Pseudocolor enhancement of mammogram texture abnormalities

Kód výsledku v IS VaVaI

RIV/67985556:_____/19:00505448 - isvavai.cz

Výsledek na webu

https://link.springer.com/article/10.1007%2Fs00138-019-01028-6

DOI - Digital Object Identifier

10.1007/s00138-019-01028-6

Jazyk výsledku

angličtina

Název v původním jazyce

Pseudocolor enhancement of mammogram texture abnormalities

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Pseudocolor enhancement of mammogram texture abnormalities

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

GA19-12340S: Rozpoznávání povrchových materiálů při proměnlivých podmínkách optického pozorování

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Machine Vision and Applications

ISSN

0932-8092

e-ISSN

—

Svazek periodika

30

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

10

Strana od-do

785-794

Kód UT WoS článku

000469483000017

EID výsledku v databázi Scopus

2-s2.0-85064633416