Learn More
Propose a fully automatic 3D segmentation framework to segment liver on challenging cases that contain the low contrast of adjacent organs and the presence of pathologies from abdominal CT images. First, all of the atlases are weighted in the selected training datasets by calculating the similarities between the atlases and the test image to dynamically(More)
Deformable models have been quite popular in medical image analysis, particularly in image segmentation. However, when applied to 3D volumetric data, their high computational cost can be a problem. In this paper, we describe a new efficient 3D segmentation method based on deformable simplex meshes. The greedy algorithm, which has proven more computational(More)
We describe a novel appearance model with optimal combined features to produce the accurate vessel segmentation. It starts with investigating a set of multi-scale vessel features, followed by a weighed approach to optimally combine different features. Then the optimally combined features advantage the appearance model to reveal more detailed information of(More)
In this paper, a fully automatic method was proposed for lung segmentation with juxta-pleural nodules from CT. The approach consists of three phases: skin boundary detection, rough segmentation of lung contour, and pulmonary parenchyma refinement. Firstly, chest boundary is extracted through image aligning, morphology operation and connective region(More)
One major limiting factor that prevents the accurate delineation of human organs has been the presence of severe pathology and pathology affecting organ borders. Overcoming these limitations is exactly what we are concerned in this study. We propose an automatic method for accurate and robust pathological organ segmentation from CT images. The method is(More)
This paper presents a new pulmonary nodules computer-aided detection system in chest CT images utilizing the adaptive fuzzy C-Means (AFCM) technologies. Since rough segmentation of nodules tends to result in high false positive (FP), the main purpose of this study is to reduce the false-positive of candidate nodules via the clustering and classifying(More)