نتایج جستجو برای: region growing process

تعداد نتایج: 1951417  

2002
Nathalie Richard Michel Dojat Catherine Garbay

To cope with the difficulty of 3D MRI brain scans segmentation, specification and instantiation of a priori models should be constrained by local images characteristics. We introduce situated cooperative agents for the extraction of domain and control knowledge from image grey levels. Their dedicated behaviours, i.e segmentation of one type of tissue, are dynamically adapted function of their p...

2015
Sumanta Guha

We develop an algorithm to segment a 3D surface mesh intovisually meaningful regions based on analyzing the local geometry ofvertices. We begin with a novel characterization of mesh vertices as convex,concave or hyperbolic depending upon their discrete local geometry.Hyperbolic and concave vertices are considered potential feature regionboundaries. We propose a new region growing technique star...

2006
Matei Mancas Bernard Gosselin Benoit M. Macq

Our research deals with a semi-automatic region-growing segmentation technique. This method only needs one seed inside the region of interest (ROI). We applied it for spinal cord segmentation but it also shows results for parotid glands or even tumors. Moreover, it seems to be a general segmentation method as it could be applied in other computer vision domains then medical imaging. We use both...

2003
Kesheng Wu Wendy S. Koegler Jacqueline Chen Arie Shoshani

Many scientific applications generate large spatiotemporal datasets. A common way of exploring these datasets is to identify and track regions of interest. Usually these regions are defined as contiguous sets of points whose attributes satisfy some user defined conditions, e.g. high temperature regions in a combustion simulation. At each time step, the regions of interest may be identified by f...

2011
Michèle Péporté Dana Elena Ilea Eilish Twomey Paul F. Whelan

This paper describes a novel automatic skull-stripping method for premature infant data. A skull-stripping approach involves the removal of non-brain tissue from medical brain images. The new method reduces the image artefacts, generates binary masks and multiple thresholds, and extracts the region of interest. To define the outer boundary of the brain tissue, a binary mask is generated using m...

Journal: :J. Electronic Imaging 2003
S. Kubilay Pakin Roger S. Gaborski Lori L. Barski David H. Foos Kevin J. Parker

We present an algorithm for segmentation of computed radiography (CR) images of extremities into bone and soft tissue regions. The algorithm is region-based in which the regions are constructed using a region-growing procedure based on two different statistical tests. Following the region-growing process, a tissue classification method is employed. The purpose of the classification is to label ...

2013
Huiyan Jiang Baochun He Di Fang Fang Fang Zhiyuan Ma Benqiang Yang Libo Zhang

We propose a region growing vessel segmentation algorithm based on spectrum information. First, the algorithm does Fourier transform on the region of interest containing vascular structures to obtain its spectrum information, according to which its primary feature direction will be extracted. Then combined edge information with primary feature direction computes the vascular structure's center ...

2014
Assaf B. Spanier Leo Joskowicz

We describe a new method for the automatic segmentation of multiple organs of the ventral cavity in CT scans. The method is based on a set of rules that determine the order in which the organs are isolated and segmented, from the simplest one to the most difficult one. First, the body is isolated from the background. Second, the trachea and the left and right lungs are segmented based on their ...

2013
Daekeun You Matthew S. Simpson Sameer K. Antani Dina Demner-Fushman George R. Thoma

Pointers (arrows and symbols) are frequently used in biomedical images to highlight specific image regions of interest (ROIs) that are mentioned in figure captions and/or text discussion. Detection of pointers is the first step toward extracting relevant visual features from ROIs and combining them with textual descriptions for a multimodal (text and image) biomedical article retrieval system. ...

Journal: :CoRR 2014
Mohammed M. Abdelsamea

— a good segmentation result depends on a set of " correct " choice for the seeds. When the input images are noisy, the seeds may fall on atypical pixels that are not representative of the region statistics. This can lead to erroneous segmentation results. In this paper, an automatic seeded region growing algorithm is proposed for cellular image segmentation. First, the regions of interest (ROI...

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