The goal of image segmentation is to partition a digital image into disjoint regions of interest. Of the many proposed image-segmentation methods, region growing has been one of the most popular. Research on region growing, however, has focused primarily on the design of feature measures and on growing and merging criteria. Most of these methods have an inherent dependence on the order in which...

The performance of segmentation algorithms often depends on numerous parameters such as initial seed and contour placement, threshold selection, and other region-dependent a priori knowledge. While necessary for successful segmentation, appropriate setting of these parameters can be difficult to achieve and requires a user experienced with the algorithm and knowledge of the application field. I...

This paper describes generalization of multi-class region growing algorithm allowing for segmentation of 3D images (series of slices). The multi-class region growing algorithm was proposed in [1]. Additionally, a new method for finding the start region was presented. As its 2D version the new algorithm does not need initial parameters since it features segmentation quality assessment. A series ...

The cell segmentation presented in this paper is part of current research work on an automated cell tracking project. Its aim is the tracking of the movement of representative cells in a 3D collagen gel in order to determine the activity of the cell after the injection of certain medical substances. A major requirement for this project is an efficient and robust method for the segmentation of c...

Connection Machine Nawal Copty, Sanjay Ranka, Geo rey Fox, and Ravi Shankar School of Computer and Information Science Syracuse University, Syracuse, NY 13244 Email: nkcopty, ranka, [email protected], [email protected] Published in: Proc. 22nd International Conference on Parallel Processing, 1993, Vol. III, pp. 102-105 Abstract { This paper presents a parallel algorithm for solving the re...

I Abstract-We present a method that combines region growing and edge detection for image segmentation. We start with a split-and-merge algorithm [12] where the parameters have been set up so that an oversegmented image results. Then region boundaries are eliminated or modified on the basis of criteria that integrate contrast with boundary smoothness, variation of the image gradient along the bo...

The segmentation of linear structures in a gray-scale image is widely used in a variety of image analysis and visualization tasks. In case of biomedical image analysis of volumetric data (such as CT or MRI scans), it is very common that a linear structure (blood vessel) is attached to a sheet-shaped (planar) and/or blob-shaped (ellipsoid) region (large tissue or fluid mass such as cistern basin...

Region growing algorithm has successfully been used as a segmentation technique of digital images. The current study went one step further by utilizing the potential use of thresholding the region growing algorithm as features extraction technique. The proposed features extraction algorithm is called seeded region growing features extraction (SRGFE). This algorithm was used to extract four feat...

A method based on extracted trunks is proposed for automatic determination of corresponding trees between a georeferenced pair of terrestrial images and airborne laser scanner data in a wooded area. Trunks in the terrestrial images are obtained by region growing segmentation between left and right edges of trunks detected within a region of interest. Trunks in a digital tree height model genera...

Fingerprint Segmentation is one of the critical and important steps in Automatic Fingerprint Recognition System (AFIS). It is a process that separates the fingerprint image into two regions, the foreground and background. The foreground region will have the fingerprint region containing features for recognition and the background region is the unwanted region which can be excluded from further ...