Automated Segmentation of Skin Lesions Using Seed Points and Scale-Invariant Semantic Mathematic Model

2015 ◽  
pp. 219-227
Author(s):  
Z. Faizal Khan
Keyword(s):  
Mathematic Model ◽  
Skin Lesions ◽  
Automated Segmentation ◽  
Scale Invariant ◽  
Seed Points

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

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

Fine Structural Alterations in Thyroid Follicular Cells (FC) and Changes in Serum Thyroxine Produced by Feeding Polychlorinated Biphenyl (PCB) to Rats

1977 ◽  
Vol 35 ◽  
pp. 498-499
Author(s):  
W.T. Collins ◽  
Charles C. Capen ◽  
Louis Kasza
Keyword(s):  
Feed Efficiency ◽  
Polychlorinated Biphenyl ◽  
Serum Proteins ◽  
Skin Lesions ◽  
Hepatic Necrosis ◽  
Ultrastructural Changes ◽  
Lymphoid Tissues ◽  
Heat Transfer Fluids ◽  
Thyroid Follicular Cells ◽  
Peripheral Metabolism

The widespread contamination of the environment with PCB, a compound used extensively by industry in hydraulic and heat transfer fluids as well as plasticizers and solvents in adhesives and sealants, has resulted in detectable tissue levels in a large portion of the human population, domestic animals, and wildlife. Intoxication with PCB produces severe hepatic necrosis, degeneration of lymphoid tissues and kidney, skin lesions, decreased reproductive performance, reduced feed efficiency, and decreased weight gain. PCB also has been reported to reduce the binding of thyroid hormone to serum proteins and enhance the peripheral metabolism of thyroxine with increased excretion of thyroxine-glucuronide in the bile (Bastomsky, Endocrinology 95: 1150-1155, 1974).The objectives of this investigation were (1) to investigate the histopathologic, histochemical, and ultrastructural changes in thyroid FC produced by the acute (4 week) and chronic (12 week) administration of low (50 ppm) and high (500 ppm) doses of PCB to rats, (2) to correlate these alterations to changes in serum immunoreactive thyroxine concentration, and (3) to investigate the persistence of the effects of PCB on the thyroid gland.

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