Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Fast 3D reconstruction from medical image series based on thresholding method Eşikleme metodunu kullanarak medikal görüntü serisinden hizli 3 boyutlu model oluşturma
Date
2010-07-15
Author
Öz, Sinan
Serinağaoğlu Doğrusöz, Yeşim
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
192
views
0
downloads
Cite This
Many practical applications in the field of medical image processing need valid, reliable and fast image segmentation. In this study, we propose a semi-automatic segmentation approach. In this approach, an extended version of the Otsu's method for three level thresholding and a recursive connected component algorithm are combined. The segmentation process is accomplished using Extended Otsu's method and labeling in each consecutive slice. Extended Otsu's method is a thresholding method selecting two threshold values that maximizes the between-class variances, essentially by this way within-class variances are also minimized. Since information on pixel positions does not affect the outcome of Extended Otsu's method, we need to perform some processing after labeling. This processing comprises area filtering and searching the regions of interest. The proposed approach is applied to the consecutive slices. For this reason, the 3D segmentation is successfully achieved from 2D medical images. The approach not only is efficient and reliable but also requires very limited user intervention.
Subject Keywords
Image reconstruction
,
Biomedical imaging
,
Image segmentation
,
Labeling
,
Biomedical image processing
,
Filtering
,
Histograms
URI
https://hdl.handle.net/11511/48351
DOI
https://doi.org/10.1109/biyomut.2010.5479753
Collections
Department of Electrical and Electronics Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
Implement of three segmentation algorithms for CT images of torso
Öz, Sinan; Serinağaoğlu Doğrusöz, Yeşim; Department of Electrical and Electronics Engineering (2011)
Many practical applications in the field of medical image processing require valid and reliable segmentation of images. In this dissertation, we propose three different semi-automatic segmentation frameworks for 2D-upper torso medical images to construct 3D geometric model of the torso structures. In the first framework, an extended version of the Otsu’s method for three level thresholding and a recursive connected component algorithm are combined. The segmentation process is accomplished by first using Ext...
A Fast shape detection approach by directional integrations
Okman, Osman Erman; Akar, Gözde; Department of Electrical and Electronics Engineering (2013)
Detection and identification of objects from aerial images are important problems for various types of application areas. For many of the man-made structures shape is a fundamental feature by which these objects are separated from the background and other structures. In this thesis, a novel geometric shape detection algorithm based on the spatial properties of structures is proposed. Since the objects are transformed into 1-D vectors by evaluating directional integrals and detections occur by the analysis o...
Improving edge detection using ıntersection consistency
Çiftçi, Serdar; Yarman Vural, Fatoş Tunay; Kalkan, Sinan; Department of Computer Engineering (2011)
Edge detection is an important step in computer vision since edges are utilized by the successor visual processing stages including many tasks such as motion estimation, stereopsis, shape representation and matching, etc. In this study, we test whether a local consistency measure based on image orientation (which we call Intersection Consistency - IC), which was previously shown to improve detection of junctions, can be used for improving the quality of edge detection of seven different detectors; namely, C...
Automatic building extraction from high resolution satellite images for map updating: A model based approach
San, D. Koc; TÜRKER, MUSTAFA (2007-10-12)
An approach was developed for automatically updating the buildings of an existing vector database from high resolution satellite images using spectral image classification, Digital Elevation Models (DEM) and the model-based extraction techniques. First, the areas that contain buildings are detected using spectral image classification and the normalized Digital Surface Model (nDSM). The classified output provides the shapes and the approximate locations of the buildings. However, those buildings that have si...
A Robust quality metric for image super resolution /
Kipman, Yiğit; Akar, Gözde; Department of Electrical and Electronics Engineering (2015)
Superresolution have become an active topic in image processing in the last decade. Various superresolution algorithms have been developed; however these superresolution algorithms may introduce defects such as blurring, aliasing, added noise and ringing. Evaluating the performance of these superresolution algorithms is an important problem; because the original high resolution image is not available while quantifying the quality of superresolution image. Subjective tests can be made to quantify the perceiv...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
S. Öz and Y. Serinağaoğlu Doğrusöz, “Fast 3D reconstruction from medical image series based on thresholding method Eşikleme metodunu kullanarak medikal görüntü serisinden hizli 3 boyutlu model oluşturma,” 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/48351.