Breast Tissue Classification Using Local Binary Pattern Variants: A Comparative Study

Minu George; Reyer Zwiggelaar

doi:10.1007/978-3-319-95921-4_15

Breast Tissue Classification Using Local Binary Pattern Variants: A Comparative Study

Minu George
, Reyer Zwiggelaar

Adran Cyfrifiadureg

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Trafodion Cynhadledd (ISBN)

4 Dyfyniadau (Scopus)

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Mammographic tissue density is considered to be one of the major risk factors for developing breast cancer. In this paper we use quantitative measurements of Local Binary Patterns and its variants for breast tissue classification. We compare the classification results of LBP, ELBP, Uniform ELBP and M-ELBP for classifying mammograms as fatty, glandular and dense. A Bayesian-Network classifier is used with stratified ten-fold cross-validation. The experimental results indicate that ELBP patterns at different orientations extract more relevant elliptical breast tissue information from the mammograms indicating the importance of directional filters for breast tissue classification.

Iaith wreiddiol	Saesneg
Teitl	Proceedings 22nd Conference, Medical Image Understanding and Analysis 2018
Golygyddion	Mark Nixon, Sasan Mahmoodi, Reyer Zwiggelaar
Cyhoeddwr	Springer Nature
Tudalennau	143-152
Nifer y tudalennau	10
ISBN (Electronig)	978-3-319-95921-4
ISBN (Argraffiad)	978-3-319-95920-7
Dynodwyr Gwrthrych Digidol (DOIs)	https://doi.org/10.1007/978-3-319-95921-4_15
Statws	Cyhoeddwyd - 21 Awst 2018
Digwyddiad	Proceedings 22nd Conference Medical Image Understanding and Analysis - Southampton, Teyrnas Unedig Prydain Fawr a Gogledd Iwerddon Hyd: 09 Gorff 2018 → 11 Gorff 2018

Cyfres gyhoeddiadau

Enw	Communications in Computer and Information Science
Cyhoeddwr	Springer Nature
Cyfrol	894
ISSN (Argraffiad)	1865-0929
ISSN (Electronig)	1865-0937

Cynhadledd

Cynhadledd	Proceedings 22nd Conference Medical Image Understanding and Analysis
Teitl cryno	MIUA 2018
Gwlad/Tiriogaeth	Teyrnas Unedig Prydain Fawr a Gogledd Iwerddon
Dinas	Southampton
Cyfnod	09 Gorff 2018 → 11 Gorff 2018

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10.1007/978-3-319-95921-4_15

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George, M., & Zwiggelaar, R. (2018). Breast Tissue Classification Using Local Binary Pattern Variants: A Comparative Study. Yn M. Nixon, S. Mahmoodi, & R. Zwiggelaar (Gol.), Proceedings 22nd Conference, Medical Image Understanding and Analysis 2018 (tt. 143-152). (Communications in Computer and Information Science; Cyfrol 894). Springer Nature. https://doi.org/10.1007/978-3-319-95921-4_15

@inproceedings{fc95f46c7b0d4f78960720598811279b,

title = "Breast Tissue Classification Using Local Binary Pattern Variants: A Comparative Study",

abstract = "Mammographic tissue density is considered to be one of the major risk factors for developing breast cancer. In this paper we use quantitative measurements of Local Binary Patterns and its variants for breast tissue classification. We compare the classification results of LBP, ELBP, Uniform ELBP and M-ELBP for classifying mammograms as fatty, glandular and dense. A Bayesian-Network classifier is used with stratified ten-fold cross-validation. The experimental results indicate that ELBP patterns at different orientations extract more relevant elliptical breast tissue information from the mammograms indicating the importance of directional filters for breast tissue classification.",

author = "Minu George and Reyer Zwiggelaar",

year = "2018",

month = aug,

day = "21",

doi = "10.1007/978-3-319-95921-4\_15",

language = "English",

isbn = "978-3-319-95920-7",

series = "Communications in Computer and Information Science",

publisher = "Springer Nature",

pages = "143--152",

editor = "Mark Nixon and Sasan Mahmoodi and Reyer Zwiggelaar",

booktitle = "Proceedings 22nd Conference, Medical Image Understanding and Analysis 2018",

address = "Switzerland",

note = "Proceedings 22nd Conference Medical Image Understanding and Analysis, MIUA 2018 ; Conference date: 09-07-2018 Through 11-07-2018",

