Segmentation and Fuzzy-Logic Classification of M-FISH Chromosome Images

Choi, Hyun-Jun; Castleman, Kenneth R.; Bovik, Alan C.

doi:10.1109/icip.2006.312360

“…Then chromosome pixels are classified using a fuzzylogic classifier [6]. Given a cluster, the landmarks on the boundary and skeleton are computed as shown in Fig 3, We have tested our algorithm on 12 images from ADIR's M-FISH image database.…”

Section: Resultsmentioning

confidence: 99%

“…After the chromosome segmentation, only the chromosome pixels are classified using an unsupervised classification method called fuzzy-logic classifier [6].…”

Section: Ii-a Foreground-background Segmentation and Pixel Classificmentioning

confidence: 99%

Maximum-Likelihood Decomposition of Overlapping and Touching M-Fish Chromosomes Using Geometry, Size and Color Information

Choi

¹

,

Bovik

²

,

Castleman³

2006

2006 International Conference of the IEEE Engineering in Medicine and Biology Society

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Since the birth of chromosome analysis by the aid of computers, building a fully automated chromosome analysis system has been the ultimate goal. Along with many other challenges, automating chromosome classification and segmentation has been one of the major challenges especially due to overlapping and touching chromosomes. In this paper we present a novel decomposition method for overlapping and touching chromosomes in M-FISH images. To overcome the limited success of previous decomposition methods that use partial information about a chromosome cluster, we have incorporated more knowledge about the clusters into a maximum-likelihood frame work. The proposed method evaluates multiple hypotheses based on geometric information, pixel classification results, and chromosome sizes, and a hypothesis that has a maximum-likelihood is chosen as the best decomposition of a given cluster. About 90% of accuracy was obtained for two or three chromosome clusters, which consist about 95% of all clusters with two or more chromosomes.

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“…Then chromosome pixels are classified using a fuzzylogic classifier [6]. Given a cluster, the landmarks on the boundary and skeleton are computed as shown in Fig 3, and the cluster is decomposed into multiple hypotheses and the likelihood of each hypothesis is computed by eq.…”

Section: Resultsmentioning

confidence: 99%

Maximum-Likelihood Decomposition of Overlapping and Touching M-Fish Chromosomes Using Geometry, Size and Color Information

Choi

¹

,

Bovik

²

,

Castleman

³

2006

2006 International Conference of the IEEE Engineering in Medicine and Biology Society

Self Cite

View full text Add to dashboard Cite

Since the birth of chromosome analysis by the aid of computers, building a fully automated chromosome analysis system has been the ultimate goal. Along with many other challenges, automating chromosome classification and segmentation has been one of the major challenges especially due to overlapping and touching chromosomes. In this paper we present a novel decomposition method for overlapping and touching chromosomes in M-FISH images. To overcome the limited success of previous decomposition methods that use partial information about a chromosome cluster, we have incorporated more knowledge about the clusters into a maximum-likelihood frame work. The proposed method evaluates multiple hypotheses based on geometric information, pixel classification results, and chromosome sizes, and a hypothesis that has a maximum-likelihood is chosen as the best decomposition of a given cluster. About 90% of accuracy was obtained for two or three chromosome clusters, which consist about 95% of all clusters with two or more chromosomes.

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“…Ten M-FISH images from a publicly available database were used to test our methods. The segmentation accuracy was more than 98% on average [13].…”

Section: Other Algorithmsmentioning

confidence: 88%

Algorithms for Chromosome Classification

Yan¹,

Bai²

2013

ENG

3

0

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Automated chromosome classification has been an important pattern recognition problem for decades. In order to improve the performance of automated chromosome classification, artificial intelligence and machine learning methods have been widely used in the computer-assisted chromosome detection and classification systems. This paper is focused on these algorithms, especially on artificial neural network (ANN) and wavelet transform algorithms. The principle and the realization of these algorithms are analyzed. Results of these algorithms are compared and discussed.

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Segmentation and Fuzzy-Logic Classification of M-FISH Chromosome Images

Cited by 28 publications

References 5 publications

Maximum-Likelihood Decomposition of Overlapping and Touching M-Fish Chromosomes Using Geometry, Size and Color Information

Maximum-Likelihood Decomposition of Overlapping and Touching M-Fish Chromosomes Using Geometry, Size and Color Information

Maximum-Likelihood Decomposition of Overlapping and Touching M-Fish Chromosomes Using Geometry, Size and Color Information

Algorithms for Chromosome Classification

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