735 research outputs found
Stratified decision forests for accurate anatomical landmark localization in cardiac images
Accurate localization of anatomical landmarks is an important step in medical imaging, as it provides useful prior information for subsequent image analysis and acquisition methods. It is particularly useful for initialization of automatic image analysis tools (e.g. segmentation and registration) and detection of scan planes for automated image acquisition. Landmark localization has been commonly performed using learning based approaches, such as classifier and/or regressor models. However, trained models may not generalize well in heterogeneous datasets when the images contain large differences due to size, pose and shape variations of organs. To learn more data-adaptive and patient specific models, we propose a novel stratification based training model, and demonstrate its use in a decision forest. The proposed approach does not require any additional training information compared to the standard model training procedure and can be easily integrated into any decision tree framework. The proposed method is evaluated on 1080 3D highresolution and 90 multi-stack 2D cardiac cine MR images. The experiments show that the proposed method achieves state-of-theart landmark localization accuracy and outperforms standard regression and classification based approaches. Additionally, the proposed method is used in a multi-atlas segmentation to create a fully automatic segmentation pipeline, and the results show that it achieves state-of-the-art segmentation accuracy
Fingerprint Image-Quality Estimation and its Application to Multialgorithm Verification
Signal-quality awareness has been found to increase recognition rates and to
support decisions in multisensor environments significantly. Nevertheless,
automatic quality assessment is still an open issue. Here, we study the
orientation tensor of fingerprint images to quantify signal impairments, such
as noise, lack of structure, blur, with the help of symmetry descriptors. A
strongly reduced reference is especially favorable in biometrics, but less
information is not sufficient for the approach. This is also supported by
numerous experiments involving a simpler quality estimator, a trained method
(NFIQ), as well as the human perception of fingerprint quality on several
public databases. Furthermore, quality measurements are extensively reused to
adapt fusion parameters in a monomodal multialgorithm fingerprint recognition
environment. In this study, several trained and nontrained score-level fusion
schemes are investigated. A Bayes-based strategy for incorporating experts past
performances and current quality conditions, a novel cascaded scheme for
computational efficiency, besides simple fusion rules, is presented. The
quantitative results favor quality awareness under all aspects, boosting
recognition rates and fusing differently skilled experts efficiently as well as
effectively (by training).Comment: Published at IEEE Transactions on Information Forensics and Securit
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