Multi-scale textural feature extraction and particle swarm optimization based model selection for false positive reduction in mammography

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
Imad ZyoutMarcin Grzegorzek

Abstract

The high number of false positives and the resulting number of avoidable breast biopsies are the major problems faced by current mammography Computer Aided Detection (CAD) systems. False positive reduction is not only a requirement for mass but also for calcification CAD systems which are currently deployed for clinical use. This paper tackles two problems related to reducing the number of false positives in the detection of all lesions and masses, respectively. Firstly, textural patterns of breast tissue have been analyzed using several multi-scale textural descriptors based on wavelet and gray level co-occurrence matrix. The second problem addressed in this paper is the parameter selection and performance optimization. For this, we adopt a model selection procedure based on Particle Swarm Optimization (PSO) for selecting the most discriminative textural features and for strengthening the generalization capacity of the supervised learning stage based on a Support Vector Machine (SVM) classifier. For evaluating the proposed methods, two sets of suspicious mammogram regions have been used. The first one, obtained from Digital Database for Screening Mammography (DDSM), contains 1494 regions (1000 normal and 494 abnormal samples)....Continue Reading

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Citations

Dec 29, 2015·Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society·Ewa Pietka, Arkadiusz Gertych
Jan 23, 2017·International Journal of Computer Assisted Radiology and Surgery·Xiaopan XuHongbing Lu
Oct 26, 2018·Journal of Healthcare Engineering·Meenakshi M PawarAkshay Dudhane
Mar 4, 2017·Computer Methods and Programs in Biomedicine·Peng CaoOsmar Zaiane

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