Classification of Microcalcification Clusters in Digital Mammograms Using a Stack Generalization Based Classifier
This paper presents a machine learning based approach for the discrimination of malignant and benign microcalcification (MC) clusters in digital mammograms. A series of morphological operations was carried out to facilitate the feature extraction from segmented microcalcification. A combination of morphological, texture, and distribution features from individual MC components and MC clusters were extracted and a correlation-based feature selection technique was used. The clinical relevance of the selected features is discussed. The proposed method was evaluated using three different databases: Optimam Mammography Image Database (OMI-DB), Digital Database for Screening Mammography (DDSM), and Mammographic Image Analysis Society (MIAS) database. The best classification accuracy ( 95.00 ± 0.57 %) was achieved for OPTIMAM using a stack generalization classifier with 10-fold cross validation obtaining an A z value equal to 0.97 ± 0.01.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2019 |
|---|---|
| Erschienen: |
2019 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:5 |
|---|---|
| Enthalten in: |
Journal of imaging - 5(2019), 9 vom: 12. Sept. |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Alam, Nashid [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Classification |
|---|
| Anmerkungen: |
Date Revised 03.09.2021 published: Electronic Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.3390/jimaging5090076 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM330034359 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM330034359 | ||
| 003 | DE-627 | ||
| 005 | 20231225210425.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2019 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.3390/jimaging5090076 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1100.xml |
| 035 | |a (DE-627)NLM330034359 | ||
| 035 | |a (NLM)34460670 | ||
| 035 | |a (PII)76 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Alam, Nashid |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Classification of Microcalcification Clusters in Digital Mammograms Using a Stack Generalization Based Classifier |
| 264 | 1 | |c 2019 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 03.09.2021 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a This paper presents a machine learning based approach for the discrimination of malignant and benign microcalcification (MC) clusters in digital mammograms. A series of morphological operations was carried out to facilitate the feature extraction from segmented microcalcification. A combination of morphological, texture, and distribution features from individual MC components and MC clusters were extracted and a correlation-based feature selection technique was used. The clinical relevance of the selected features is discussed. The proposed method was evaluated using three different databases: Optimam Mammography Image Database (OMI-DB), Digital Database for Screening Mammography (DDSM), and Mammographic Image Analysis Society (MIAS) database. The best classification accuracy ( 95.00 ± 0.57 %) was achieved for OPTIMAM using a stack generalization classifier with 10-fold cross validation obtaining an A z value equal to 0.97 ± 0.01 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a classification | |
| 650 | 4 | |a digital mammogram | |
| 650 | 4 | |a microcalcification | |
| 650 | 4 | |a morphological features | |
| 650 | 4 | |a stack generalization | |
| 700 | 1 | |a R E Denton, Erika |e verfasserin |4 aut | |
| 700 | 1 | |a Zwiggelaar, Reyer |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of imaging |d 2016 |g 5(2019), 9 vom: 12. Sept. |w (DE-627)NLM26971538X |x 2313-433X |7 nnns |
| 773 | 1 | 8 | |g volume:5 |g year:2019 |g number:9 |g day:12 |g month:09 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.3390/jimaging5090076 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 5 |j 2019 |e 9 |b 12 |c 09 | ||