| Summary
|
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. Directionally selective DT-CWT
feature parameters are used 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. The proposed system can be used as a reliable
decision maker for laboratory studies. This website gives interested
scientists the opportunity to test our algorithm on their data. |
| Input
|
Our
software takes a
JPG color image of the
following 14 classes as input:
Breast cancer cell lines:
- BT-20
- Cama-1
- MDA-MB-157
- MDA-MB-361
- MDA-MB-453
- MDA-MB-468
- T47D
Liver cancer cell lines:
- Focus
- Hep40
- HepG2
- Huh7
- mv
- PLC
- SkHep1
Image sizes should be equal to 3096-by-4140 or larger. The
magnification factor of the image can be chosen as 10x, 20x or
40x,
respectively. The cancer type (breast or liver) can be
given as additional information, if
known.
|
| Output
|
Our software
displays the estimated class of the uploaded image and the confluency
level. |
| Sample
|
Example
Images and
datasets
|
| Reference
|
"Image
Classification
of Human Carcinoma Cells Using Complex Wavelet-Based Covariance
Descriptors" by Furkan Keskin,
Alexander Suhre, Kivanc Kose, Tulin Ersahin, Rengul Cetin-Atalay and A.
Enis Cetin. |
| Contact
|
Enis
Cetin: 
Rengul Cetin-Atalay: 
|
