Background: Accurate brain tissue segmentation from magnetic resonance (MR) images is an important step in analysis of cerebral images. There are software packages which are used for brain segmentation. These packages usually contain a set of skull stripping, intensity non-uniformity (bias) correction and segmentation routines. Thus, assessment of the quality of the segmented gray matter (GM), ...

his paper presents a novel range image segmentation algorithm based on a newly proposed obust estimator: Adaptive Scale Sample Consensus (ASSC) [28]. The proposed algorithm is a odel-based top-down technique and directly extracts the required primitives (models) from the aw images. Compared with current popular methods (region-based and edge-based methods), the lgorithm is very robust to noisy ...

The purpose of the present experiment is to further understand the effect of levels of processing (top-down vs. bottom-up) on the perception of movement kinematics and primitives for grasping actions in order to gain insight into possible primitives used by the mirror system. In the present study, we investigated the potential of identifying such primitives using an action segmentation task. Sp...

During the process of unknown word detection in Chinese word segmentation, many detected word candidates are invalid. These false unknown word candidates deteriorate the overall segmentation accuracy, as it will affect the segmentation accuracy of known words. Therefore, we propose to eliminate as many invalid word candidates as possible by a pruning process. Our experiments show that by cuttin...

In this paper we propose a fully automatic 2-stage cascaded approach for segmentation of liver and its tumors in CT (Computed Tomography) images using densely connected fully convolutional neural network (DenseNet). We independently train liver and tumor segmentation models and cascade them for a combined segmentation of the liver and its tumor. The first stage involves segmentation of liver an...

The process of segmenting an input image refers to the task of determining coherent image regions which are likely to correspond to the existing objects in the image. An image segmentation algorithm follows some basic grouping rules and usually attaches a label to each pixel, denoting which region the pixel belongs to. One such way to model segmentation as a labeling problem is to consider an i...

In cluttered scenes, some object boundaries may not be marked by image cues. In such cases, the boundaries must be defined top-down as a result of object recognition. Here we ask if observers can retain the boundaries of several recognized objects in order to segment an unfamiliar object. We generated scenes consisting of neatly stacked objects, and the objects themselves consisted of neatly st...

E ver since the establishment of cell theory in the early 19th century, which recognized the cell as the fundamental building unit of life, biologists have sought to explain the underlying principles. Momentous discoveries were made in the course of many decades of research [1], but the quest to attain full understanding of cellular mechanisms and how to manipulate them to improve health, conti...

The paper proposes a new method of linear text segmentation based on lexical cohesion of a text. Namely, first a single chain of disambiguated words in a text is established, then the rips of this single chain are considered as boundaries for the segments of the cohesion text structure (Cohesion TextTiling or CTT). The summaries of arbitrarily length are obtained by extraction using three diffe...