Mammographic Mass Identification In Dense Breasts Using Multi-Scale Analysis Of Structured Micro-Patterns
15TH INTERNATIONAL WORKSHOP ON BREAST IMAGING (IWBI2020)(2020)
Abstract
The paper proposes a novel approach for the identification of cancerous regions located in a dense part of a breast. This task is particularly challenging even for experienced radiologists due to lack of clear boundaries between the cancerous and normal tissue. Multi-scale analysis of structured micro-patterns generated from local binary patterns (LBP) was used to generate a very small number of features which allowed for successful detection of cancerous regions. The proposed technique was tested on two publicly available datasets: Digital Database for Screening Mammography (DDSM) and INbreast. The area under the receiver operating characteristic (AUC) curve for DDSM with 2 features only was 0.99 and 0.92 for INbreast with 3 features.
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Key words
breast cancer, mammography, CAD, dense ROI, local binary pattern, structured micro-patterns, machine learning
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