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Pseudocolor enhancement of mammogram texture abnormalities
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SYSNO ASEP 0505448 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Pseudocolor enhancement of mammogram texture abnormalities Author(s) Haindl, Michal (UTIA-B) RID, ORCID
Remeš, Václav (UTIA-B) RIDNumber of authors 2 Article number 6 Source Title Machine Vision and Applications. - : Springer - ISSN 0932-8092
Roč. 30, č. 4 (2019), s. 785-794Number of pages 10 s. Publication form Print - P Language eng - English Country DE - Germany Keywords Mammograms ; Region of interest enhancement ; Computer-aided diagnosis ; Texture model ; Markov random field Subject RIV BD - Theory of Information OECD category Automation and control systems R&D Projects GA19-12340S GA ČR - Czech Science Foundation (CSF) Method of publishing Limited access Institutional support UTIA-B - RVO:67985556 UT WOS 000469483000017 EID SCOPUS 85064633416 DOI 10.1007/s00138-019-01028-6 Annotation We present a novel method for enhancing texture irregularities, both lesions and microcalcifications, in digital X-ray mammograms. It can be implemented in computer-aided diagnostic systems to help improve radiologists’ diagnosis precision. The method provides three different outputs aimed at enhancing three different sizes of mammogram abnormalities. Our approach uses a two-dimensional adaptive causal autoregressive texture model to represent local texture characteristics. Based on these, we enhance suspicious breast tissue abnormalities, such as microcalcifications and masses, to make signs of developing cancer better visually discernible. We extract over 200 local textural features from different frequency bands, which are then combined into a single multichannel image using the Karhunen–Loeve transform. We propose an extension to existing contrast measures for the evaluation of contrast around regions of interest. Our method was extensively tested on the INbreast database and compared both visually and numerically with three state-of-the-art enhancement methods, with favorable results. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2020 Electronic address https://link.springer.com/article/10.1007%2Fs00138-019-01028-6
Number of the records: 1