scholarly journals Nonrigid Registration of 2-D and 3-D Dynamic Cell Nuclei Images for Improved Classification of Subcellular Particle Motion

2011 ◽  
Vol 20 (4) ◽  
pp. 1011-1022 ◽  
Author(s):  
Il-Han Kim ◽  
Yi-Chun M. Chen ◽  
David L. Spector ◽  
Roland Eils ◽  
Karl Rohr
Keyword(s):  
Particle Motion ◽  
Nonrigid Registration ◽  
Cell Nuclei ◽  
Dynamic Cell

scholarly journals Detection and Classification of Overlapping Cell Nuclei in Cytology Effusion Images Using a Double-Strategy Random Forest

2018 ◽  
Vol 8 (9) ◽  
pp. 1608 ◽  
Author(s):  
Khin Win ◽  
Somsak Choomchuay ◽  
Kazuhiko Hamamoto ◽  
Manasanan Raveesunthornkiat
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Performance Metrics ◽  
Superior Performance ◽  
New Combination ◽  
Final Decision ◽  
Cell Detection ◽  
Cell Nuclei ◽  
Dominant Feature

Due to the close resemblance between overlapping and cancerous nuclei, the misinterpretation of overlapping nuclei can affect the final decision of cancer cell detection. Thus, it is essential to detect overlapping nuclei and distinguish them from single ones for subsequent quantitative analyses. This paper presents a method for the automated detection and classification of overlapping nuclei from single nuclei appearing in cytology pleural effusion (CPE) images. The proposed system is comprised of three steps: nuclei candidate extraction, dominant feature extraction, and classification of single and overlapping nuclei. A maximum entropy thresholding method complemented by image enhancement and post-processing was employed for nuclei candidate extraction. For feature extraction, a new combination of 16 geometrical and 10 textural features was extracted from each nucleus region. A double-strategy random forest was performed as an ensemble feature selector to select the most relevant features, and an ensemble classifier to differentiate between overlapping nuclei and single ones using selected features. The proposed method was evaluated on 4000 nuclei from CPE images using various performance metrics. The results were 96.6% sensitivity, 98.7% specificity, 92.7% precision, 94.6% F1 score, 98.4% accuracy, 97.6% G-mean, and 99% area under curve. The computation time required to run the entire algorithm was just 5.17 s. The experiment results demonstrate that the proposed algorithm yields a superior performance to previous studies and other classifiers. The proposed algorithm can serve as a new supportive tool in the automated diagnosis of cancer cells from cytology images.

Machine Learning for Gastric Cancer Detection

2020 ◽  
Vol 9 (2) ◽  
pp. 48-58
Author(s):  
Abraham Pouliakis ◽  
Periklis Foukas ◽  
Konstantinos Triantafyllou ◽  
Niki Margari ◽  
Efrossyni Karakitsou ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cancer Detection ◽  
Image Analysis System ◽  
Cell Nuclei ◽  
Training Set ◽  
Cytological Data ◽  
Morphometric Feature ◽  
Test Sets ◽  
Analysis System

The objective of this study is the investigation of the potential value of a logistic regression model for the classification of gastric cytological data. The model was based on the morphological features of cell nuclei. The aim was the discrimination of benign from malignant nuclei and subsequently patients. Cytological images of gastric smears were analyzed by an image analysis system capable to extract cell nuclear features. Measurements from 50% of the patients were selected as a training set for model creation, while the measurements from the remaining patients were used as test set to validate the results. Furthermore, a model for the classification of individual patients, based on the classification of their cell nuclei has been developed. This approach set gave a correct classification at the level of 90% on the training and test sets on the nuclear level. Concluding the application of morphometric feature selection in combination with logistic regression may offer useful and complementary information about the potential of malignancy of gastric nuclei and patient cases.

SHAPE AND TEXTURE INDEXES APPLICATION TO CELL NUCLEI CLASSIFICATION

2013 ◽  
Vol 27 (01) ◽  
pp. 1357002 ◽  
Author(s):  
GUILLAUME THIBAULT ◽  
BERNARD FERTIL ◽  
CLAIRE NAVARRO ◽  
SANDRINE PEREIRA ◽  
PIERRE CAU ◽  
...  
Keyword(s):  
Large Range ◽  
New Method ◽  
Classification Model ◽  
Classification Method ◽  
Cell Nuclei ◽  
Characterization Techniques ◽  
A Cell ◽  
Texture Characterization

This paper describes the sequence of construction of a cell nuclei classification model by the analysis, the characterization and the classification of shape and texture. We describe first the elaboration of dedicated shape indexes and second the construction of the associated classification submodel. Then we present a new method of texture characterization, based on the construction and the analysis of statistical matrices encoding the texture. The various characterization techniques developed in this paper are systematically compared to previous approaches. In particular, we paid special attention to the results obtained by a versatile classification method using a large range of descriptors dedicated to the characterization of shapes and textures. Finally, the last classifier built with our methods achieved 88% of classification out of the 94% possible.

Feature-based analysis of cell nuclei structure for classification of histopathological images

2018 ◽  
Vol 78 ◽  
pp. 152-162 ◽  
Author(s):  
Pin Wang ◽  
Sha Xu ◽  
Yongming Li ◽  
Lirui Wang ◽  
Qi Song
Keyword(s):  
Cell Nuclei ◽  
Histopathological Images ◽  
Feature Based
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