Breast Mass Detection in Digital Mammogram Based on Gestalt Psychology
Open Access
- 1 January 2018
- journal article
- research article
- Published by Hindawi Limited in Journal of Healthcare Engineering
- Vol. 2018, 1-13
- https://doi.org/10.1155/2018/4015613
Abstract
Breast Mass Detection in Digital Mammogram Based on Gestalt Psychology: 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 radiologists 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 2.21FPI on DDSM. Our framework has achieved a better performance compared with other algorithms.Keywords
Funding Information
- National Natural Science Foundation of China (81727802, 61701404, 81671648, 2015SF119, 2015LCYJ001, YZZ15095)
This publication has 47 references indexed in Scilit:
- A century of Gestalt psychology in visual perception: I. Perceptual grouping and figure–ground organization.Psychological Bulletin, 2012
- A review of automatic mass detection and segmentation in mammographic imagesMedical Image Analysis, 2010
- Cancer Statistics, 2008CA: A Cancer Journal for Clinicians, 2008
- Approaches for automated detection and classification of masses in mammogramsPattern Recognition, 2006
- Pseudo-angiomatous stromal hyperplasia presenting as a breast mass: imaging findings in three patientsThe Breast, 2004
- Automatic Identification of the Pectoral Muscle in MammogramsIEEE Transactions on Medical Imaging, 2004
- Detection of breast masses in mammograms by density slicing and texture flow-field analysisIEEE Transactions on Medical Imaging, 2001
- Computer-aided breast cancer detection and diagnosis of masses using difference of Gaussians and derivative-based feature saliencyIEEE Transactions on Medical Imaging, 1997
- An adaptive density-weighted contrast enhancement filter for mammographic breast mass detectionIEEE Transactions on Medical Imaging, 1996
- Detection of stellate distortions in mammogramsIEEE Transactions on Medical Imaging, 1996