Ranklet Logo
 

Bibliography

Ranklets were originally introduced in:

  • Ranklets: orientation selective non-parametric features applied to face detection, by F. Smeraldi, in: Proceedings of the 16th International Conference on Pattern Recognition, Quebec QC, vol. 3, pages 379-382, August 2002 (paper)

Below, you will find an (incomplete) list of published papers and other material on Ranklets. Please contact the site administrator if you are aware of publications or other materials that are not included in this list, or if you would like your recent work added.

Publications in conferences and journals

  • Ranklets: orientation selective non-parametric features applied to face detection, by F. Smeraldi, in: Proceedings of the 16th International Conference on Pattern Recognition, Quebec QC, vol. 3, pages 379-382, August 2002 (link)

  • A nonparametric approach to face detection using ranklets, by F. Smeraldi, in: Proceedings of the 4th International Conference on Audio and Video-based Biometric Person Authentication (AVBPA 03), Guildford, UK, pages 351-359, June 2003 (link)

  • Ranklets on Hexagonal Pixel Lattices, by F. Smeraldi and M. A. Rob, in: Proceedings of the British Machine Vision Conference, Norwich, UK, vol. 1, pages 163-170, September 2003 (link)

  • Non-rigid structure from motion using non-parametric tracking and non-linear optimization, by A. Del Bue, F. Smeraldi and L. Agapito, in: Proceedings of the IEEE Workshop on Articulated and Nonrigid Motion, Washington DC, June 2004 (link)

  • Finding objects with hypothesis testing, by E. Franceschi, F. Odone, F. Smeraldi and A. Verri, in: Proceedings of the Workshop on Learning for Adaptable Visual Systems (LAVS), in conjunction with ICPR'04, Cambridge, UK, August 2004 (link)

  • Statistical learning approaches with application to face detection, by E. Franceschi, F. Odone and A. Verri,in Advanced Studies in Biometrics, LNCS vol. 3161, Springer, pp 91-104, 2005 (link)

  • A classical tool revisited: object detection by statistical testing, by E. Franceschi, F. Odone, F. Smeraldi and A. Verri, in: Proceedings of the International Conference on Instrumentation, Control and Information Technology (SICE), pages 2961 - 2964, Okayama, Japan, August 2005 (link)

  • Feature selection with nonparametric statistics, by E. Franceschi, F. Odone, F. Smeraldi and A. Verri, in: Proceedings of the International Conference on Image Processing, volume I, pages 325-328, Genoa, Italy, September 2005 (link)

  • Tracking points on deformable objects with ranklets, by F. Smeraldi, A. Del Bue and L. Agapito, in: Proceedings of the International Conference on Image Processing, volume III, pages 121-124, Genoa, Italy, September 2005 (link)

  • A ranklet-based CAD for digital mammography, by E. Angelini, R. Campanini, E. Iampieri, N. Lanconelli, M. Masotti, T. Petkov and M. Roffilli, in: Proceedings of the 8th International Workshop on Digital Mammography, Manchester, UK, June 18-21, 2006, Springer, pp. 340-346 (link)

  • Exploring ranklets performances in mammographic mass classification using recursive feature elimination, by M. Masotti, in: Proceedings of the 16th IEEE International Workshop on Machine Learning for Signal Processing, Maynooth, Ireland, September 6-8, 2006, pp. 265-270 (link)

  • HMT of the ranklet transform for face recognition and verification, by M. A. Ismail and R. A. El-Khoribi, GVIP Journal 6(3), pp 7-13, Dec 2006 (link)

  • A ranklet-based image representation for mass classification in digital mammograms, by M. Masotti, Medical Physics, 33(10) (2006) 3951-3961 (link)

  • Detection of mass type-Breast Cancer using Homogeneity and Ranklets on Dense Mammographic Images by Park, J.Y., Chon, M.S., Kim, W.H. and Kim, S.M., Park, J.Y., Chon, M.S., Kim, W.H. and Kim, S.M., in Proceedings of the KIEE Conference, pp 148-150, 2006 (link)

  • Discrete Hidden Markov Tree modelling of ranklet transform for mass classification in mammograms, by A. S. A. Mohammed, R. A. El-Khoribi, L. Fekry, GVIP Special Issue on Mammograms, pp 61-68, 2007 (link)

