Automatic segmentation of fiducial marks using attribute-based mathematical morphology

2001 ◽  
Vol 10 (2) ◽  
pp. 560 ◽  
Author(s):  
Xiaoliang Wu
Keyword(s):  
Mathematical Morphology ◽  
Automatic Segmentation

Automatic segmentation of newborn brain MRI using mathematical morphology

2011 ◽  
Author(s):  
Laura Gui ◽  
Radoslaw Lisowski ◽  
Tamara Faundez ◽  
Petra S. Huppi ◽  
Francois Lazeyras ◽  
...  
Keyword(s):  
Mathematical Morphology ◽  
Automatic Segmentation ◽  
Brain Mri ◽  
Newborn Brain

Mathematical Morphology-Based Automatic Restoration and Segmentation for Degraded Machine -Printed Character Images

2006 ◽  
pp. 318-339
Author(s):  
Shigueo Nomura ◽  
Keiji Yamanaka ◽  
Osamu Katai ◽  
Hiroshi Kawakammi ◽  
Takayuki Shiose
Keyword(s):  
Statistical Analysis ◽  
Mathematical Morphology ◽  
Feature Vector ◽  
Automatic Segmentation ◽  
Binary Image ◽  
Cost Calculation ◽  
Vertical Projection ◽  
Thinning Algorithm ◽  
Binary Images ◽  
The Third

This chapter presents a morphological approach (AutoS) for automatic segmentation with feature vector extraction of seriously degraded machine-printed character images. This approach consists of four modules. The first detects and segments natural pitch characters based on the vertical projection of their binary images. The second uses an algorithm based on the vertical projection and statistical analysis of coordinates to detect fragments in broken characters and merge them before the eventual segmentation. The third employs a morphological thickening algorithm on the binary image to locate the separating boundaries of overlapping characters. Finally, the fourth executes a morphological thinning algorithm and a segmentation cost calculation to determine the most appropriate coordinate at the image for dividing touching characters. By the automatic running of a suitable segmentation module for each problem, the AutoS approach has been robust, flexible and effective in obtaining useful and accurate feature vectors concerning degraded machine-printed character images.

Prediction of lymph node metastase in NSCLC

2010 ◽  
Vol 01 (05) ◽  
pp. 219-226 ◽  
Author(s):  
F. Beyer ◽  
B. Buerke ◽  
J. Gerss ◽  
K. Scheffe ◽  
M. Puesken ◽  
...  
Keyword(s):  
Lymph Nodes ◽  
Fdg Pet ◽  
Automatic Segmentation ◽  
Correlation Coefficients ◽  
Mediastinal Lymph Nodes ◽  
Intra Class Correlation ◽  
Axial Diameter ◽  
Pet Ct ◽  
Fdg Pet Ct

SummaryPurpose: To distinguish between benign and malignant mediastinal lymph nodes in patients with NSCLC by comparing 2D and semiautomated 3D measurements in FDG-PET-CT.Patients, material, methods: FDG-PET-CT was performed in 46 patients prior to therapy. 299 mediastinal lymph-nodes were evaluated independently by two radiologists, both manually and by semi-automatic segmentation software. Longest-axial-diameter (LAD), shortest-axial-diameter (SAD), maximal-3D-diameter, elongation and volume were obtained. FDG-PET-CT and clinical/FDG-PET-CT follow up examinations and/or histology served as the reference standard. Statistical analysis encompassed intra-class-correlation-coefficients and receiver-operator-characteristics-curves (ROC). Results: The standard of reference revealed involvement in 87 (29%) of 299 lymph nodes. Manually and semi-automatically measured 2D parameters (LAD and SAD) showed a good correlation with mean

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