Seed Point Location Research Articles

TUNNEL MODELING USING MOBILE MAPPING LIDAR POINTS

Abstract. In this paper, a method to model a tunnel using lidar points is presented. The data used was collected using Leica Pegasus Two Ultimate with a Z+F 9012 Profiler mounted on a mobile platform. The tunnel was approximately 151 m long. Visual inspection of a cross-section of the tunnel showed two rail tracks supported on ballast and sidewalks along both sidewalls of the tunnel. The walls and the ceiling of the tunnel were made of five planar surfaces. The tunnel alignment was straight, without any horizontal or vertical curves. The bearing of the central axis of the tunnel was N12.2oW. The following methodology was developed to model just the planar surfaces of the tunnel by excluding the rails, ballast, sidewalks, powerlines, and other accessories. The entire methodology was divided into three broad parts. In the first part, a model cross-section was created. Since the design plans of the tunnel were not available, the model cross-section polyline was created using mean tunnel dimensions from random cross-section points. The model cross-section consisted of the walls and the ceiling of the tunnel. Points were placed at every 1 cm along the model polyline. Six of the model points that represented the shape of the tunnel were selected as salient points. The lower-left salient point was considered as the seed point. In the second part, to define a reference axis of the tunnel, an approximate centerline was manually defined by selecting points at its start and end. Lidar points within 1 m at the start and the end of the tunnel were modeled using the model points to determine the centroids. The reference axis was determined by connecting the centroids at the start and the end of the tunnel. In the third part, the tunnel points were sliced along the reference axis at 5 cm intervals. The model cross-section was matched to points within each tunnel slice using a three-stage approach. In the first stage, the pattern of salient points was matched to the tunnel points by placing the seed point at every tunnel point location. The distances between salient points and their nearest tunnel points were calculated. Ten sets of tunnel points with the least differences to the salient points were shortlisted. In the second stage, a dense point-to-point matching was performed between the model and sliced tunnel data at the shortlisted points. The shortlisted point location with the least difference between the tunnel and the model points was considered as a match. At this point, the model points were hinged to the tunnel points at the seed point location. Hence, in the last stage, a six-parameter affine transformation was performed to match the model points to the tunnel data. The transformed model points at every 5 cm of the length of the tunnel were considered as current shape of the tunnel.

Computer-aided Kidney Segmentation on Abdominal CT Images Using Fuzzy Based Denoising for Gaussian Noise

In this paper, we have proposed an image filtering technique (fuzzy logic based approach) to preserve important signal elements such as edges, smoothing the details of an abdominal CT image such as kidney to make it appear clear and sharpener. At first, abdominal CT images are retrieved from Gaussian noise using fuzzy logic based approach. After that, kidney region have been segmented from the abdomen area using region growing algorithm. In region growing process, the first stage detects the abdomen boundary using contour detection algorithm. The second stage identifies the kidney using seed point location. After successful segmentation, the kidney region is extracted and it is given to some region growing methods such as Region Growing Interest (ROI), pixel filling, erosion, labeling and dilation to find the accurate segmented area of these abdominal CT images. The results of a series of tests on 62 images from 16 patients indicate an interrelationship up to 73% between automatic and manual segmentation.

Robust-Seed: seed-based segmentation improvement by optimisation

Many medical image segmentation methods require the selection of seed points inside the target structure. Often times, the location of these seed points determines the accuracy of the resulting target structure delineation and may lead to undesirably high delineation variability. We present Robust-Seed, a new method for automatically reducing the variability of manual and semi-automatic seed-based segmentation methods with respect to the seed point location without compromising the target structure segmentation accuracy. The inputs are a volumetric image, a seed point inside the target structure, and a seed-based segmentation method. The output is a new seed point that optimises the target structure segmentation result. The algorithm iteratively computes a new seed point location that improves the expected target structure segmentation for the given method. Experimental evaluation of seed-based fast-marching level-set and adaptive region growing segmentation of the kidney and the liver on 32 CT scans with ground-truth delineations shows that Robust-Seed yields a perfect robustness score with no significant compromise on the segmentation quality (paired t-test, p < 0.05). The key advantages of Robust-Seed are that it is automatic, that it is independent of target structure and segmentation method used, and that it applies to a wide class of anatomical structures and clinical tasks.