scholarly journals Automated Segmentation and Morphometry of Cell and Tissue Structures. Selected Algorithms in ImageJ

10.5772/36729 ◽  
2012 ◽  
Author(s):  
Dimiter Prodanov ◽  
Kris Verstreke
Keyword(s):  
Automated Segmentation ◽  
Tissue Structures

Automated segmentation of tissue structures in optical coherence tomography data

2009 ◽  
Vol 14 (3) ◽  
pp. 034046 ◽  
Author(s):  
Fernando Gasca ◽  
Lukas Ramrath ◽  
Gereon Huettmann ◽  
Achim Schweikard
Keyword(s):  
Optical Coherence Tomography ◽  
Automated Segmentation ◽  
Optical Coherence ◽  
Tissue Structures ◽  
Tomography Data

Pattern Recognition in Histo-Pathology: Basic Considerations

1982 ◽  
Vol 21 (01) ◽  
pp. 15-22 ◽  
Author(s):  
W. Schlegel ◽  
K. Kayser
Keyword(s):  
Pattern Recognition ◽  
Graph Theory ◽  
Normal Tissue ◽  
Diagnostic Procedures ◽  
Minimal Distance ◽  
Malignant Growth ◽  
Colon Tissue ◽  
First Results ◽  
Tissue Structures ◽  
The Given

A basic concept for the automatic diagnosis of histo-pathological specimen is presented. The algorithm is based on tissue structures of the original organ. Low power magnification was used to inspect the specimens. The form of the given tissue structures, e. g. diameter, distance, shape factor and number of neighbours, is measured. Graph theory is applied by using the center of structures as vertices and the shortest connection of neighbours as edges. The algorithm leads to two independent sets of parameters which can be used for diagnostic procedures. First results with colon tissue show significant differences between normal tissue, benign and malignant growth. Polyps form glands that are twice as wide as normal and carcinomatous tissue. Carcinomas can be separated by the minimal distance of the glands formed. First results of pattern recognition using graph theory are discussed.

Influence of ultrasonication on functional components and tissue structures of seed coat powder of adzuki beans

2005 ◽  
Vol 16 (3) ◽  
pp. 249-254
Author(s):  
Tomoko Kimura ◽  
Mieko Kagaya ◽  
Michitaka Naitou ◽  
Hiroko Sasaki ◽  
Tatsuyuki Sugahara
Keyword(s):  
Seed Coat ◽  
Tissue Structures ◽  
Functional Components

Morphology of NFA Organic Photovoltaic Blends by Automated Segmentation of Spatially Resolved Electron Spectra

2019 ◽  
Author(s):  
Jochen Kammerer ◽  
Rasum R. Schröder ◽  
Pavlo Perkhun ◽  
Olivier Margeat ◽  
Wolfgang Köntges ◽  
...  
Keyword(s):  
Organic Photovoltaic ◽  
Automated Segmentation ◽  
Electron Spectra ◽  
Spatially Resolved

scholarly journals Automated Segmentation of Infarct Lesions in T1-Weighted MRI Scans Using Variational Mode Decomposition and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1952
Author(s):  
May Phu Paing ◽  
Supan Tungjitkusolmun ◽  
Toan Huy Bui ◽  
Sarinporn Visitsattapongse ◽  
Chuchart Pintavirooj
Keyword(s):  
Deep Learning ◽  
Brain Infarction ◽  
Three Dimensional ◽  
Dice Similarity Coefficient ◽  
Automated Segmentation ◽  
Variational Mode Decomposition ◽  
Brain Scans ◽  
Automated Method ◽  
Mode Decomposition ◽  
Segmentation Task

Automated segmentation methods are critical for early detection, prompt actions, and immediate treatments in reducing disability and death risks of brain infarction. This paper aims to develop a fully automated method to segment the infarct lesions from T1-weighted brain scans. As a key novelty, the proposed method combines variational mode decomposition and deep learning-based segmentation to take advantages of both methods and provide better results. There are three main technical contributions in this paper. First, variational mode decomposition is applied as a pre-processing to discriminate the infarct lesions from unwanted non-infarct tissues. Second, overlapped patches strategy is proposed to reduce the workload of the deep-learning-based segmentation task. Finally, a three-dimensional U-Net model is developed to perform patch-wise segmentation of infarct lesions. A total of 239 brain scans from a public dataset is utilized to develop and evaluate the proposed method. Empirical results reveal that the proposed automated segmentation can provide promising performances with an average dice similarity coefficient (DSC) of 0.6684, intersection over union (IoU) of 0.5022, and average symmetric surface distance (ASSD) of 0.3932, respectively.

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