Abstract
The paper presents the methodology developed to solve the class imbalanced problem that occurs in the classification of Thin-Layer Chromatography (TLC) images. The proposed methodology is based on resampling, and consists in the undersampling of the majority class (normal class), while the minority classes, which contain Lysosomal Storage Disorders (LSD) samples, are oversampled with the generation of synthetic samples. For image classification two approaches are presented, one based on a hierarchical classifier and another uses a multiclassifier system, where both classifiers are trained and tested using balanced data sets. The results demonstrate a better performance of the multiclassifier system using the balanced sets.

Abstract
In spite of significant advances in image segmentation techniques, evaluation of these methods thus far has been largely subjective. Typically, the effectiveness of a new algorithm is demonstrated only by the presentation of a few segmented images that axe evaluated by some method, or it is otherwise left to subjective evaluation by the reader. We propose a new approach for evaluation of segmentation that takes into account not only the accuracy of the boundary localization of the created segments but also the under-segmentation and over-segmentation effects, regardless to the number of regions in each partition. In addition, it takes into account the way humans perceive visual information. This new metric can be applied both to automatically provide a ranking among different segmentation algorithms and to find an optimal set of input parameters of a given algorithm.

Abstract
Grouping and segmentation of images remains a challenging problem in computer vision. Recently, a number of authors have demonstrated a good performance on this task using spectral methods that are based on the eigensolution of a similarity matrix. In this paper, we implement a variation of the existing methods that combines aspects from several of the best-known eigenvector segmentation algorithms to produce a discrete optimal solution of the relaxed continuous eigensolution.

Abstract
The study of cancer cell invasion under the effect of different conditions is fundamental for the understanding of the invasion mechanism and to test possible therapies for its regulation. In this study, to simulate cancer cell invasion across tissue basement membrane, biologists established in vitro invasion assays with cancer cells invading extracellular matrix components. However, analysis of the assay is manual, being time-consuming and error-prone, which motivates an objective and automated analysis tool. With the objective of automating the analysis of cell invasion assays we present a new methodology to detect cells in 3D matrix cell assays and correctly estimate their invasion, measured by the depth of the penetration in the gel. Detection is based on the sliding band filter, by evaluating the gradient convergence and not intensity. As such it can detect low contrast cells which otherwise would be lost. For cell depth estimation we present a new tool based on the analysis of cell detections from multiple brightfield images taken at different depths of focus, using a new focus estimation approach based on the convergence gradient's magnitude. The final cell detection's precision and recall are of 0.896 and 0.910 respectively, and the average error in the cell's position estimate is of 0.41µm, 0.37µm and 3.7µm in the x, y and z directions, respectively. © 2009. The copyright of this document resides with its authors.

Abstract
In the Arabidopsis root, asymmetric stem-cell divisions produce daughters that form the different root cell types. Here we report the establishment of a confocal tracking system that allows the analysis of numbers and orientations of cell divisions in root stem cells. The system provides direct evidence that stem cells have lower division rates than cells in the proximal meristem. It also allows tracking of cell division timing, which we have used to analyse the synchronization of root cap divisions. Finally, it gives new insights into lateral root cap formation: epidermal stem-cell daughters can rotate the orientation of the division plane like the stem cell.

Abstract
Microscopy cell image analysis is a fundamental tool for biological research. In particular, multivariate fluorescence microscopy is used to observe different aspects of cells in cultures. It is still common practice to perform analysis tasks by visual inspection of individual cells which is time consuming, exhausting and prone to induce subjective bias. This makes automatic cell image analysis essential for large scale, objective studies of cell cultures. Traditionally the task of automatic cell analysis is approached through the use of image segmentation methods for extraction of cells' locations and shapes. Image segmentation, although fundamental, is neither an easy task in computer vision nor is it robust to image quality changes. This makes image segmentation for cell detection semi-automated requiring frequent tuning of parameters. We introduce a new approach for cell detection and shape estimation in multivariate images based on the sliding band filter (SBF). This filter's design makes it adequate to detect overall convex shapes and as such it performs well for cell detection. Furthermore, the parameters involved are intuitive as they are directly related to the expected cell size. Using the SBF filter we detect cells' nucleus and cytoplasm location and shapes. Based on the assumption that each cell has the same approximate shape center in both nuclei and cytoplasm fluorescence channels, we guide cytoplasm shape estimation by the nuclear detections improving performance and reducing errors. Then we validate cell detection by gathering evidence from nuclei and cytoplasm channels. Additionally, we include overlap correction and shape regularization steps which further improve the estimated cell shapes. The approach is evaluated using two datasets with different types of data: a 20 images benchmark set of simulated cell culture images, containing 1000 simulated cells; a 16 images Drosophila melanogaster Kc167 dataset containing 1255 cells, stained for DNA and actin. Both image datasets present a difficult problem due to the high variability of cell shapes and frequent cluster overlap between cells. On the Drosophila dataset our approach achieved a precision/recall of 95%/69% and 82%/90% for nuclei and cytoplasm detection respectively and an overall accuracy of 76%.