}

George, M & Zwiggelaar, R 2018, Breast Tissue Classification Using Local Binary Pattern Variants: A Comparative Study. yn M Nixon, S Mahmoodi & R Zwiggelaar (gol.), Proceedings 22nd Conference, Medical Image Understanding and Analysis 2018. Communications in Computer and Information Science, cyfrol. 894, Springer Nature, tt. 143-152, Proceedings 22nd Conference Medical Image Understanding and Analysis, Southampton, Teyrnas Unedig Prydain Fawr a Gogledd Iwerddon, 09 Gorff 2018. https://doi.org/10.1007/978-3-319-95921-4_15

Breast Tissue Classification Using Local Binary Pattern Variants: A Comparative Study. / George, Minu; Zwiggelaar, Reyer.
Proceedings 22nd Conference, Medical Image Understanding and Analysis 2018. gol. / Mark Nixon; Sasan Mahmoodi; Reyer Zwiggelaar. Springer Nature, 2018. t. 143-152 (Communications in Computer and Information Science; Cyfrol 894).

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Trafodion Cynhadledd (ISBN)

TY - GEN

T1 - Breast Tissue Classification Using Local Binary Pattern Variants

T2 - Proceedings 22nd Conference Medical Image Understanding and Analysis

AU - George, Minu

AU - Zwiggelaar, Reyer

PY - 2018/8/21

Y1 - 2018/8/21

N2 - Mammographic tissue density is considered to be one of the major risk factors for developing breast cancer. In this paper we use quantitative measurements of Local Binary Patterns and its variants for breast tissue classification. We compare the classification results of LBP, ELBP, Uniform ELBP and M-ELBP for classifying mammograms as fatty, glandular and dense. A Bayesian-Network classifier is used with stratified ten-fold cross-validation. The experimental results indicate that ELBP patterns at different orientations extract more relevant elliptical breast tissue information from the mammograms indicating the importance of directional filters for breast tissue classification.

AB - Mammographic tissue density is considered to be one of the major risk factors for developing breast cancer. In this paper we use quantitative measurements of Local Binary Patterns and its variants for breast tissue classification. We compare the classification results of LBP, ELBP, Uniform ELBP and M-ELBP for classifying mammograms as fatty, glandular and dense. A Bayesian-Network classifier is used with stratified ten-fold cross-validation. The experimental results indicate that ELBP patterns at different orientations extract more relevant elliptical breast tissue information from the mammograms indicating the importance of directional filters for breast tissue classification.

U2 - 10.1007/978-3-319-95921-4_15

DO - 10.1007/978-3-319-95921-4_15

M3 - Conference Proceeding (ISBN)

SN - 978-3-319-95920-7

T3 - Communications in Computer and Information Science

SP - 143

EP - 152

BT - Proceedings 22nd Conference, Medical Image Understanding and Analysis 2018

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George M, Zwiggelaar R. Breast Tissue Classification Using Local Binary Pattern Variants: A Comparative Study. Yn Nixon M, Mahmoodi S, Zwiggelaar R, golygyddion, Proceedings 22nd Conference, Medical Image Understanding and Analysis 2018. Springer Nature. 2018. t. 143-152. (Communications in Computer and Information Science). Epub 2018 Awst 20. doi: 10.1007/978-3-319-95921-4_15

Breast Tissue Classification Using Local Binary Pattern Variants: A Comparative Study

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