  • Homogeneity and ranklet based mass-type cancer detection in dense mammographic images, by W. Kim and S. Kim, Proceedings of the International Conference on Advanced Nondestructive Evaluation, Vol 1, pp 157-162, World Scientific, 2007 (link)

  • Non-rigid structure from motion using ranklet-based tracking and non-linear optimization, by A. Del Bue, F. Smeraldi and L. Agapito, in: Image and Vision Computing, volume 25, issue 3, pages 297-310, March 2007 (link)

  • Texture classification using invariant Ranklet features, by M. Masotti, R. Campanini, Pattern Recognition Letters 29, pp 1980-1986, 2008 (link)

  • Support Vector Machine training of HMT models for Multispectral Image Classification, by R. A. El-Khoribi, International Journal of Computer Science and Network Security, Vol. 8, No. 9, Sept 2008 (link)

  • Support Vector Machine training of HMT models for Land Cover Image Classification, by R. A. El-Khoribi, ICGST-GVIP 8(IV), pp 7-11, Dec 2008 (link)

  • Large margin GMM of ranklets for multispectral image classification, by R. A. El-Khoribi, IGST International Journal on Graphics, Vision and Image Processing (GVIP), vol 8, issue 4, pp 13-18, 2008 (link)

  • Mammography mass detection: a multi-stage hybrid approach, by N. Sahba, V. Tavakoli, A. Ahmadian, M. Giti, Proceedings of SPIE, Vol. 7259, 725947, 2009 (link)

  • Reducing false positive marks in breast mass computer-aided detection via bilateral ranklet texture analysis, by M. Masotti, A. Rodi, R. Campanini, International Journal on Computer Assisted Radiology and Surgery, Volume 4, Supplement 1, pp S356-S357, June 2009 (link)

  • Computer‐aided mass detection in mammography: False positive reduction via gray‐scale invariant ranklet texture features by Masotti M, Lanconelli N, Campanini R., in Medical physics, 36(2), 311-316, 2009 (link)

  • Robust color texture features based on ranklets and discrete Fourier transform, by F. Bianconi, A. Fernandez, E. Gonzalez and J. Armesto, Journal of Electornic Imaging 18(4), 043012 (Oct-Dec 2009) (link)

  • Variance Ranklets: orientation-selective rank features for contrast modulations, by G. Azzopardi, F. Smeraldi, in: Proc. of the British Machine Vision Conference, London (UK), Sept 2009 (link)

  • Fast algorithms for the computation of Ranklets, by F. Smeraldi, Proceedings of ICIP, Cairo (Egypt), pp 3969-3972, November 2009 (link)

  • Colour and texture features for image retrieval in granite industry, by M. J. Alvarez, E. Gonzalez, F. Bianconi, J. Armesto, A. Fernandez in: Dyna, vol. 77, no 161, pp121-130, March 2010 (link)

  • A comparative study of feature extraction methods for wood texture classification, by Prasetiyo, M. Khalid, R. Yusof and F. Meriaudeau, Proc. of the 6th International Conference on Signal-Image Technology and Internet Based Systems, pp 23-29, Dec 2010 (link)

  • Theoretical and experimental comparison of different approaches for colour texture classification, by F. Bianconi, R. Harvey, P. Southam and A. Fernandez, Journal of Electronic Imaging, Volume 20, Issue 4, pp. 043006-043006-17, Oct 2011 (link)

  • Ranklets: a qualitative review, by M. Saha, S. R. Choudhury, K. Roy, National conference on Electronics, Communication and Signal Processing (NCECS), Siliguri, Darjeeling, West Bengal, pp 68-73, Sept 2011 (link)

  • Features Extraction and Fuzzy Logic based Classification for False Positives Reduction in Mammographic Images, by Mencattini, A., Rabottino, G., Salmeri, M., Lojacono, R., and Tamilia, E., in MIAD, pp. 13-25, 2011 (link)

  • Image retrieval based on content using color feature, by A. J. Afifi and W. M. Ashour, ISRN Computer Graphics, Volume 2012 (2012), Article ID 248285 (link)

  • Automatic detection of mass type breast cancer using texture analysis in Korean digital mammography, by E. B. Jo, J. H. Lee, J. Y. Park and S. M. Kim, World academy of science, engineering and technology 64, 2012 (link)

