Survey on liver CT image segmentation methods <s> Region-growing based automatic methods <s> Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications. <s> BIB001 </s> Survey on liver CT image segmentation methods <s> Region-growing based automatic methods <s> Purpose ::: Quantitative assessment and essentially segmentation of liver and its tumours are of clinical importance in various procedures such as diagnosis, treatment planning, and monitoring. Moreover, segmentation of liver is the basis of further processing such as visualization, liver resection planning, and liver shape analysis. In this paper, we propose an algorithm to estimate an initial liver boundary. <s> BIB002
An advanced region-growing technique is employed by Rusk et al. (2007) . In their research, the histogram of intensities is analyzed to estimate the intensity distribution of the liver. The detected upper and lower values are employed to threshold the image to a binary mask, which is subsequently eroded with a large kernel. This process results in an isolated region in the center of the liver that is used as seed region for subsequent region-growing. After re-estimating the intensity distribution of liver tissue from the seed region, adjacent voxels are consecutively added if all voxels within 5 mm radius are within the estimated intensity interval. To prevent leakage of the region-growing into the heart, a surface connecting the bottom parts of both lung lobes is detected in advance and used as blocker. Post-processing is started at the boundary to the right lung lobe, a new intensity distribution is estimated from not yet classified voxels and employed by a locally constrained second region-growing in this area. Subsequently, leakage through the Vena Cava is corrected by detecting circles of predefined radius in transversal slices and deleting them if they fulfill the required length criterion. Cavity filling is employed to label vessel trees and smaller tumors as liver tissue. Finally, the binary mask is converted to a mesh, smoothed, and converted back to voxel data of the original resolution. Clustering, voxel classification, EM algorithm and region-growing to segment the liver in CT images are employed in the method proposed by  . The EM algorithm is used to define confidence intervals for the intensity distributions of air, fat, soft-tissue, and bone in the supplied training images. Using these intensity values, quad-tree decomposition is employed to separate each slice of the image to be analyzed into tissue and non-tissue regions. For all tissue regions, Haralick texture features BIB001 ) are calculated and forwarded to a classification and regression tree BIB002 . The tree then estimates the probability for liver tissue. The region with the highest probability of liver tissue in all slices is selected as seed region. A 2D region-growing process is started from there, adding new voxels if all gray-values in the 9 × 9 neighborhood fall within the trained confidence interval.