Classification of Spectroscopic Images in the DIROlab Environment

Kaster, Frederik O.; Kelm, B. Michael; Zechmann, Christian M.; Weber, Marc‐André; Hamprecht, Fred A.; Nix, O.

doi:10.1007/978-3-642-03904-1_70

Cited by 2 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3.1 Exemplary application to 1.5 Tesla data of the prostate As a case study, we recapitulate the results from a previous study, in which we validated our library on 1.5 Tesla MRSI data of prostate carcinomas [22]. We used two different data sets for the training of signal quality and of voxel class classifiers: For signal quality classification, we provided 36864 training spectra and 45312 test spectra and extracted 101 magnitude channels as features during preprocessing (data set 1).…”

Section: Case Studiesmentioning

confidence: 83%

An object-oriented library for systematic training and comparison of classifiers for computer-assisted tumor diagnosis from MRSI measurements

et al. 2010

View full text Add to dashboard Cite

We present an object-oriented library for the systematic training, testing and benchmarking of classification algorithms for computer-assisted diagnosis tasks, with a focus on tumor probability estimation from magnetic resonance spectroscopy imaging (MRSI) measurements. In connection with a graphical user interface for data annotation, it allows clinical end users to flexibly adapt these classifiers towards changed classification tasks, to benchmark various classifiers and preprocessing steps and to perform quality control of the results. This poses an advantage over previous classification software solutions, which required expert knowledge in pattern recognition techniques in order to adapt them to changes in the data acquisition protocols. This software will constitute a major part of the MRSI analysis functionality of RONDO, an integrated software platform for cancer diagnosis and therapy planning which is under current development

show abstract

Section: Case Studiesmentioning

confidence: 83%