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access icon free Superpixel texture analysis for classification of breast masses in dense background

© The Institution of Engineering and Technology
Received 22/11/2017, Accepted 18/03/2018, Revised 12/02/2018, Published 21/03/2018

Finding masses in dense background is a difficult task for even experienced radiologist. It is due to the similarity of intensity between the masses and the overlapped normal dense tissues. A novel method for classification of masses localised in dense background of breast is proposed. Nine structured superpixel patterns were generated using local binary pattern technique on superpixels. Analysis of these nine structured superpixel patterns revealed the most prominent ones, allowing for successful classification of malignant masses and normal dense breast regions. Two mammographic databases were used to evaluate the proposed approach: the publicly available digital database for screening mammography (DDSM), and a local database of mammograms (BreastScreen SA, BSSA). A total of 525 regions of interest (ROIs) were used (301 extracted from DDSM and 224 from BSSA). All 525 ROIs were localised in dense backgrounds of breasts. The results indicate that features generated from structured superpixel patterns can produce very effective and efficient texture descriptors of breast masses localised in dense background. Using Fisher linear discriminant analysis classifier, an area under the receiver operating characteristic curve score of 0.96 was achieved for DDSM and 0.93 for BSSA with only six features.

Inspec keywords: cancer; feature extraction; mammography; medical image processing; image classification; image texture

Other keywords: superpixel texture analysis; dense background; structured superpixel patterns; local binary pattern technique; overlapped normal dense tissues; superpixels; breast masses; malignant masses; normal dense breast regions

Subjects: Other topics in statistics; Computer vision and image processing techniques; X-ray techniques: radiography and computed tomography (biomedical imaging/measurement); Optical, image and video signal processing; X-rays and particle beams (medical uses); Patient diagnostic methods and instrumentation; Biology and medical computing

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