Investigating materials for breast nodules simulation by using segmentation and similarity analysis of digital images

Siqueira, Paula N.; Marcomini, Karem D.; Sousa, Maria Angélica Zucareli; Schiabel, Homero

doi:10.1117/12.2082184

Cited by 1 publication

(1 citation statement)

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“…In order to overcome such limitations, we have developed in previous works [SIQUEIRA et al, 2015], [SOUZA et al, 2018] an innovative breast phantom, especially designed to be used in evaluating computational techniques designed either for detecting relevant findings or for their interpretation. Its main characteristic is the possibility of obtaining images of random distribution of simulated structures over many layers of random background distribution in order to simulate not only different thicknesses but also different densities.…”

Section: Introductionmentioning

confidence: 99%

Features of a Free Mammography Database to Manage a Random Breast Phantom Images Dataset Intended for Testing Cad Schemes

Schiabel¹,

Matheus²,

Cardoso³

2022

JHS

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A previous structured images database intended to be used as a tool to researchers testing developed image processing techniques in mammography is being upgraded with new tools and images sets. Its original management system allows to choose and download high quality mammographic images, as from old digitized films as well as from direct digital files acquired more recently. Its search engine is of freely access online, it has incorporated new features, and new types of images intended to aid performance tests of processing schemes. In this sense, we discuss here new tools implemented in the management system to be able to handle images of a new breast phantom with random distributions, exposed to different DR mammography equipment. We described some of the new database management system features to archive, search and download phantom images, together with an evaluation which compares these kind of images with those from actual breasts in order to validate this new set in terms of visual analysis. Previous results not only have indicated a reasonable level of similarity between phantom (ie., simulated) images and actual ones, but the utility of implement this dataset as a section of our mammography database in aiding performance tests of CADx schemes techniques addressed to detect and/or classify signals of interest in digital mammography.

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Section: Introductionmentioning

confidence: 99%