Statistical temporal changes for breast cancer detection: a preliminary study

Gobert Lee; Mariusz Bajger

doi:10.1007/978-3-319-07887-8_88

Statistical temporal changes for breast cancer detection: a preliminary study

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

In this paper, we propose a statistical temporal change scheme for early breast cancer detection. Temporal mammographic data have been found useful to detect changes in the breasts of women. Many temporal analysis approaches require temporal image registration. Variations in patient positioning, changes in the field of view and natural changes in the breasts over time pose significant challenges. Our proposed scheme, on the other hand, does not depend on image registration. Instead, the temporal statistical region merging technqiue was used to find homogeneous breast regions over time. Changes identified are then assessed for abnormality by a rule-based classifier. Using a small temporal dataset of 10 women (5 cancerous and 5 normal), the detection rate was found to be 100% with a 0.1 false positive per case (that is, only one false positive was found in the entire dataset of 10 cases). These preliminary results show that the proposed temporal changes detection scheme has a great potential in providing clinical assistance in early breast cancer detection. The results, however, need to be further verified with a larger dataset.

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, Takeshi Hara, Chisako Muramatsu
Place of Publication	Switzerland
Publisher	Springer
Pages	635-642
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_88
Publication status	Published - 2014
Event	12th International Workshop on Breast Imaging, IWDM 2014 - Duration: 29 Jun 2014 → 2 Jul 2014 Conference number: 12th

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Number	8539
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Workshop on Breast Imaging, IWDM 2014
Period	29/06/14 → 2/07/14

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

computer-aided detection
mammography
statistical region merging
temporal analysis
temporal change detection

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https://rdcu.be/dA8IvLicence: In Copyright

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Lee, G., & Bajger, M. (2014). Statistical temporal changes for breast cancer detection: a preliminary study. 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. 635-642). (Lecture Notes in Computer Science; No. 8539). Springer . https://doi.org/10.1007/978-3-319-07887-8_88

Lee, Gobert ; Bajger, Mariusz. / Statistical temporal changes for breast cancer detection : a preliminary study. 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 ; Takeshi Hara ; Chisako Muramatsu. Switzerland : Springer , 2014. pp. 635-642 (Lecture Notes in Computer Science; 8539).

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title = "Statistical temporal changes for breast cancer detection: a preliminary study",

abstract = "In this paper, we propose a statistical temporal change scheme for early breast cancer detection. Temporal mammographic data have been found useful to detect changes in the breasts of women. Many temporal analysis approaches require temporal image registration. Variations in patient positioning, changes in the field of view and natural changes in the breasts over time pose significant challenges. Our proposed scheme, on the other hand, does not depend on image registration. Instead, the temporal statistical region merging technqiue was used to find homogeneous breast regions over time. Changes identified are then assessed for abnormality by a rule-based classifier. Using a small temporal dataset of 10 women (5 cancerous and 5 normal), the detection rate was found to be 100\% with a 0.1 false positive per case (that is, only one false positive was found in the entire dataset of 10 cases). These preliminary results show that the proposed temporal changes detection scheme has a great potential in providing clinical assistance in early breast cancer detection. The results, however, need to be further verified with a larger dataset.",

keywords = "computer-aided detection, mammography, statistical region merging, temporal analysis, temporal change detection",

author = "Gobert Lee and Mariusz Bajger",

year = "2014",

doi = "10.1007/978-3-319-07887-8\_88",

language = "English",

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

series = "Lecture Notes in Computer Science",

publisher = "Springer ",

number = "8539",

pages = "635--642",

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

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

note = "12th International Workshop on Breast Imaging, IWDM 2014 ; Conference date: 29-06-2014 Through 02-07-2014",

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Lee, G & Bajger, M 2014, Statistical temporal changes for breast cancer detection: a preliminary study. 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, no. 8539, Springer , Switzerland, pp. 635-642, 12th International Workshop on Breast Imaging, IWDM 2014, 29/06/14. https://doi.org/10.1007/978-3-319-07887-8_88

Statistical temporal changes for breast cancer detection: a preliminary study. / Lee, Gobert ; Bajger, Mariusz.
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; Takeshi Hara; Chisako Muramatsu. Switzerland: Springer , 2014. p. 635-642 (Lecture Notes in Computer Science; No. 8539).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Statistical temporal changes for breast cancer detection

T2 - 12th International Workshop on Breast Imaging, IWDM 2014

AU - Lee, Gobert

AU - Bajger, Mariusz

N1 - Conference code: 12th

PY - 2014

Y1 - 2014

N2 - In this paper, we propose a statistical temporal change scheme for early breast cancer detection. Temporal mammographic data have been found useful to detect changes in the breasts of women. Many temporal analysis approaches require temporal image registration. Variations in patient positioning, changes in the field of view and natural changes in the breasts over time pose significant challenges. Our proposed scheme, on the other hand, does not depend on image registration. Instead, the temporal statistical region merging technqiue was used to find homogeneous breast regions over time. Changes identified are then assessed for abnormality by a rule-based classifier. Using a small temporal dataset of 10 women (5 cancerous and 5 normal), the detection rate was found to be 100% with a 0.1 false positive per case (that is, only one false positive was found in the entire dataset of 10 cases). These preliminary results show that the proposed temporal changes detection scheme has a great potential in providing clinical assistance in early breast cancer detection. The results, however, need to be further verified with a larger dataset.

AB - In this paper, we propose a statistical temporal change scheme for early breast cancer detection. Temporal mammographic data have been found useful to detect changes in the breasts of women. Many temporal analysis approaches require temporal image registration. Variations in patient positioning, changes in the field of view and natural changes in the breasts over time pose significant challenges. Our proposed scheme, on the other hand, does not depend on image registration. Instead, the temporal statistical region merging technqiue was used to find homogeneous breast regions over time. Changes identified are then assessed for abnormality by a rule-based classifier. Using a small temporal dataset of 10 women (5 cancerous and 5 normal), the detection rate was found to be 100% with a 0.1 false positive per case (that is, only one false positive was found in the entire dataset of 10 cases). These preliminary results show that the proposed temporal changes detection scheme has a great potential in providing clinical assistance in early breast cancer detection. The results, however, need to be further verified with a larger dataset.

KW - computer-aided detection

KW - mammography

KW - statistical region merging

KW - temporal analysis

KW - temporal change detection

UR - https://www.scopus.com/pages/publications/84903993403

U2 - 10.1007/978-3-319-07887-8_88

DO - 10.1007/978-3-319-07887-8_88

M3 - Conference contribution

SN - 978-3-319-07886-1

T3 - Lecture Notes in Computer Science

SP - 635

EP - 642

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

A2 - Fujita, Hiroshi

A2 - Hara, Takeshi

A2 - Muramatsu, Chisako

PB - Springer

CY - Switzerland

Y2 - 29 June 2014 through 2 July 2014

ER -

Lee G , Bajger M. Statistical temporal changes for breast cancer detection: a preliminary study. 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. Switzerland: Springer . 2014. p. 635-642. (Lecture Notes in Computer Science; 8539). doi: 10.1007/978-3-319-07887-8_88

Statistical temporal changes for breast cancer detection: a preliminary study

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UN SDGs

Keywords

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