Segmentation and classification of multispectral chromosome biages

Alhanjouri, Mohammed A.; Abou-Chadi, Fatma E. Z.; Hamdy, N.

doi:10.1109/nrsc.2005.194026

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…Color compensation techniques are explained in [5].In this author conclude that by analyzing the intensity combinations of each pixel, all chromosome pixels in an image are classified. Use of Discrete Wavelet Transform (DWT) and Bayes rule for M-FISH image segmentation and classification is presented in [6].Classification of multicolor fluorescence in situ hybridization images using Gaussian mixture models is discussed in [7]. Use of support vector machines (SVM) for segmentation and classification of M-FISH image was described in [8].…”

Section: Literature Surveymentioning

confidence: 99%

Comparative Study of IAFCM & SSFCM Segmentation Techniques for Analysis of M-FISH Chromosome Images

Korane¹

2019

IJRASET

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Analysis of Chromosomes is an important and difficult task for clinical diagnosis and biological research. Conventional analysis of chromosomes using gray scale images is a complex and tough task. With the arrival of multi-spectral image acquisition since 1996, chromosome analysis becomes much easier using M-FISH (Multi-spectral Fluorescence In-Situ Hybridization) chromosome images. In this paper IAFCM(Improved Adaptive Fuzzy C mean Clustering) & SSFCM(Spatial & Spectral Fuzzy C Mean Clustering) algorithms are used for segmentation. Some noise removal techniques like Pre-processing are also applied to improve segmentation accuracy of M-FISH chromosome images.

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Section: Literature Surveymentioning

confidence: 99%

Comparative Study of IAFCM & SSFCM Segmentation Techniques for Analysis of M-FISH Chromosome Images

Korane¹

2019

IJRASET

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“…Mohammed et al [8] presented an automated method for segmentation and classification of multispectral chromosome images. They used adaptive thresholding and valley searching for background cancellation.…”

Section: Literature Surveymentioning

confidence: 99%

M-Fish Karyotyping - A New Approach Based on Watershed Transform

Sreejini¹,

Lijiya²,

Govindan³

2012

IJCSEIT

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“…There are two main types of chromosome abnormalities, namely 1) structural abnormality and 2) numerical abnormality (insertion or deletion of chromosome copies) [9]- [10]. The former type needs visible chromosome banding with resolution above 500, while the latter type needs to count the total number of chromosomes and banding at high resolution may not be needed, e.g., checking chromosome 21 trisomy.…”

Section: Introductionmentioning

confidence: 99%

Band classification based on chromosome shapes

Uttamatanin

Intarapanich

Kaewkamnerd

et al. 2013

The 6th 2013 Biomedical Engineering International Conference

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For an automatic chromosome classification, band resolution information is required in order to perform diagnosis on numerical and structural abnormalities. Metaphases with low band resolution are normal used for numerical abnormality analysis while metaphases with high band resolution are used for structural abnormality analysis. In our work, we classify metaphases into low and high band resolution groups using chromosome shape. This band classification task can automatically perform without human intervention leading to faster the diagnosis process. The results showed that chromosome shape information is able to classify metaphases into low and high band resolution groups with the accuracy of 73.08% and 95.24%, respectively.

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Segmentation and classification of multispectral chromosome biages

Cited by 3 publications

References 7 publications

Comparative Study of IAFCM & SSFCM Segmentation Techniques for Analysis of M-FISH Chromosome Images

Comparative Study of IAFCM & SSFCM Segmentation Techniques for Analysis of M-FISH Chromosome Images

M-Fish Karyotyping - A New Approach Based on Watershed Transform

Band classification based on chromosome shapes

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