Classification of microcalcification clusters in digital mammograms using a stack generalization based classifier

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

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

12 Dyfyniadau(SciVal)
465 Wedi eu Llwytho i Lawr (Pure)

Crynodeb

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.

Iaith wreiddiolSaesneg
Rhif yr erthygl76
CyfnodolynJournal of Imaging
Cyfrol5
Rhif cyhoeddi9
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 12 Medi 2019

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