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.

Many segmentation methods for thoracic volume data require manual input in the form of a seed point, initial contour, volume of interest etc. The aim of the work presented here is to further automate this segmentation initialization step. In this paper an anatomical modeling and matching method is proposed to coarsely segment thoracic volume data into anatomically labeled regions. An anatomical model of the thorax is constructed in two steps: 1) individual organs are modeled with blended fuzzy implicit surfaces and 2) the single organ models are grouped into a tree structure with a solid modeling technique named constructive solid geometry (CSG). The combination of CSG with fuzzy implicit surfaces allows a hierarchical scene description by means of a boundary model, which characterizes the scene volume as a boundary potential function. From this boundary potential, an energy function is defined which is minimal when the model is registered to the tissue-air transitions in thoracic magnetic resonance imaging (MRI) data. This allows automatic registration in three steps: feature detection, initial positioning and energy minimization. The model matching has been validated in phantom simulations and on 15 clinical thoracic volume scans from different subjects. In 13 of these sets the matching method accurately partitioned the image volumes into a set of volumes of interest for the heart, lungs, cardiac ventricles, and thorax outlines. The method is applicable to segmentation of various types of thoracic MR-images, provided that a large part of the thorax is contained in the image volume.