Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/107612
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Type: | Conference paper |
Title: | Deep structured learning for mass segmentation from mammograms |
Author: | Dhungel, N. Carneiro, G. Bradley, A. |
Citation: | Proceedings / ICIP ... International Conference on Image Processing, 2015, vol.2015-December, pp.2950-2954 |
Publisher: | IEEE |
Issue Date: | 2015 |
Series/Report no.: | IEEE International Conference on Image Processing ICIP |
ISBN: | 9781479983391 |
ISSN: | 1522-4880 |
Conference Name: | 2015 IEEE International Conference on Image Processing (ICIP 2015) (27 Sep 2015 - 30 Sep 2015 : Quebec City, CANADA) |
Statement of Responsibility: | Neeraj Dhungel, Gustavo Carneiro, Andrew P. Bradley |
Abstract: | In this paper, we present a novel method for the segmentation of breast masses from mammograms exploring structured and deep learning. Specifically, using structured support vector machine (SSVM), we formulate a model that combines different types of potential functions, including one that classifies image regions using deep learning. Our main goal with this work is to show the accuracy and efficiency improvements that these relatively new techniques can provide for the segmentation of breast masses from mammograms. We also propose an easily reproducible quantitative analysis to assess the performance of breast mass segmentation methodologies based on widely accepted accuracy and running time measurements on public datasets, which will facilitate further comparisons for this segmentation problem. In particular, we use two publicly available datasets (DDSM-BCRP and INbreast) and propose the computation of the running time taken for the methodology to produce a mass segmentation given an input image and the use of the Dice index to quantitatively measure the segmentation accuracy. For both databases, we show that our proposed methodology produces competitive results in terms of accuracy and running time. |
Keywords: | Mammograms, mass segmentation, structured learning, structured inference |
Rights: | © 2015 IEEE |
DOI: | 10.1109/ICIP.2015.7351343 |
Grant ID: | http://purl.org/au-research/grants/arc/DP140102794 http://purl.org/au-research/grants/arc/FT110100623 |
Published version: | http://dx.doi.org/10.1109/icip.2015.7351343 |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
RA_hdl_107612.pdf Restricted Access | Restricted Access | 938.37 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.