Fully automated attenuation measurement and motion correction in FLIP image sequences

Fluorescence loss in photobleaching (FLIP) is a method to study compartment connectivity in living cells. A FLIP sequence is obtained by alternatively bleaching a spot in a cell and acquiring an image of the complete cell. Connectivity is estimated by comparing fluorescence signal attenuation in different cell parts. The measurements of the fluorescence attenuation are hampered by the low signal to noise ratio of the FLIP sequences, by sudden sample shifts and by sample drift. This paper describes a method that estimates the attenuation by modeling photobleaching as exponentially decaying signals. Sudden motion artifacts are minimized by registering the frames of a FLIP sequence to target frames based on the estimated model and by removing frames that contain deformations. Linear motion (sample drift) is reduced by minimizing the entropy of the estimated attenuation coefficients. Experiments on 16 in vivo FLIP sequences of muscle cells in Drosophila show that the proposed method results in fluorescence attenuations similar to the manually identified gold standard, but with standard deviations of approximately 50 times smaller. As a result of this higher precision, cell compartment edges and details such as cell nuclei become clearly discernible. The main value of this method is that it uses a model of the bleaching process to correct motion and that the model based fluorescence intensity and attenuation estimates can be interpreted easily. The proposed method is fully automatic, and runs in approximately one minute per sequence, making it suitable for unsupervised batch processing of large data series.     

A multiresolution image segmentation technique based on pyramidalsegmentation and fuzzy clustering

In this paper, an unsupervised image segmentation technique is presented, which combines pyramidal image segmentation with the fuzzy c-means clustering algorithm. Each layer of the pyramid is split into a number of regions by a root labeling technique, and then fuzzy c-means is used to merge the regions of the layer with the highest image resolution. A cluster validity functional is used to find the optimal number of objects automatically. Segmentation of a number of synthetic as well as clinical images is illustrated and two fully automatic segmentation approaches are evaluated, which determine the left ventricular volume (LV) in 140 cardiovascular magnetic resonance (MR) images. First fuzzy c-means is applied without pyramids. In the second approach the regions generated by pyramidal segmentation are merged by fuzzy c-means. The correlation coefficients of manually and automatically defined LV lumen of all 140 and 20 end-diastolic images were equal to 0.86 and 0.79, respectively, when images were segmented with fuzzy c-means alone. These coefficients increased to 0.90 and 0.93 when the pyramidal segmentation was combined with fuzzy c-means. This method can be applied to any dimensional representation and at any resolution level of an image series. The evaluation study shows good performance in detecting LV lumen in MR images.