Detecting Abnormal Mammographic Cases in Temporal Studies Using Image Registration Features

Robert Marti; Yago Díez; Arnau Oliver; Meritxell Tortajada; Reyer Zwiggelaar; Xavier Lladó

doi:10.1007/978-3-319-07887-8_85

Detecting Abnormal Mammographic Cases in Temporal Studies Using Image Registration Features

Robert Marti, Yago Díez, Arnau Oliver, Meritxell Tortajada, Reyer Zwiggelaar, Xavier Lladó

Department of Computer Science

University of Girona

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

5 Citations (Scopus)

Abstract

Image registration is increasingly being used to help radiologists when comparing temporal mammograms for lesion detection and classification. This paper evaluates the use of image and deformation features extracted from image registration results in order to detect abnormal cases with masses. Using a dataset of 264 mammographic images from 66 patients (33 normals and 33 with masses) results show that the use of a non-rigid registration method clearly improves detection results compared to no registration (AUC: 0.76 compared to 0.69). Moreover, feature combination using left and right breasts further improves the performance (AUC to 0.88) compared to single image features.

Original language	English
Title of host publication	Breast Imaging - 12th International Workshop, IWDM 2014, Proceedings
Subtitle of host publication	12th International Workshop, IWDM 2014, Gifu City, Japan, June 29 - July 2, 2014, Proceedings
Editors	Hiroshi Fujita, Takshi Hara, Chisako Muramatsu
Publisher	Springer Nature
Pages	612-619
Number of pages	8
ISBN (Electronic)	978-3-319-07887-8
ISBN (Print)	978-3-319-07886-1
DOIs	https://doi.org/10.1007/978-3-319-07887-8_85
Publication status	Published - 06 Jun 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8539 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Access to Document

10.1007/978-3-319-07887-8_85

Permanent link

Persistent link

Cite this

Marti, R., Díez, Y., Oliver, A., Tortajada, M., Zwiggelaar, R., & Lladó, X. (2014). Detecting Abnormal Mammographic Cases in Temporal Studies Using Image Registration Features. In H. Fujita, T. Hara, & C. Muramatsu (Eds.), Breast Imaging - 12th International Workshop, IWDM 2014, Proceedings: 12th International Workshop, IWDM 2014, Gifu City, Japan, June 29 - July 2, 2014, Proceedings (pp. 612-619). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8539 LNCS). Springer Nature. https://doi.org/10.1007/978-3-319-07887-8_85

Marti, Robert ; Díez, Yago ; Oliver, Arnau et al. / Detecting Abnormal Mammographic Cases in Temporal Studies Using Image Registration Features. Breast Imaging - 12th International Workshop, IWDM 2014, Proceedings: 12th International Workshop, IWDM 2014, Gifu City, Japan, June 29 - July 2, 2014, Proceedings. editor / Hiroshi Fujita ; Takshi Hara ; Chisako Muramatsu. Springer Nature, 2014. pp. 612-619 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{50b0bc62978c4d318b3e82d0ada262f7,

title = "Detecting Abnormal Mammographic Cases in Temporal Studies Using Image Registration Features",

abstract = "Image registration is increasingly being used to help radiologists when comparing temporal mammograms for lesion detection and classification. This paper evaluates the use of image and deformation features extracted from image registration results in order to detect abnormal cases with masses. Using a dataset of 264 mammographic images from 66 patients (33 normals and 33 with masses) results show that the use of a non-rigid registration method clearly improves detection results compared to no registration (AUC: 0.76 compared to 0.69). Moreover, feature combination using left and right breasts further improves the performance (AUC to 0.88) compared to single image features.",

author = "Robert Marti and Yago D{\'i}ez and Arnau Oliver and Meritxell Tortajada and Reyer Zwiggelaar and Xavier Llad{\'o}",

year = "2014",

month = jun,

day = "6",

doi = "10.1007/978-3-319-07887-8_85",

language = "English",

isbn = "978-3-319-07886-1",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Nature",

