Cluster-based active learning for compact image classification

In this paper, we consider active sampling to label pixels grouped with hierarchical clustering. The objective of the method is to match the data relationships discovered by the clustering algorithm with the user's desired class semantics. The first is represented as a complete tree to be pruned and the second is iteratively provided by the user. The active learning algorithm proposed searches the pruning of the tree that best matches the labels of the sampled points. By choosing the part of the tree to sample from according to current pruning's uncertainty, sampling is focused on most uncertain clusters. This way, large clusters for which the class membership is already fixed are no longer queried and sampling is focused on division of clusters showing mixed labels. The model is tested on a VHR image in a multiclass classification setting. The method clearly outperforms random sampling in a transductive setting, but cannot generalize to unseen data, since it aims at optimizing the classification of a given cluster structure.

Evaluation and comparison of current fetal ultrasound image segmentation methods for biometric measurements: a grand challenge.

This paper presents the evaluation results of the methods submitted to Challenge US: Biometric Measurements from Fetal Ultrasound Images, a segmentation challenge held at the IEEE International Symposium on Biomedical Imaging 2012. The challenge was set to compare and evaluate current fetal ultrasound image segmentation methods. It consisted of automatically segmenting fetal anatomical structures to measure standard obstetric biometric parameters, from 2D fetal ultrasound images taken on fetuses at different gestational ages (21 weeks, 28 weeks, and 33 weeks) and with varying image quality to reflect data encountered in real clinical environments. Four independent sub-challenges were proposed, according to the objects of interest measured in clinical practice: abdomen, head, femur, and whole fetus. Five teams participated in the head sub-challenge and two teams in the femur sub-challenge, including one team who tackled both. Nobody attempted the abdomen and whole fetus sub-challenges. The challenge goals were two-fold and the participants were asked to submit the segmentation results as well as the measurements derived from the segmented objects. Extensive quantitative (region-based, distance-based, and Bland-Altman measurements) and qualitative evaluation was performed to compare the results from a representative selection of current methods submitted to the challenge. Several experts (three for the head sub-challenge and two for the femur sub-challenge), with different degrees of expertise, manually delineated the objects of interest to define the ground truth used within the evaluation framework. For the head sub-challenge, several groups produced results that could be potentially used in clinical settings, with comparable performance to manual delineations. The femur sub-challenge had inferior performance to the head sub-challenge due to the fact that it is a harder segmentation problem and that the techniques presented relied more on the femur's appearance.

Equilibration and disequilibration between monazite and garnet: indication from phase-composition and quantitative texture analysis

The integration of information which can be gained from accessory [i.e. age (t)] and rock-forming minerals [i.e. temperature (T) and pressure (P)] requires a more profound understanding of the equilibration kinetics during metamorphic processes. This paper presents an approach comparing conventional P-T estimate from equilibrated assemblages of rock-forming minerals with temperature data derived from yttrium-garnet-monazite (YGM) and yttrium-garnet-xenotime (YGX) geothermometry. Such a comparison provides an initial indication on differences between equilibration of major and trace elements. Regarding this purpose, two migmatites, two polycyclic and one monocyclic gneiss from the Central Alps (Switzerland, northern Italy) were investigated. While the polycyclic samples exhibit trace-element equilibration between monazite and garnet grains assigned to the same metamorphic event, there are relics of monazite and garnet obviously surviving independent of their textural position. These observations suggest that surface processes dominate transport processes during equilibration of those samples. The monocyclic gneiss, on the contrary, displays rare isolated monazite with equilibration of all elements, despite comparably large transport distances. With a nearly linear crystal-size distribution of the garnet grain population, growth kinetics, related to the major elements, were likely surface-controlled in this sample. In contrast to these completely equilibrated examples, the migmatites indicate disequilibrium between garnet and monazite with a change in REE patterns on garnet transects. The cause for this disequilibrium may be related to a potential disequilibrium initiated by a changing bulk chemistry during melt segregation. While migmatite environments are expected to support high transport rates (i.e. high temperatures and melt presence), the evolution of equilibration in migmatites is additionaly related to change in chemistry. As a key finding, surface-controlled equilibration kinetics seem to dominate transport-controlled processes in the investigated samples. This may be decisive information towards the understanding of age data derived from monazite.

Investigation of discrete imaging models and iterative image reconstruction in differential X-ray phase-contrast tomography.

Differential X-ray phase-contrast tomography (DPCT) refers to a class of promising methods for reconstructing the X-ray refractive index distribution of materials that present weak X-ray absorption contrast. The tomographic projection data in DPCT, from which an estimate of the refractive index distribution is reconstructed, correspond to one-dimensional (1D) derivatives of the two-dimensional (2D) Radon transform of the refractive index distribution. There is an important need for the development of iterative image reconstruction methods for DPCT that can yield useful images from few-view projection data, thereby mitigating the long data-acquisition times and large radiation doses associated with use of analytic reconstruction methods. In this work, we analyze the numerical and statistical properties of two classes of discrete imaging models that form the basis for iterative image reconstruction in DPCT. We also investigate the use of one of the models with a modern image reconstruction algorithm for performing few-view image reconstruction of a tissue specimen.