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Image classification of human carcinoma cells using complex wavelet-based covariance descriptors
- Source :
- PLoS ONE, Vol 8, Iss 1, p e52807 (2013), PLoS ONE
- Publication Year :
- 2013
- Publisher :
- Public Library of Science (PLoS), 2013.
-
Abstract
- Cancer cell lines are widely used for research purposes in laboratories all over the world. Computer-assisted classification of cancer cells can alleviate the burden of manual labeling and help cancer research. In this paper, we present a novel computerized method for cancer cell line image classification. The aim is to automatically classify 14 different classes of cell lines including 7 classes of breast and 7 classes of liver cancer cells. Microscopic images containing irregular carcinoma cell patterns are represented by subwindows which correspond to foreground pixels. For each subwindow, a covariance descriptor utilizing the dual-tree complex wavelet transform (DT-[Formula: see text]WT) coefficients and several morphological attributes are computed. Directionally selective DT-[Formula: see text]WT feature parameters are preferred primarily because of their ability to characterize edges at multiple orientations which is the characteristic feature of carcinoma cell line images. A Support Vector Machine (SVM) classifier with radial basis function (RBF) kernel is employed for final classification. Over a dataset of 840 images, we achieve an accuracy above 98%, which outperforms the classical covariance-based methods. The proposed system can be used as a reliable decision maker for laboratory studies. Our tool provides an automated, time- and cost-efficient analysis of cancer cell morphology to classify different cancer cell lines using image-processing techniques, which can be used as an alternative to the costly short tandem repeat (STR) analysis. The data set used in this manuscript is available as supplementary material through http://signal.ee.bilkent.edu.tr/cancerCellLineClassificationSampleImages.html.
- Subjects :
- Support Vector Machine
Transform
02 engineering and technology
Bioinformatics
Biochemistry
Computer Applications
Imaging
Wavelet
Segmentation
Engineering
Molecular Cell Biology
0202 electrical engineering, electronic engineering, information engineering
Image Processing, Computer-Assisted
Statistical Signal Processing
Computer Engineering
Complex wavelet transform
Features
Multidisciplinary
Tumor
Contextual image classification
wavelet analysis
article
Wavelet transform
Obstetrics and Gynecology
Covariance
carcinoma cell
Cellular Structures
Oncology
covariance
Medicine
020201 artificial intelligence & image processing
Cellular Types
Three-dimensional
Electrical Engineering
Research Article
Computer Modeling
short tandem repeat
Science
Wavelet Analysis
Image processing
Biology
decision making
Cell Line
liver cancer
breast cancer
Imaging, Three-Dimensional
Cell Line, Tumor
Support Vector Machines
Breast Cancer
Cancer Detection and Diagnosis
Humans
Computer-assisted
support vector machine
human
reliability
business.industry
cost effectiveness analysis
human cell
Carcinoma
020206 networking & telecommunications
Pattern recognition
image processing
Support vector machine
ComputingMethodologies_PATTERNRECOGNITION
Computer Science
Signal Processing
Artificial intelligence
business
Classifier (UML)
cell structure
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 8
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....f9e088b4a7505b61f9e8e1877fbde158