  • Detection of breast cancer based on texture analysis from digital mammograms, by Eun-Byeol Jo, Ju-Hwan Lee, Jun-Young Park, Sung-Min Kim, Intelligent Autonomous Systems 12, Advances in Intelligent Systems and Computing vol. 194, pp 893-900, 2013 (link)

  • Robust texture analysis using multi-resolution gray-scale invariant features for breast sonographic tumor diagnosis by Yang, M. C., Moon, W. K., Wang, Y. C., Bae, M. S., Huang, C. S., Chen, J. H., & Chang, R. F. Medical Imaging, IEEE Transactions on, 32(12), 2262-2273, 2013 (link)

  • Automatic Characterization of the Visual Appearance of Industrial Materials through Colour and Texture Analysis: An Overview of Methods and Applications, by E. González, F. Bianconi, M. X. Álvarez and S. A. Saetta, Advances in Optical Technologies Volume 2013, Article ID 503541, 2013 (link)

  • Assessment of a novel mass detection algorithm in mammograms, by Kozegar, E., Soryani, M., Minaei, B. and Domingues, I., in Journal of cancer research and therapeutics, 9(4), pp.592-600, 2013(link)

  • Using multi resolution census and ranklet transformation in long base line SAR image matching, by Ghannadi, M. A., M. Saadatseresht, M. Motagh, and A. Eftekhari, ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 1, pp 181-184, 2013 (link)

  • Intensity-invariant texture features for breast ultrasound classification, by W. Gómez, W. C. A. Pereira and A. F. C. Infantosi, XXIV Brasilian Congress on Biomedical Engineering, pp 529-532, 2014 (link)

  • Content based image retrival using invariant color and texture features, by Sruthi, K.C. and Ahamed, S.P., in International Journal of Advanced Research in Computer and Communication Engineering Vol. 3, Issue 12, pp 8863-8867, December 2014 (link)

  • Computer-aided diagnosis of breast tumors using textures from intensity transformed sonographic images, by Lo, C. M., Chang, R. F., Huang, C. S., & Moon, W. K., 1st Global Conference on Biomedical Engineering & 9th Asian-Pacific Conference on Medical and Biological Engineering: October 9-12, 2014, Tainan, Taiwan (pp. 124-127). Springer International Publishing, 2015. (link)

  • An efficient method for detection of masses in mammogram images, by Haddadnia J, Rahmani-Seryasat O, Ghayoumi-Zadeh H, Rabiee H. in Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi, 36(3), pp.2269-2277, 2015. (link)

  • Computer-aided diagnosis for distinguishing between triple-negative breast cancer and fibroadenomas based on ultrasound texture features, by Moon, W.K., Huang, Y.S., Lo, C.M., Huang, C.S., Bae, M.S., Kim, W.H., Chen, J.H. and Chang, R.F., in Medical Physics 42, no. 6 Part 1, pp 3024-3035, 2015 (link)

  • Intensity-Invariant Texture Analysis for Classification of BI-RADS Category 3 Breast Masses, by Lo, C.M., Moon, W.K., Huang, C.S., Chen, J.H., Yang, M.C. and Chang, R.F., Ultrasound in medicine & biology 41, no. 7, pp 2039-2048, 2015 (link)

  • Digital watermarking using combination of Ranklets and wavelets, by Jan, Zahoor, Nazia Azeem, and Faryal Zahoor. In First International Conference on Anti-Cybercrime (ICACC), pp. 1-5. IEEE, 2015 (link)

  • Content-Based Image Retrieval Hybrid Approach using Artificial Bee Colony and K-means Algorithms, by Alharan, A.F., Al-Haboobi, A.S., Kurmasha, H.T. and Albayati, A.J., in International Journal of Sciences: Basic and Applied Research (IJSBAR), 27, pp.235-258, 2016 (link)

  • Robust texture analysis of multi-modal images using local structure preserving ranklet and multi-task learning for breast tumor diagnosis, by Xi, X., Xu, H., Shi, H., Zhang, C., Ding, H.Y., Zhang, G., Tang, Y. and Yin, Y., Neurocomputing, 259, pp.210-218, 2017 (link)

