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A patch-based tensor decomposition algorithm for M-FISH image classification.

Min Wang^1,2, Ting-Zhu Huang¹, Jingyao Li²

¹School of Mathematical Sciences/Research Center for Image and Vision Computing, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology

|May 5, 2016

Summary

This summary is machine-generated.

This study introduces a new patch-based classification algorithm for multiplex-fluorescence in situ hybridization (M-FISH) images. The method improves the detection of chromosomal abnormalities, leading to more accurate diagnoses of genetic diseases and cancers.

Keywords:

HOSVD M-FISH chromosome image classification cytogenetics image segmentation tensor decomposition

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Area of Science:

Genetics
Bioinformatics
Medical Imaging

Background:

Multiplex-fluorescence in situ hybridization (M-FISH) is crucial for detecting chromosomal abnormalities.
Accurate chromosome classification is essential for M-FISH analysis.
Existing methods often overlook neighborhood pixel information.

Purpose of the Study:

To develop a novel patch-based classification algorithm for M-FISH images.
To leverage neighborhood pixel correlations and spectral channel information for improved classification.
To enhance the accuracy of chromosomal abnormality detection.

Main Methods:

Proposed a patch-based classification algorithm utilizing higher order singular value decomposition (HOSVD).
Assumed pixels within a patch share the same class for representation.
Tested the algorithm on a comprehensive M-FISH database.

Main Results:

The developed method demonstrated improved performance in M-FISH image classification.
Achieved the highest correct classification ratio (CCR) compared to existing methods (FCM, AFCM, IAFCM, SparseRC).
The algorithm effectively utilizes neighborhood pixel correlations and spectral information.

Conclusions:

The proposed patch-based HOSVD algorithm offers superior performance for M-FISH chromosome classification.
This advancement can lead to improved diagnosis of genetic diseases and cancers.
The method addresses limitations of previous pixel-wise classification approaches.