scholarly journals Fuzzy C Mean Thresholding based Level Set for Automated Segmentation of Skin Lesions

2013 ◽  
Vol 04 (03) ◽  
pp. 66-71 ◽  
Author(s):  
Ammara Masood ◽  
Adel Ali Al-Jumaily
Keyword(s):  
Level Set ◽  
Skin Lesions ◽  
Automated Segmentation

Automated segmentation of skin lesions: Modified Fuzzy C mean thresholding based level set method

INMIC ◽  
2013 ◽  
Author(s):  
Ammara Masood ◽  
Adel Ali Al Jumaily ◽  
Azadeh Noori Hoshyar ◽  
Omama Masood
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Skin Lesions ◽  
Automated Segmentation

Poster - Thur Eve - 05: Semi-Automated Segmentation of Lung Tumours on CT Scans Using Level Set Sparse Field Active Model

2010 ◽  
Vol 37 (7Part2) ◽  
pp. 3887-3887
Author(s):  
J Awad ◽  
L Wilson ◽  
G Parraga ◽  
A Fenster
Keyword(s):  
Level Set ◽  
Ct Scans ◽  
Automated Segmentation ◽  
Lung Tumours ◽  
Active Model

scholarly journals Automated segmentation of intraretinal cystoid macular edema based on Gaussian mixture model

2019 ◽  
Vol 13 (01) ◽  
pp. 1950020
Author(s):  
Jinghong Wu ◽  
Sijie Niu ◽  
Qiang Chen ◽  
Wen Fan ◽  
Songtao Yuan ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Level Set ◽  
Spatial Information ◽  
High Sensitivity ◽  
Gaussian Mixture ◽  
Automated Segmentation ◽  
Potential Fluid ◽  
Sd Oct

We introduce a method based on Gaussian mixture model (GMM) clustering and level-set to automatically detect intraretina fluid on diabetic retinopathy (DR) from spectral domain optical coherence tomography (SD-OCT) images in this paper. First, each B-scan is segmented using GMM clustering. The original clustering results are refined using location and thickness information. Then, the spatial information among every consecutive five B-scans is used to search potential fluid. Finally, the improved level-set method is used to obtain the accurate boundaries. The high sensitivity and accuracy demonstrated here show its potential for detection of fluid.

scholarly journals Automated Segmentation of Abnormal Tissues in Medical Images

2019 ◽  
Author(s):  
H Khastavaneh ◽  
H Ebrahimpour-komleh
Keyword(s):  
Diagnostic Procedures ◽  
Skin Lesions ◽  
Tissue Type ◽  
Automated Segmentation ◽  
Tissue Segmentation ◽  
Learning Techniques ◽  
Segmentation Methods ◽  
Specific Tissue ◽  
Better Than ◽  
Abnormal Tissue

Nowadays, medical image modalities are almost available everywhere. These modalities are bases of diagnosis of various diseases sensitive to specific tissue type. Usually physicians look for abnormalities in these modalities in diagnostic procedures. Count and volume of abnormalities are very important for optimal treatment of patients. Segmentation is a preliminary step for these measurements and also further analysis. Manual segmentation of abnormalities is cumbersome, error prone, and subjective. As a result, automated segmentation of abnormal tissue is a need. In this study, representative techniques for segmentation of abnormal tissues are reviewed. Main focus is on the segmentation of multiple sclerosis lesions, breast cancer masses, lung nodules, and skin lesions. As experimental results demonstrate, the methods based on deep learning techniques perform better than other methods that are usually based on handy feature engineering techniques. Finally, the most common measures to evaluate automated abnormal tissue segmentation methods are reported.

Automated segmentation of pigmented skin lesions in multispectral imaging

2005 ◽  
Vol 50 (22) ◽  
pp. N345-N357 ◽  
Author(s):  
Mauro Carrara ◽  
Stefano Tomatis ◽  
Aldo Bono ◽  
Cesare Bartoli ◽  
Daniele Moglia ◽  
...  
Keyword(s):  
Multispectral Imaging ◽  
Skin Lesions ◽  
Automated Segmentation ◽  
Pigmented Skin Lesions
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