  • The adaptive computer-aided diagnosis system based on tumor sizes for the classification of breast tumors detected at screening ultrasound, by Moon, W.K., Chen, I.L., Chang, J.M., Shin, S.U., Lo, C.M. and Chang, R.F., in Ultrasonics, Volume 76, pp 70-77, 2017 (link)

  • Accurate needle localization in two-dimensional ultrasound images, by Daoud, M.I., Khraiwesh, S., Zayadeen, A. and Alazrai, R., in Proc. of the 10th International Conference on Electrical and Electronics Engineering (ELECO), pp. 578-582, IEEE, 2017 (link)

  • Preliminary Study of Chronic Liver Classification on Ultrasound Images Using an Ensemble Model, by Bharti P., Mittal D., and Ananthasivan R., in Ultrasonic Imaging 2018, Vol. 40(6) 357–379, 2018 (link)

  • Novel robust digital watermarking in mid-rank co-efficient based on DWT and RT transform, by Jan Z, Ullah I, Tahir F, Islam N, Shah B., in New Knowledge in Information Systems and Technologies: Volume 2 (pp. 295-302), Springer, 2019 (link)

  • Texture Analysis Based on Auto-Mutual Information for Classifying Breast Lesions with Ultrasound, by Gómez-Flores W, Rodríguez-Cristerna A, de Albuquerque Pereira WC, in Ultrasound in medicine & biology 45(8):2213-25, 2019 (link)

  • Boosting content based image retrieval performance through integration of parametric & nonparametric approaches, by Rana, S.P., Dey, M. and Siarry, P., in Journal of Visual Communication and Image Representation, 58, pp 205-219, 2019 (link)

  • Evaluation of TP53/PIK3CA mutations using texture and morphology analysis on breast MRI, by Moon WK, Chen HH, Shin SU, Han W, Chang RF, in Magnetic resonance imaging, 63, pp. 60-69, 2019 (link)

  • Detection of Huanglongbing disease based on intensity-invariant texture analysis of images in the visible spectrum, by Gómez-Flores W, Garza-Saldaña JJ, Varela-Fuentes SE, in Computers and Electronics in Agriculture 162, pages 825-835, 2019 (link)

  • Evaluation of TP53/PIK3CA mutations using texture and morphology analysis on breast MRI, by Moon, W.K., Chen, H.H., Shin, S.U., Han, W. and Chang, R.F., in Magnetic resonance imaging, 63, pp.60-69, 2019 (link)

  • Computer-aided detection of hyperacute stroke based on relative radiomic patterns in computed tomography, by Lo, C.M., Hung, P.H. and Hsieh, K.L.C., in Applied Sciences, 9(8), p.1668, 2019 (link)

  • Hybrid feature selection-based feature fusion for liver disease classification on ultrasound images, by Bharti P, Mittal D., in Advances in Computational Techniques for Biomedical Image Analysis, pp. 145-164, Academic Press, 2020 (link)

  • Two-level combined classification technique using Ranklet transformation for the detection of MRI brain tumor, by Singh, M., & Shrimali, V., In IEEE 17th India council international conference (INDICON), pp. 1-5, 2020. (link)

  • Computer-aided diagnosis of isocitrate dehydrogenase genotypes in glioblastomas from radiomic patterns by Lo, C.M., Weng, R.C., Cheng, S.J., Wang, H.J. and Hsieh, K.L.C., Medicine 99, no. 8, 2020 (link)

  • Glaucoma Image Classification Using Entropy Feature and Maximum Likelihood Classifier, by Rebinth, A., Kumar, S.M., Kumanan, T. and Varaprasad, G., in Journal of Physics: Conference Series, vol. 1964, no. 4, p. 042075. IOP Publishing, 2021. (link)

  • A computer-aided diagnosis system for differentiation and delineation of malignant regions on whole-slide prostate histopathology image using spatial statistics and multidimensional DenseNet, by Chen, C.M., Huang, Y.S., Fang, P.W., Liang, C.W. and Chang, R.F., in Medical physics, 47(3), pp.1021-1033, 2020 (link)

  • Foliar fungal disease classification in banana plants using elliptical local binary pattern on multiresolution dual tree complex wavelet transform domain, by Mathew, D., Kumar, C.S. and Cherian, K.A., in Information Processing in Agriculture Volume 8, Issue 4, pp 581-592, 2021 (link)

