| bibtype |
J -
Journal Article
|
| ARLID |
0505448 |
| utime |
20240103222124.5 |
| mtime |
20190612235959.9 |
| SCOPUS |
85064633416 |
| WOS |
000469483000017 |
| DOI |
10.1007/s00138-019-01028-6 |
| title
(primary) (eng) |
Pseudocolor enhancement of mammogram texture abnormalities |
| specification |
| page_count |
10 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0254218 |
| ISSN |
0932-8092 |
| title
|
Machine Vision and Applications |
| volume_id |
30 |
| volume |
4 (2019) |
| page_num |
785-794 |
| publisher |
|
|
| keyword |
Mammograms |
| keyword |
Region of interest enhancement |
| keyword |
Computer-aided diagnosis |
| keyword |
Texture model |
| keyword |
Markov random field |
| author
(primary) |
| ARLID |
cav_un_auth*0101093 |
| full_dept (cz) |
Rozpoznávání obrazu |
| full_dept (eng) |
Department of Pattern Recognition |
| department (cz) |
RO |
| department (eng) |
RO |
| full_dept |
Department of Pattern Recognition |
| name1 |
Haindl |
| name2 |
Michal |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0286710 |
| full_dept (cz) |
Rozpoznávání obrazu |
| full_dept |
Department of Pattern Recognition |
| department (cz) |
RO |
| department |
RO |
| full_dept |
Department of Pattern Recognition |
| name1 |
Remeš |
| name2 |
Václav |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0376011 |
| project_id |
GA19-12340S |
| agency |
GA ČR |
| country |
CZ |
|
| abstract
(eng) |
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. |
| result_subspec |
WOS |
| RIV |
BD |
| FORD0 |
20000 |
| FORD1 |
20200 |
| FORD2 |
20205 |
| reportyear |
2020 |
| num_of_auth |
2 |
| mrcbC52 |
4 A hod sml 4ah 4as 20231122144046.1 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0297004 |
| mrcbC64 |
1 Department of Pattern Recognition UTIA-B 10201 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE |
| confidential |
S |
| contract |
| name |
Copyright |
| date |
20190408 |
| note |
Copyright Transfer Statement |
|
| article_num |
6 |
| mrcbC86 |
3+4 Article Biochemistry Molecular Biology|Biophysics |
| mrcbC91 |
C |
| mrcbT16-e |
COMPUTERSCIENCEARTIFICIALINTELLIGENCE|COMPUTERSCIENCECYBERNETICS|ENGINEERINGELECTRICALELECTRONIC |
| mrcbT16-j |
0.521 |
| mrcbT16-s |
0.517 |
| mrcbT16-B |
35.038 |
| mrcbT16-D |
Q3 |
| mrcbT16-E |
Q4 |
| arlyear |
2019 |
| mrcbTft |
\nSoubory v repozitáři: haindl-0505448.pdf, haindl-0505448-Copyright.pdf |
| mrcbU14 |
85064633416 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000469483000017 WOS |
| mrcbU63 |
cav_un_epca*0254218 Machine Vision and Applications 0932-8092 1432-1769 Roč. 30 č. 4 2019 785 794 Springer |
|