Automatic Segmentation of Microcalcification Clusters

Nashid Alam; Arnau Oliver; Erika R. E. Denton; Reyer Zwiggelaar

doi:10.1007/978-3-319-95921-4_24

Automatic Segmentation of Microcalcification Clusters

Nashid Alam, Arnau Oliver, Erika R. E. Denton, Reyer Zwiggelaar

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Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Trafodion Cynhadledd (Nid-Cyfnodolyn fathau)

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Early detection of microcalcification (MC) clusters plays a crucial role in enhancing breast cancer diagnosis. Two automated MC cluster segmentation techniques are proposed based on morphological operations that incorporate image decomposition and interpolation methods. For both approaches, initially the contrast between the background tissue and MC cluster was increased and subsequently morphological operations were used. Evaluation was based on the Dice similarity scores and the results of MC cluster classification. A total number of 248 (131 benign and 117 malignant) and 24 (12 benign and 12 malignant) biopsy-proven digitized mammograms were considered from the DDSM and MIAS databases, which showed a classification accuracy of 94.48 ± 1.11% and
100.00 ± 0.00% respectively.

Iaith wreiddiol	Saesneg
Teitl	Proceedings 22nd Conference, Medical Image Understanding and Analysis 2018
Golygyddion	Mark Nixon, Sasan Mahmoodi, Reyer Zwiggelaar
Cyhoeddwr	Springer Nature
Tudalennau	251-261
Nifer y tudalennau	11
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_24
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_24

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Alam, N., Oliver, A., Denton, E. R. E., & Zwiggelaar, R. (2018). Automatic Segmentation of Microcalcification Clusters. Yn M. Nixon, S. Mahmoodi, & R. Zwiggelaar (Gol.), Proceedings 22nd Conference, Medical Image Understanding and Analysis 2018 (tt. 251-261). (Communications in Computer and Information Science; Cyfrol 894). Springer Nature. https://doi.org/10.1007/978-3-319-95921-4_24

@inproceedings{6670c14699a84188966307121ffa7956,

title = "Automatic Segmentation of Microcalcification Clusters",

abstract = "Early detection of microcalcification (MC) clusters plays a crucial role in enhancing breast cancer diagnosis. Two automated MC cluster segmentation techniques are proposed based on morphological operations that incorporate image decomposition and interpolation methods. For both approaches, initially the contrast between the background tissue and MC cluster was increased and subsequently morphological operations were used. Evaluation was based on the Dice similarity scores and the results of MC cluster classification. A total number of 248 (131 benign and 117 malignant) and 24 (12 benign and 12 malignant) biopsy-proven digitized mammograms were considered from the DDSM and MIAS databases, which showed a classification accuracy of 94.48 ± 1.11% and 100.00 ± 0.00% respectively.",

author = "Nashid Alam and Arnau Oliver and Denton, {Erika R. E.} and Reyer Zwiggelaar",

year = "2018",

month = aug,

day = "21",

doi = "10.1007/978-3-319-95921-4_24",

language = "English",

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

series = "Communications in Computer and Information Science",

publisher = "Springer Nature",

pages = "251--261",

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",

}

Alam, N, Oliver, A, Denton, ERE & Zwiggelaar, R 2018, Automatic Segmentation of Microcalcification Clusters. 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. 251-261, 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_24

Automatic Segmentation of Microcalcification Clusters. / Alam, Nashid; Oliver, Arnau; Denton, Erika R. E. et al.
Proceedings 22nd Conference, Medical Image Understanding and Analysis 2018. gol. / Mark Nixon; Sasan Mahmoodi; Reyer Zwiggelaar. Springer Nature, 2018. t. 251-261 (Communications in Computer and Information Science; Cyfrol 894).

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Trafodion Cynhadledd (Nid-Cyfnodolyn fathau)

TY - GEN

T1 - Automatic Segmentation of Microcalcification Clusters

AU - Alam, Nashid

AU - Oliver, Arnau

AU - Denton, Erika R. E.

AU - Zwiggelaar, Reyer

PY - 2018/8/21

Y1 - 2018/8/21

N2 - Early detection of microcalcification (MC) clusters plays a crucial role in enhancing breast cancer diagnosis. Two automated MC cluster segmentation techniques are proposed based on morphological operations that incorporate image decomposition and interpolation methods. For both approaches, initially the contrast between the background tissue and MC cluster was increased and subsequently morphological operations were used. Evaluation was based on the Dice similarity scores and the results of MC cluster classification. A total number of 248 (131 benign and 117 malignant) and 24 (12 benign and 12 malignant) biopsy-proven digitized mammograms were considered from the DDSM and MIAS databases, which showed a classification accuracy of 94.48 ± 1.11% and 100.00 ± 0.00% respectively.

AB - Early detection of microcalcification (MC) clusters plays a crucial role in enhancing breast cancer diagnosis. Two automated MC cluster segmentation techniques are proposed based on morphological operations that incorporate image decomposition and interpolation methods. For both approaches, initially the contrast between the background tissue and MC cluster was increased and subsequently morphological operations were used. Evaluation was based on the Dice similarity scores and the results of MC cluster classification. A total number of 248 (131 benign and 117 malignant) and 24 (12 benign and 12 malignant) biopsy-proven digitized mammograms were considered from the DDSM and MIAS databases, which showed a classification accuracy of 94.48 ± 1.11% and 100.00 ± 0.00% respectively.

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U2 - 10.1007/978-3-319-95921-4_24

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M3 - Conference Proceeding (Non-Journal item)

SN - 978-3-319-95920-7

T3 - Communications in Computer and Information Science

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EP - 261

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

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A2 - Mahmoodi, Sasan

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PB - Springer Nature

T2 - Proceedings 22nd Conference Medical Image Understanding and Analysis

Y2 - 9 July 2018 through 11 July 2018

ER -

Automatic Segmentation of Microcalcification Clusters

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