  • Evaluation of statistical and Haralick texture features for lymphoma histological images classification, by Azevedo Tosta, T.A., de Faria, P.R., Neves, L.A. and do Nascimento, M.Z., in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 9, no. 6, pp 613-624, 2021 (link)

  • Detection of mass type-Breast Cancer using Homogeneity and Ranklets on Dense Mammographic Images by Park, J.Y., Chon, M.S., Kim, W.H. and Kim, S.M., Park, J.Y., Chon, M.S., Kim, W.H. and Kim, S.M., in Proceedings of the KIEE Conference, pp 148-150, 2006

  • Assessment of a novel mass detection algorithm in mammograms, by Kozegar, E., Soryani, M., Minaei, B. and Domingues, I., in Journal of cancer research and therapeutics, 9(4), pp.592-600, 2013 (link)

  • Using multi resolution census and ranklet transformation in long base line SAR image matching, by Ghannadi, M. A., M. Saadatseresht, M. Motagh, and A. Eftekhari, ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 1, pp 181-184, 2013 (link)

  • Content based image retrival using invariant color and texture features, by Sruthi, K.C. and Ahamed, S.P., in International Journal of Advanced Research in Computer and Communication Engineering Vol. 3, Issue 12, pp 8863-8867, December 2014 (link)

  • Computer-aided diagnosis of breast tumors using textures from intensity transformed sonographic images, by Lo, C. M., Chang, R. F., Huang, C. S., & Moon, W. K., 1st Global Conference on Biomedical Engineering & 9th Asian-Pacific Conference on Medical and Biological Engineering: October 9-12, 2014, Tainan, Taiwan (pp. 124-127). Springer International Publishing, 2015. (link)

  • An efficient method for detection of masses in mammogram images, by Haddadnia J, Rahmani-Seryasat O, Ghayoumi-Zadeh H, Rabiee H. in Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi, 36(3), pp.2269-2277, 2015. (link)

  • Computer-aided diagnosis for distinguishing between triple-negative breast cancer and fibroadenomas based on ultrasound texture features, by Moon, W.K., Huang, Y.S., Lo, C.M., Huang, C.S., Bae, M.S., Kim, W.H., Chen, J.H. and Chang, R.F., in Medical Physics 42, no. 6 Part 1, pp 3024-3035, 2015 (link)

  • Intensity-Invariant Texture Analysis for Classification of BI-RADS Category 3 Breast Masses, by Lo, C.M., Moon, W.K., Huang, C.S., Chen, J.H., Yang, M.C. and Chang, R.F., Ultrasound in medicine & biology 41, no. 7, pp 2039-2048, 2015 (link)

  • Detection of mass type-Breast Cancer using Homogeneity and Ranklets on Dense Mammographic Images by Park, J.Y., Chon, M.S., Kim, W.H. and Kim, S.M., Park, J.Y., Chon, M.S., Kim, W.H. and Kim, S.M., in Proceedings of the KIEE Conference, pp 148-150, 2006

  • Assessment of a novel mass detection algorithm in mammograms, by Kozegar, E., Soryani, M., Minaei, B. and Domingues, I., in Journal of cancer research and therapeutics, 9(4), pp.592-600, 2013(link)

  • Using multi resolution census and ranklet transformation in long base line SAR image matching, by Ghannadi, M. A., M. Saadatseresht, M. Motagh, and A. Eftekhari, ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 1, pp 181-184, 2013 (link)

  • Content based image retrival using invariant color and texture features, by Sruthi, K.C. and Ahamed, S.P., in International Journal of Advanced Research in Computer and Communication Engineering Vol. 3, Issue 12, pp 8863-8867, December 2014 (link)

  • Computer-aided diagnosis of breast tumors using textures from intensity transformed sonographic images, by Lo, C. M., Chang, R. F., Huang, C. S., & Moon, W. K., 1st Global Conference on Biomedical Engineering & 9th Asian-Pacific Conference on Medical and Biological Engineering: October 9-12, 2014, Tainan, Taiwan (pp. 124-127). Springer International Publishing, 2015. (link)