pages = "612--619",

editor = "Hiroshi Fujita and Takshi Hara and Chisako Muramatsu",

booktitle = "Breast Imaging - 12th International Workshop, IWDM 2014, Proceedings",

address = "Switzerland",

}

Marti, R, Díez, Y, Oliver, A, Tortajada, M, Zwiggelaar, R & Lladó, X 2014, Detecting Abnormal Mammographic Cases in Temporal Studies Using Image Registration Features. in H Fujita, T Hara & C Muramatsu (eds), Breast Imaging - 12th International Workshop, IWDM 2014, Proceedings: 12th International Workshop, IWDM 2014, Gifu City, Japan, June 29 - July 2, 2014, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8539 LNCS, Springer Nature, pp. 612-619. https://doi.org/10.1007/978-3-319-07887-8_85

Detecting Abnormal Mammographic Cases in Temporal Studies Using Image Registration Features. / Marti, Robert; Díez, Yago; Oliver, Arnau et al.
Breast Imaging - 12th International Workshop, IWDM 2014, Proceedings: 12th International Workshop, IWDM 2014, Gifu City, Japan, June 29 - July 2, 2014, Proceedings. ed. / Hiroshi Fujita; Takshi Hara; Chisako Muramatsu. Springer Nature, 2014. p. 612-619 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8539 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

TY - GEN

T1 - Detecting Abnormal Mammographic Cases in Temporal Studies Using Image Registration Features

AU - Marti, Robert

AU - Díez, Yago

AU - Oliver, Arnau

AU - Tortajada, Meritxell

AU - Zwiggelaar, Reyer

AU - Lladó, Xavier

PY - 2014/6/6

Y1 - 2014/6/6

N2 - Image registration is increasingly being used to help radiologists when comparing temporal mammograms for lesion detection and classification. This paper evaluates the use of image and deformation features extracted from image registration results in order to detect abnormal cases with masses. Using a dataset of 264 mammographic images from 66 patients (33 normals and 33 with masses) results show that the use of a non-rigid registration method clearly improves detection results compared to no registration (AUC: 0.76 compared to 0.69). Moreover, feature combination using left and right breasts further improves the performance (AUC to 0.88) compared to single image features.

AB - Image registration is increasingly being used to help radiologists when comparing temporal mammograms for lesion detection and classification. This paper evaluates the use of image and deformation features extracted from image registration results in order to detect abnormal cases with masses. Using a dataset of 264 mammographic images from 66 patients (33 normals and 33 with masses) results show that the use of a non-rigid registration method clearly improves detection results compared to no registration (AUC: 0.76 compared to 0.69). Moreover, feature combination using left and right breasts further improves the performance (AUC to 0.88) compared to single image features.

UR - http://www.scopus.com/inward/record.url?scp=84903975722&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-07887-8_85

DO - 10.1007/978-3-319-07887-8_85

M3 - Conference Proceeding (Non-Journal item)

SN - 978-3-319-07886-1

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 612

EP - 619

BT - Breast Imaging - 12th International Workshop, IWDM 2014, Proceedings

A2 - Fujita, Hiroshi

A2 - Hara, Takshi

A2 - Muramatsu, Chisako

PB - Springer Nature

ER -

Marti R, Díez Y, Oliver A, Tortajada M, Zwiggelaar R, Lladó X. Detecting Abnormal Mammographic Cases in Temporal Studies Using Image Registration Features. In Fujita H, Hara T, Muramatsu C, editors, Breast Imaging - 12th International Workshop, IWDM 2014, Proceedings: 12th International Workshop, IWDM 2014, Gifu City, Japan, June 29 - July 2, 2014, Proceedings. Springer Nature. 2014. p. 612-619. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-07887-8_85

Detecting Abnormal Mammographic Cases in Temporal Studies Using Image Registration Features

Abstract

Publication series

Access to Document

Permanent link

Other files and links

Fingerprint

Cite this