Semi-automatic three-dimensional vessel segmentation using a connected component localization of the Region-Scalable Fitting Energy

2015 ◽  
Author(s):  
Marco Fedele ◽  
Elena Faggiano ◽  
Luca Barbarotta ◽  
Francesco Cremonesi ◽  
Luca Formaggia ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Vessel Segmentation ◽  
Connected Component

Automatic Pharynx Segmentation from MRI Data for Analysis of Sleep Related Disorders

2015 ◽  
Vol 24 (04) ◽  
pp. 1550018 ◽  
Author(s):  
Tatyana Ivanovska ◽  
René Laqua ◽  
Muhammad Laiq Shahid ◽  
Lars Linsen ◽  
Katrin Hegenscheid ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Automatic Extraction ◽  
Data Sets ◽  
Connected Component ◽  
Resonance Imaging ◽  
The Third ◽  
General Rules ◽  
Magnetic Resonance Imaging Mri ◽  
Edge Based ◽  
2D And 3D

In our project, we analyse throat structures using magnetic resonance imaging (MRI) to associate anatomic risk factors with sleep related disorders. Pharynx segmentation is the first step in the three-dimensional analysis of throat tissues.We present a pipeline for automatic pharynx segmentation. The automatic part of the approach consists of three steps: smoothing, thresholding, 2D and 3D connected component analysis. Whereas two first steps are rather common, the third step provides a set of general rules for the automatic extraction of the pharyngeal component. Our method requires less than one minute to extract the pharyngeal structures. The approach is evaluated quantitatively on 30 data sets using region-based and edge-based measures.

Three-dimensional vessel segmentation using a novel combinatory filter framework

2014 ◽  
Vol 59 (22) ◽  
pp. 7013-7029 ◽  
Author(s):  
Y Ding ◽  
W O C Ward ◽  
T Wästerlid ◽  
P A Gowland ◽  
A M Peters ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Vessel Segmentation

scholarly journals Three-Dimensional Vessel Segmentation in Whole-Tissue and Whole-Block Imaging Using a Deep Neural Network

2020 ◽  
Author(s):  
Takashi Ohnishi ◽  
Alexei Teplov ◽  
Noboru Kawata ◽  
Kareem Ibrahim ◽  
Peter Ntiamoah ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Three Dimensional ◽  
Vessel Segmentation

Three-Dimensional Blood Vessel Segmentation and Centerline Extraction based on Two-Dimensional Cross-Section Analysis

2014 ◽  
Vol 43 (5) ◽  
pp. 1223-1234 ◽  
Author(s):  
Rahul Prasanna Kumar ◽  
Fritz Albregtsen ◽  
Martin Reimers ◽  
Bjørn Edwin ◽  
Thomas Langø ◽  
...  
Keyword(s):  
Blood Vessel ◽  
Cross Section ◽  
Three Dimensional ◽  
Vessel Segmentation ◽  
Two Dimensional ◽  
Blood Vessel Segmentation ◽  
Centerline Extraction ◽  
Section Analysis

scholarly journals An Improved Fuzzy Connectedness Method for Automatic Three-Dimensional Liver Vessel Segmentation in CT Images

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Rui Zhang ◽  
Zhuhuang Zhou ◽  
Weiwei Wu ◽  
Chung-Chih Lin ◽  
Po-Hsiang Tsui ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Region Growing ◽  
Threshold Level ◽  
Ct Images ◽  
Vessel Segmentation ◽  
Fuzzy Connectedness ◽  
Segmentation Strategy ◽  
Volumetric Images ◽  
Vesselness Filter ◽  
Sensitivity Specificity

In this paper, an improved fuzzy connectedness (FC) method was proposed for automatic three-dimensional (3D) liver vessel segmentation in computed tomography (CT) images. The vessel-enhanced image (i.e., vesselness image) was incorporated into the fuzzy affinity function of FC, rather than the intensity image used by traditional FC. An improved vesselness filter was proposed by incorporating adaptive sigmoid filtering and a background-suppressing item. The fuzzy scene of FC was automatically initialized by using the Otsu segmentation algorithm and one single seed generated adaptively, while traditional FC required multiple seeds. The improved FC method was evaluated on 40 cases of clinical CT volumetric images from the 3Dircadb (n=20) and Sliver07 (n=20) datasets. Experimental results showed that the proposed liver vessel segmentation strategy could achieve better segmentation performance than traditional FC, region growing, and threshold level set. Average accuracy, sensitivity, specificity, and Dice coefficient of the improved FC method were, respectively, (96.4 ± 1.1)%, (73.7 ± 7.6)%, (97.4 ± 1.3)%, and (67.3 ± 5.7)% for the 3Dircadb dataset and (96.8 ± 0.6)%, (89.1 ± 6.8)%, (97.6 ± 1.1)%, and (71.4 ± 7.6)% for the Sliver07 dataset. It was concluded that the improved FC may be used as a new method for automatic 3D segmentation of liver vessel from CT images.

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