  • An efficient method for detection of masses in mammogram images, by Haddadnia J, Rahmani-Seryasat O, Ghayoumi-Zadeh H, Rabiee H. in Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi, 36(3), pp.2269-2277, 2015. (link)

  • Computer-aided diagnosis for distinguishing between triple-negative breast cancer and fibroadenomas based on ultrasound texture features, by Moon, W.K., Huang, Y.S., Lo, C.M., Huang, C.S., Bae, M.S., Kim, W.H., Chen, J.H. and Chang, R.F., in Medical Physics 42, no. 6 Part 1, pp 3024-3035, 2015 (link)

  • Intensity-Invariant Texture Analysis for Classification of BI-RADS Category 3 Breast Masses, by Lo, C.M., Moon, W.K., Huang, C.S., Chen, J.H., Yang, M.C. and Chang, R.F., Ultrasound in medicine & biology 41, no. 7, pp 2039-2048, 2015 (link)

  • Digital watermarking using combination of Ranklets and wavelets, by Jan, Zahoor, Nazia Azeem, and Faryal Zahoor. In First International Conference on Anti-Cybercrime (ICACC), pp. 1-5. IEEE, 2015 (link)

  • Content-Based Image Retrieval Hybrid Approach using Artificial Bee Colony and K-means Algorithms, by Alharan, A.F., Al-Haboobi, A.S., Kurmasha, H.T. and Albayati, A.J., in International Journal of Sciences: Basic and Applied Research (IJSBAR), 27, pp.235-258, 2016 (link)

  • Robust texture analysis of multi-modal images using local structure preserving ranklet and multi-task learning for breast tumor diagnosis, by Xi, X., Xu, H., Shi, H., Zhang, C., Ding, H.Y., Zhang, G., Tang, Y. and Yin, Y., Neurocomputing, 259, pp.210-218, 2017 (link)

  • The adaptive computer-aided diagnosis system based on tumor sizes for the classification of breast tumors detected at screening ultrasound, by Moon, W.K., Chen, I.L., Chang, J.M., Shin, S.U., Lo, C.M. and Chang, R.F., in Ultrasonics, Volume 76, pp 70-77, 2017 (link)

  • Accurate needle localization in two-dimensional ultrasound images, by Daoud, M.I., Khraiwesh, S., Zayadeen, A. and Alazrai, R., in Proc. of the 10th International Conference on Electrical and Electronics Engineering (ELECO), pp. 578-582, IEEE, 2017 (link)

  • Preliminary Study of Chronic Liver Classification on Ultrasound Images Using an Ensemble Model, by Bharti P., Mittal D., and Ananthasivan R., in Ultrasonic Imaging 2018, Vol. 40(6) 357–379, 2018 (link)

  • Novel robust digital watermarking in mid-rank co-efficient based on DWT and RT transform, by Jan Z, Ullah I, Tahir F, Islam N, Shah B., in New Knowledge in Information Systems and Technologies: Volume 2 (pp. 295-302), Springer, 2019 (link)

  • Texture Analysis Based on Auto-Mutual Information for Classifying Breast Lesions with Ultrasound, by Gómez-Flores W, Rodríguez-Cristerna A, de Albuquerque Pereira WC, in Ultrasound in medicine & biology 45(8):2213-25, 2019 (link)

  • Boosting content based image retrieval performance through integration of parametric & nonparametric approaches, by Rana, S.P., Dey, M. and Siarry, P., in Journal of Visual Communication and Image Representation, 58, pp 205-219, 2019 (link)

  • Evaluation of TP53/PIK3CA mutations using texture and morphology analysis on breast MRI, by Moon WK, Chen HH, Shin SU, Han W, Chang RF, in Magnetic resonance imaging, 63, pp. 60-69, 2019 (link)

  • Detection of Huanglongbing disease based on intensity-invariant texture analysis of images in the visible spectrum, by Gómez-Flores W, Garza-Saldaña JJ, Varela-Fuentes SE, in Computers and Electronics in Agriculture 162, pages 825-835, 2019 (link)

  • Evaluation of TP53/PIK3CA mutations using texture and morphology analysis on breast MRI, by Moon, W.K., Chen, H.H., Shin, S.U., Han, W. and Chang, R.F., in Magnetic resonance imaging, 63, pp.60-69, 2019 (link)

