume":"9","author":"Rakotomamonjy","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1109\/TIP.2008.2008070","article-title":"Shearlet-based total variation diffusion for denoising","volume":"18","author":"Easley","year":"2009","journal-title":"IEEE Trans. Image Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.acha.2007.09.003","article-title":"Sparse directional image representations using the discrete shearlet transform","volume":"25","author":"Easley","year":"2008","journal-title":"Appl. Comput. Harmonic Anal."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1921","DOI":"10.1137\/090780912","article-title":"Adaptive multiresolution analysis structures and shearlet systems","volume":"49","author":"Han","year":"2011","journal-title":"SIAM J. Numer. Anal."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.acha.2008.10.004","article-title":"Edge analysis and identification using the continuous shearlet transform","volume":"27","author":"Guo","year":"2009","journal-title":"Appl. Comput. Harmonic Anal."},{"key":"ref_23","unstructured":"Kutyniok, G., and Petersen, P. (2015). Classification of edges using compactly supported shearlets. Appl. Comput. Harmonic Anal."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1109\/TIP.2002.1014998","article-title":"The curvelet transform for image denoising","volume":"11","author":"Starck","year":"2002","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","unstructured":"Guo, K., and Labate, D. (2012). Shearlets: Multiscale Analysis for Multivariate Data, Springer Science & Business Media."},{"key":"ref_26","unstructured":"Grohs, P., Keiper, S., Kutyniok, G., and Sch\u00e4fer, M. (2014). Approximation Theory XIV: San Antonio 2013, Springer International Publishing."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1137\/060649781","article-title":"Optimally sparse multidimensional representation using shearlets","volume":"39","author":"Guo","year":"2007","journal-title":"SIAM J. Math. Anal."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1564","DOI":"10.1016\/j.jat.2011.06.005","article-title":"Compactly supported shearlets are optimally sparse","volume":"163","author":"Kutyniok","year":"2011","journal-title":"J. Approx. Theory"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"6","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1109\/36.752194","article-title":"Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices","volume":"37","author":"Soh","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"45","DOI":"10.5589\/m02-004","article-title":"An analysis of co-occurrence texture statistics as a function of grey level quantization","volume":"28","author":"Clausi","year":"2002","journal-title":"Can. J. Remote Sens."},{"key":"ref_32","unstructured":"Graycomatrix. Available online: http:\/\/www.mathworks.com\/help\/images\/ref\/graycomatrix.html."},{"key":"ref_33","unstructured":"Gonzalez, R.C., and Woods, R.E. (2008). Digital Image Processing, Prentice Hall."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1109\/34.1000236","article-title":"Mean shift: A robust approach toward feature space analysis","volume":"24","author":"Comaniciu","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","first-page":"2211","article-title":"Multiple kernel learning algorithms","volume":"12","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_36","unstructured":"Gehler, P., and Nowozin, S. (October, January 27). On feature combination for multiclass object classification. Proceedings of the IEEE 12th International Conference on Computer Vision, Kyoto, Japan."},{"key":"ref_37","unstructured":"Software and Demo. Available online: https:\/\/www.math.uh.edu\/~dlabate\/software.html."},{"key":"ref_38","unstructured":"Haghighat, M., Zonouz, S., and Abdel-Mottaleb, M. (2013). International Conference on Computer Analysis of Images and Patterns, Springer."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Junior, O.L., Delgado, D., Gon\u00e7alves, V., and Nunes, U. (2009, January 4\u20137). Trainable classifier-fusion schemes: An application to pedestrian detection. Proceedings of the 12th International IEEE Conference on Intelligent Transportation Systems (ITSC), St. Louis, MO, USA.","DOI":"10.1109\/ITSC.2009.5309700"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1016\/j.bspc.2013.06.011","article-title":"Shearlet-based texture feature extraction for classification of breast tumor in ultrasound image","volume":"8","author":"Zhou","year":"2013","journal-title":"Biomed. Signal Process. Control"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/2\/3\/25\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:30:35Z","timestamp":1760211035000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/2\/3\/25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,9,9]]},"references-count":40,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2016,9]]}},"alternative-id":["jimaging2030025"],"URL":"https:\/\/doi.org\/10.3390\/jimaging2030025","relation":{},"ISSN":["2313-433X"],"issn-type":[{"type":"electronic","value":"2313-433X"}],"subject":[],"published":{"date-parts":[[2016,9,9]]}}}