Name
Title | ||
|---|---|---|
Feature normalization via expectation maximization and unsupervised nonparametric classification for M-FISH chromosome images. |
Abstract | ||
|---|---|---|
Multicolor fluorescence in situ hybridization (M-FISH) techniques provide color karyotyping that allows simultaneous analysis of numerical and structural abnormalities of whole human chromosomes. Chromosomes are stained combinatorially in M-FISH. By analyzing the intensity combinations of each pixel, all chromosome pixels in an image are classified. Often, the intensity distributions between different images are found to be considerably different and the difference becomes the source of misclassifications of the pixels. Improved pixel classification accuracy is the most important task to ensure the success of the M-FISH technique. In this paper, we introduce a new feature normalization method for M-FISH images that reduces the difference in the feature distributions among different images using the expectation maximization (EM) algorithm. We also introduce a new unsupervised, nonparametric classification method for M-FISH images. The performance of the classifier is as accurate as the maximum-likelihood classifier, whose accuracy also significantly improved after the EM normalization. We would expect that any classifier will likely produce an improved classification accuracy following the EM normalization. Since the developed classification method does not require training data, it is highly convenient when ground truth does not exist. A significant improvement was achieved on the pixel classification accuracy after the new feature normalization. Indeed, the overall pixel classification accuracy improved by 20% after EM normalization. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/TMI.2008.918320 | IEEE Trans. Med. Imaging |
Keywords | Field | DocType |
pixel classification accuracy,expectation-maximisation algorithm,cellular biophysics,m-fish,feature normalization method,biomedical optical imaging,normalization,color karyotyping,unsupervised nonparametric classification,expectation maximization,maximum-likelihood,fluorescence,chromosome,nonparametric statistics,multicolor fluorescence in situ hybridization (m-fish),image classification,expectation maximization algorithm,m-fish human chromosome images,intensity distributions,maximum-likelihood classifier,classification,multicolor fluorescence in situ hybridization techniques,expectation maximization (em),unsupervised,medical image processing,image colour analysis,em algorithm,ground truth,maximum likelihood | Computer vision,Normalization (statistics),Nonparametric classification,Pattern recognition,Expectation–maximization algorithm,Nonparametric statistics,Ground truth,Pixel,Artificial intelligence,Contextual image classification,Classifier (linguistics),Mathematics | Journal |
Volume | Issue | ISSN |
27 | 8 | 1558-254X |
Citations | PageRank | References |
8 | 0.66 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hyohoon Choi | 1 | 59 | 5.37 |
| Alan C. Bovik | 2 | 5062 | 349.55 |
| Kenneth R. Castleman | 3 | 91 | 12.80 |