  • Computer-aided detection of hyperacute stroke based on relative radiomic patterns in computed tomography, by Lo, C.M., Hung, P.H. and Hsieh, K.L.C., in Applied Sciences, 9(8), p.1668, 2019 (link)

  • Hybrid feature selection-based feature fusion for liver disease classification on ultrasound images, by Bharti P, Mittal D., in Advances in Computational Techniques for Biomedical Image Analysis, pp. 145-164, Academic Press, 2020 (link) (*)

  • Content-Based Image Retrieval Hybrid Approach using Artificial Bee Colony and K-means Algorithms, by Alharan, A.F., Al-Haboobi, A.S., Kurmasha, H.T. and Albayati, A.J., in International Journal of Sciences: Basic and Applied Research (IJSBAR), 27, pp.235-258, 2016 (link)

  • Robust texture analysis of multi-modal images using local structure preserving ranklet and multi-task learning for breast tumor diagnosis, by Xi, X., Xu, H., Shi, H., Zhang, C., Ding, H.Y., Zhang, G., Tang, Y. and Yin, Y., Neurocomputing, 259, pp.210-218, 2017 (link)

  • The adaptive computer-aided diagnosis system based on tumor sizes for the classification of breast tumors detected at screening ultrasound, by Moon, W.K., Chen, I.L., Chang, J.M., Shin, S.U., Lo, C.M. and Chang, R.F., in Ultrasonics, Volume 76, pp 70-77, 2017 (link)

  • Accurate needle localization in two-dimensional ultrasound images, by Daoud, M.I., Khraiwesh, S., Zayadeen, A. and Alazrai, R., in Proc. of the 10th International Conference on Electrical and Electronics Engineering (ELECO), pp. 578-582, IEEE, 2017 (link)

  • Preliminary Study of Chronic Liver Classification on Ultrasound Images Using an Ensemble Model, by Bharti P., Mittal D., and Ananthasivan R., in Ultrasonic Imaging 2018, Vol. 40(6) 357–379, 2018 (link)

  • Novel robust digital watermarking in mid-rank co-efficient based on DWT and RT transform, by Jan Z, Ullah I, Tahir F, Islam N, Shah B., in New Knowledge in Information Systems and Technologies: Volume 2 (pp. 295-302), Springer, 2019 (link)

  • Texture Analysis Based on Auto-Mutual Information for Classifying Breast Lesions with Ultrasound, by Gómez-Flores W, Rodríguez-Cristerna A, de Albuquerque Pereira WC, in Ultrasound in medicine & biology 45(8):2213-25, 2019 (link)

  • Boosting content based image retrieval performance through integration of parametric & nonparametric approaches, by Rana, S.P., Dey, M. and Siarry, P., in Journal of Visual Communication and Image Representation, 58, pp 205-219, 2019 (link)(*)

  • Evaluation of TP53/PIK3CA mutations using texture and morphology analysis on breast MRI, by Moon WK, Chen HH, Shin SU, Han W, Chang RF, in Magnetic resonance imaging, 63, pp. 60-69, 2019 (link)

  • Detection of Huanglongbing disease based on intensity-invariant texture analysis of images in the visible spectrum, by Gómez-Flores W, Garza-Saldaña JJ, Varela-Fuentes SE, in Computers and Electronics in Agriculture 162, pages 825-835, 2019 (link)

  • Evaluation of TP53/PIK3CA mutations using texture and morphology analysis on breast MRI, by Moon, W.K., Chen, H.H., Shin, S.U., Han, W. and Chang, R.F., in Magnetic resonance imaging, 63, pp.60-69, 2019 (link)

  • Computer-aided detection of hyperacute stroke based on relative radiomic patterns in computed tomography, by Lo, C.M., Hung, P.H. and Hsieh, K.L.C., in Applied Sciences, 9(8), p.1668, 2019 (link)

  • Hybrid feature selection-based feature fusion for liver disease classification on ultrasound images, by Bharti P, Mittal D., in Advances in Computational Techniques for Biomedical Image Analysis, pp. 145-164, Academic Press, 2020 (link)

  • Colour and Texture Descriptors for Visual Recognition: A Historical Overview, by Bianconi, F., Fernández, A., Smeraldi, F. and Pascoletti, G., in Journal of Imaging, 7(11):245, 2021 (link)

