crta
Hrvatska znanstvena Sekcija img
bibliografija
3 gif
 Naslovna
 O projektu
 FAQ
 Kontakt
4 gif
Pregledavanje radova
Jednostavno pretraživanje
Napredno pretraživanje
Skupni podaci
Upis novih radova
Upute
Ispravci prijavljenih radova
Ostale bibliografije
Slični projekti
 Bibliografske baze podataka

Pregled bibliografske jedinice broj: 877323

Časopis

Autori: Štajduhar, Ivan; Tomić, Mladen; Lerga, Jonatan
Naslov: Mirroring quasi-symmetric organ observations for reducing problem complexity
( Mirroring quasi-symmetric organ observations for reducing problem complexity )
Izvornik: Expert systems with applications (0957-4174) 85 (2017); 318-334
Vrsta rada: članak
Ključne riječi: Medical image analysis ; Within-class variation ; Organ orientation ; Model complexity ; Machine learning
( Medical image analysis ; Within-class variation ; Organ orientation ; Model complexity ; Machine learning )
Sažetak:
Following an obvious growth of available collections of medical images in recent years, both in number and in size, machine learning has nowadays become an important tool for solving various image-analysis-related problems, such as organ segmentation or injury/pathology detection. The potential of learning algorithms to produce models having good generalisation properties is highly dependent on model complexity and the amount of available data. Bearing in mind that complex concepts require the use of complex models, it is of paramount importance to mitigate representation complexity, where possible, therefore enabling the utilisation of simpler models for performing the same task. When dealing with image collections of quasi- symmetric organs, or imaging observations of organs taken from different quasi-symmetric perspectives, one way of reducing representation complexity would be aligning all the images in a collection for left-right or front-rear orientation. That way, a learning algorithm would not be dealing with learning redundant symmetric representations. In this paper, we study in detail the influence of such within-class variation on model complexity, and present a possible solution, that can be applied to medical-imaging computer-aided diagnosis systems. The proposed method involves compacting the data, extracting features and then learning to separate the mirror-image representation classes from one another. Two efficient approaches are considered for performing such orientation separation: a fully automated unsupervised approach and a semi- automated supervised approach. Both solutions are directly applicable to imaging data. Method performance is illustrated on two 2D and one 3D real-world publicly-available medical datasets, concerning different parts of human anatomy, and observed using different imaging techniques: colour fundus photography, mammography CT scans and volumetric knee-joint MR scans. Experimental results suggest that efficient organ-mirroring orientation- classifier models, having expected classification accuracy greater than 99%, can be estimated using either the unsupervised or the supervised approach. In the presence of noise, however, an equally good performance can be achieved only by using the supervised approach, learning from a small subset of labelled data.
Izvorni jezik: eng
Rad je indeksiran u
bazama podataka:
Current Contents Connect (CCC)
Scopus
SCI-EXP, SSCI i/ili A&HCI
Science Citation Index Expanded (SCI-EXP) (sastavni dio Web of Science Core Collectiona)
Kategorija: Znanstveni
Znanstvena područja:
Računarstvo,Kliničke medicinske znanosti
URL Internet adrese: http://www.sciencedirect.com/science/article/pii/S0957417417303640
https://doi.org/10.1016/j.eswa.2017.05.041
Broj citata:
Altmetric:
DOI: 10.1016/j.eswa.2017.05.041
URL cjelovitog teksta:
Google Scholar: Mirroring quasi-symmetric organ observations for reducing problem complexity
Upisao u CROSBI: Ivan Štajduhar (istajduh@riteh.hr), 25. Svi. 2017. u 21:08 sati



  Verzija za printanje   za tiskati


upomoc
foot_4