Emmanuel Promayon

Home Page Publications List Sorted Internship/Stages Teaching Contact

Jump to : Download | Abstract | Keywords | Contact | BibTex reference | EndNote reference |


P. Samarakoon, E. Promayon, C. Fouard. Light Random Regression Forests for Automatic, Multi-Organ Localization in CT Images. In IEEE International Symposium on Biomedical Imaging ISBI'17, April 2017.


Download paper: Doi page (doi)

Download paper: (link)

Copyright notice: Disclaimer: this material is presented to ensure timely dissemination of scholarly and technical work. Files of articles may be covered by copyright. You may browse the articles at your convenience in the same spirit as you may read a journal or conference proceedings in a public library. Retrieving, copying, distributing these files, entirely or in parts, may violate copyright protection laws. Copyright and all rights therein are retained by authors or by other copyright holders. All person copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.


Classic Random Regression Forests (RRFs) used for multi-organ localization describe the random process of multivari-ate regression by storing the histograms of offset vectors along each bounding wall direction per leaf node. On the one hand, the RAM and storage requirements of classic RRFs may become exorbitantly high when such a RRF consists of many leaf nodes, but on the other hand, a large number of leaf nodes are required for better localization. We introduce Light Random Regression Forests (LRRFs) which eliminate the need to describe the random process by formulating the localization prediction based on the random variables that describe the random process. Consequently, LRRFs with the same localization capabilities require less RAM and storage space compared to classic RRFs. LRRF comprising 4 trees with 17 decision levels is approximately 9 times faster, takes 10 times less RAM, and uses 30 times less storage space compared to a similar classic RRF


[ Respiration ] [ Softtissue ]


Emmanuel Promayon http://membres-timc.imag.fr/Emmanuel.Promayon

BibTex Reference

   Author = {Samarakoon, P. and Promayon, E. and Fouard, C.},
   Title = {Light Random Regression Forests for Automatic, Multi-Organ Localization in {CT} Images},
   BookTitle = {IEEE International Symposium on Biomedical Imaging ISBI'17},
   Month = {April},
   Year = {2017}

EndNote Reference [help]

Get EndNote Reference (.ref)

Home Page Publications List Sorted Internship/Stages Teaching Contact
This page was automatically generated thanks to JabRef and bib2html , 18 September 2018