Liver Cancer CT Image Segmentation Methods Based on Watershed Algorithm

2009 ◽  
Author(s):  
Jianhua Liu ◽  
Zhongyi Wang ◽  
Rui Zhang
Keyword(s):  
Image Segmentation ◽  
Liver Cancer ◽  
Watershed Algorithm ◽  
Ct Image ◽  
Segmentation Methods

CT image segmentation methods for bone used in medical additive manufacturing

2018 ◽  
Vol 51 ◽  
pp. 6-16 ◽  
Author(s):  
Maureen van Eijnatten ◽  
Roelof van Dijk ◽  
Johannes Dobbe ◽  
Geert Streekstra ◽  
Juha Koivisto ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Additive Manufacturing ◽  
Ct Image ◽  
Segmentation Methods

scholarly journals Segmentation of Breast Cancer Fine Needle Biopsy Cytological Images

2008 ◽  
Vol 18 (2) ◽  
pp. 159-170 ◽  
Author(s):  
Maciej Hrebień ◽  
Piotr Steć ◽  
Tomasz Nieczkowski ◽  
Andrzej Obuchowicz
Keyword(s):  
Breast Cancer ◽  
Image Segmentation ◽  
Cellular Automata ◽  
Needle Biopsy ◽  
Fine Needle Biopsy ◽  
Watershed Algorithm ◽  
Fine Needle ◽  
Segmentation Methods ◽  
Benchmark Database

Segmentation of Breast Cancer Fine Needle Biopsy Cytological ImagesThis paper describes three cytological image segmentation methods. The analysis includes the watershed algorithm, active contouring and a cellular automata GrowCut method. One can also find here a description of image pre-processing, Hough transform based pre-segmentation and an automatic nuclei localization mechanism used in our approach. Preliminary experimental results collected on a benchmark database present the quality of the methods in the analyzed issue. The discussion of common errors and possible future problems summarizes the work and points out regions that need further research.

scholarly journals Survey on liver CT image segmentation methods

2011 ◽  
Vol 37 (2) ◽  
pp. 83-95 ◽  
Author(s):  
Ahmed M. Mharib ◽  
Abdul Rahman Ramli ◽  
Syamsiah Mashohor ◽  
Rozi Binti Mahmood
Keyword(s):  
Image Segmentation ◽  
Ct Image ◽  
Liver Ct ◽  
Segmentation Methods

Effects of CT image segmentation methods on the accuracy of long bone 3D reconstructions

2011 ◽  
Vol 33 (2) ◽  
pp. 226-233 ◽  
Author(s):  
Kanchana Rathnayaka ◽  
Tony Sahama ◽  
Michael A. Schuetz ◽  
Beat Schmutz
Keyword(s):  
Image Segmentation ◽  
Long Bone ◽  
Ct Image ◽  
3D Reconstructions ◽  
Segmentation Methods

scholarly journals Comparative Study: Statistical Approach and Deep Learning Method for Automatic Segmentation Methods for Lung CT Image Segmentation

2020 ◽  
Vol 2 (4) ◽  
pp. 187-193
Author(s):  
Dr. Akey Sungheetha ◽  
Dr. Rajesh Sharma R
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Comparative Study ◽  
Performance Metrics ◽  
Automatic Segmentation ◽  
Ct Image ◽  
Learning Method ◽  
Segmentation Methods ◽  
Sensitivity Specificity ◽  
Lung Ct

Recently, deep learning technique is playing important starring role for image segmentation field in medical imaging of accurate tasks. In a critical component of diagnosis, deep learning is an organized network with homogeneous areas to provide accurate results. It is proved its superior quality with statistical model automatic segmentation methods in many critical condition environments. In this research article, we focus the improved accuracy and speed of the system process compared with conservative automatic segmentation methods. Also we compared performance metrics such as accuracy, sensitivity, specificity, precision, RMSE, Precision- Recall Curve with different algorithm in deep learning method. This comparative study covers the constructing an efficient and accurate model for Lung CT image segmentation.

Segmentation In Medical Resonance images to extract the cancerous nodule for early diagnosis on cancer

2012 ◽  
Vol 3 (2) ◽  
pp. 253-255
Author(s):  
Raman Brar
Keyword(s):  
Human Lung ◽  
Research Work ◽  
Vital Role ◽  
Watershed Algorithm ◽  
Segmentation Method ◽  
Resonance Imaging ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Background Segmentation ◽  
Lung Mri

Image segmentation plays a vital role in several medical imaging programs by assisting the delineation of physiological structures along with other parts. The objective of this research work is to segmentize human lung MRI (Medical resonance Imaging) images for early detection of cancer.Watershed Transform Technique is implemented as the Segmentation method in this work. Some comparative experiments using both directly applied watershed algorithm and after marking foreground and computed background segmentation methods show the improved lung segmentation accuracy in some image cases.

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