Journal
JOURNAL OF HEALTHCARE ENGINEERING
Volume 2018, Issue -, Pages -Publisher
HINDAWI LTD
DOI: 10.1155/2018/4015613
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Funding
- Major Program of National Natural Science Foundation of China [81727802]
- National Natural Science Foundation of China [61701404, 81671648]
- key problems of Social Development Science and Technology of Shaanxi Province [2015SF119]
- funds of clinical major projects of Tangdu Hospital [2015LCYJ001]
- Graduate Students Innovative Talents Training Program of Northwest University [YZZ15095]
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Inspired by gestalt psychology, we combine human cognitive characteristics with knowledge of radiologists in medical image analysis. In this paper, a novel framework is proposed to detect breast masses in digitized mammograms. It can be divided into three modules: sensation integration, semantic integration, and verification. After analyzing the progress of radiologist's mammography screening, a series of visual rules based on the morphological characteristics of breast masses are presented and quantified by mathematical methods. The framework can be seen as an effective trade-off between bottom-up sensation and top-down recognition methods. This is a new exploratory method for the automatic detection of lesions. The experiments are performed on Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) data sets. The sensitivity reached to 92% at 1.94 false positive per image (FPI) on MIAS and 93.84% at 2.21 FPI on DDSM. Our framework has achieved a better performance compared with other algorithms.
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