  • A New Scheme of Mammographic Masses Classification Based on the BI-RADS Lexicon, by Hernández-López, J., Gómez-Flores, W., 2021 Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE), Sevilla, Spain, pp.1-6, 2021 (link)

  • LRSCnet: Local Reference Semantic Code learning for breast tumor classification in ultrasound images, by Zhang G, Ren Y, Xi X, Li D, Guo J, Li X, Tian C, Xu Z., in BioMed Eng OnLine 20, 127, 2021 (link)

  • Detection and classification of brain tumor using hybrid feature extraction technique, by Singh, M., Shrimali, V., & Kumar, M., Multimedia tools and applications, pp 1-25, 2022 (link)

  • Fish classification using extraction of appropriate feature set, by Badawi, U. A., in International Journal of Electrical & Computer Engineering, Vol 12, Issue 3, pp 2488-2500, 2022. (link)

  • A Classifier Ensemble Method for Breast Tumor Classification Based on the BI-RADS Lexicon for Masses in Mammography, by Hernández-López, J., Gómez-Flores, W., in XXVII Brazilian Congress on Biomedical Engineering (CBEB 2020). IFMBE Proceedings, vol 83, pp. 1641-1647, Springer, 2022. (link)

Patents

  • Metodo, e relativa apparecchiatura, per la ricerca automatica di zone di interesse in immagini digitali, by M. Roffilli, E. Iampieri, E. Angelini, M. Masotti, Italian Patent BO2006A000200, March 20th, 2006 (link)

  • Method and corresponding apparatus for automatically searching for zones of interest in digital images, by M. Roffilli, E. Iampieri, E. Angelini, M. Masotti, European Patent EP1840833, Oct 3rd, 2007 (link)

Ph.D. Theses

  • Advanced hypothesis testing techniques and their applications to image classification, by E. Franceschi, Ph.D. Thesis, University of Genova (Italy), Computer Science, 2005(link)

  • Optimal image representations for mass detection in digital mammography, by M. Masotti, Ph.D. thesis, University of Bologna (Italy), Department of Physics, June 1, 2005 (thesis, viva presentation)

Online lectures / Poster presentations / Technical reports

  • Ranklets: a Complete Family of Multiscale, Orientation Selective Rank Features, by F. Smeraldi, Research Report RR0309-01, Department of Computer Science, Queen Mary, University of London, September 2003 (link)

  • Challenges in learning the appearance of faces for automated image analysis: part I, by A. Verri, Video lecture, Pattern recognition and machine learning in computer vision workshop, Grenoble (France), 2004 (link)

  • Advanced machine learning techniques for digital mammography, by M. Roffilli, Technical Report UBLCS-2006-12, University of Bologna (Italy), March 2006 (link)

  • Discriminating mass from normal breast tissue: a novel ranklet image representation for ROI encoding, by M. Masotti, AMS Acta 2194,University of Bologna (Italy), July 2006 (link)

  • False positive reduction in lung nodule computer-aided detection based on 3D ranklet transform, by M. Masotti and T. Petkov: Poster presentation, WavE 2006: Wavelets and Applications, Lausanne, Switzerland, July 10-14, 2006, Abstract No. A-48 (link)

  • Lung CAD system, by R. Campanini, N. Lanconelli, M. Masotti, A. Riccardi, and T. Petkov, AMS Acta 2320, University of Bologna (Italy), March 2007 (link)

  • Ranklet texture features for false positive reduction in computer-aided detection of breast masses, by M. Masotti, N. Lanconelli, R. Campanini, E. Angelini, M. Roffilli, E. Iampieri, International Journal on Computer Assisted Radiology and Surgery, Volume 3, Supplement 1, pp S175-S176, June 2008 (link)

  • A parallel texture analysis algorithm based on auto-mutual information for analyzing medical images, by Pal, L., Master's thesis, Center For Research and Advanced Studies, National Polytechnic Institute, Mexico City, Mexico, 2020 (link) - featuring a GPU implementation of ranklets

  • Wikipedia entry on Ranklets (link)

Updated 15 Dec 2023
Maintained by Fabrizio Smeraldi This website was written in Zim !