The spectral minutiae representation (SMC) has been recently proposed as a novel method to minutiae‐based fingerprint recognition, which is invariant to minutiae translation and rotation and presents low computational complexity. As high‐resolution palmprint recognition is also mainly based on minutiae sets, SMC has been applied to palmprints and used in full‐to‐full palmprint matching. However, the performance of that approach was still limited. As one of the main reasons for this is the much bigger size of a palmprint compared with a fingerprint, the authors propose a division of the palmprint into smaller regions. Then, to further improve the performance of spectral minutiae‐based palmprint matching, in this work the authors present anatomically inspired regional fusion while using SMC for palmprints. Firstly, the authors consider three regions of the palm, namely interdigital, thenar and hypothenar, which have inspiration in anatomic cues. Then, the authors apply SMC to region‐to‐region palmprint comparison and study regional discriminability when using the method. After that, the authors implement regional fusion at score level by combining the scores of different regional comparisons in the palm with two fusion methods, that is, sum rule and logistic regression. The authors evaluate region‐to‐region comparison and regional fusion based on spectral minutiae matching on a public high‐resolution palmprint database, THUPALMLAB. Both manual segmentation and automatic segmentation are performed to obtain the three palm regions for each palm. Essentially using the complex SMC, the authors obtain results on region‐to‐region comparison which show that the hypothenar and interdigital regions outperform the thenar region. More importantly, the authors achieve significant performance improvements by regional fusion using regions segmented both manually and automatically. One main advantage of the approach the authors took is that human examiners can segment the palm into the three regions without prior knowledge of the system, which makes the segmentation process easy to be incorporated in protocols such as in forensic science.

Magnetic Resonance Imaging (MRI) is a powerful technique for imaging cardiovascular diseases. The introduction of cardiovascular MRI into clinical practice is however hampered by the lack of efficient and accurate image analysis methods. This paper focuses on the evaluation of blood perfusion in the myocardium (the heart muscle) from MR images, using contrast-enhanced ECG-triggered MRI. We have developed an automatic quantitative analysis method, which works as follows. First, image registration is used to compensate for translation and rotation of the myocardium over time. Next, the boundaries of the myocardium are detected and for each position within the myocardium a time-intensity profile is constructed. The time interval during which the contrast agent passes for the first time through the left ventricle and the myocardium is detected and various parameters are measured from the time-intensity profiles in this interval. The measured parameters are visualized as color overlays on the original images. Analysis results are stored, so that they can later on be compared for different stress levels of the heart. The method is described in detail in this paper and preliminary validation results are presented.

Automated image-based plant identification systems are black-boxes, failing to provide an explanation of a classification. Such explanations are seen as being essential by taxonomists and are part of the traditional procedure of plant identification. In this paper, we propose a different method by extracting explicit features from flower images that can be employed to generate explanations. We take the benefit of feature extraction derived from the taxonomic characteristics of plants, with the orchids as an example domain. Feature classifiers were developed using deep neural networks. Two different methods were studied: (1) a separate deep neural network was trained for every individual feature, and (2) a single, multi-label, deep neural network was trained, combining all features. The feature classifiers were tested in predicting 63 orchid species using naive Bayes (NB) and tree-augmented Bayesian networks (TAN). The results show that the accuracy of the feature classifiers is in the range 83-93%. By combining these features using NB and TAN the species can be predicted with an accuracy of 88.9%, which is better than a standard pre-trained deep neural-network architecture, but inferior to a deep learning architecture after fine-tuning of multiple layers. The proposed novel feature extraction method still performs well for identification and is explainable, as opposed to black-box solutions that only aim for the best performance. Graphical abstract

Eigenvalue analysis is an important aspect in many data modeling methods. Unfortunately, the eigenvalues of the sample covariance matrix (sample eigenvalues) are biased estimates of the eigenvalues of the covariance matrix of the data generating process (population eigenvalues). We present a new method based on bootstrapping to reduce the bias in the sample eigenvalues: the eigenvalue estimates are updated in several iterations, where in each iteration synthetic data is generated to determine how to update the population eigenvalue estimates. Comparison of the bootstrap eigenvalue correction with a state of the art correction method by Karoui shows that depending on the type of population eigenvalue distribution, sometimes the Karoui method performs better and sometimes our bootstrap method.