Chromosome classification based on the band profile similarity along approximate medial axis

Kao, Jau-Hong; Chuang, Jen-Hui; Wang, Tsaipei

doi:10.1016/j.patcog.2007.05.016

Cited by 39 publications

(18 citation statements)

References 21 publications

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“…The raw data, preprocessing and representation are described in detail in [4,10]. Chromosome images are finally represented as variable-length strings of symbols, each of which represents the variation of the image grey-level along the chromosome median axis.…”

Section: Methodsmentioning

confidence: 99%

Interactive Structured Output Prediction: Application to Chromosome Classification

Oncina

Vidal

2011

Pattern Recognition and Image Analysis

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Abstract. Interactive Pattern Recognition concepts and techniques are applied to problems with structured output; i.e., problems in which the result is not just a simple class label, but a suitable structure of labels. For illustration purposes (a simplification of) the problem of Human Karyotyping is considered. Results show that a) taking into account label dependencies in a karyogram significantly reduces the classical (noninteractive) chromosome label prediction error rate and b) they are further improved when interactive processing is adopted.

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Section: Methodsmentioning

confidence: 99%

Interactive Structured Output Prediction: Application to Chromosome Classification

Oncina

Vidal

2011

Pattern Recognition and Image Analysis

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“…Karyotype is the normal nomenclature where the chromosomes are arranged in homologous pairs in a systematic manner to describe the normal or abnormal chromosomal complement of an individual, tissue or cell line (ISCN, 2005). Jau-hong Kao et al (2008) described chromosome classification based on the band profile similarity along approximate medial axis. This was soon followed by amniocentesis to determine the chromosomal abnormalities in fetal cells in the amniotic fluid, which formed the core of prenatal genetic diagnosis (Steele & Breg, 1966).…”

Section: History Of Cytogeneticsmentioning

confidence: 99%

Cytogenetic Techniques in Diagnosing Genetic Disorders

Kannan¹

2011

Advances in the Study of Genetic Disorders

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“…The customary features used in chromosome classification are the length, the centromeric index (CI) and the density profile of the chromosome image [1,4,9]. The density profile is obtained by sampling the gray level values along the lengthwise axis of the chromosome.…”

Section: Feature Extractionmentioning

confidence: 99%

Chromosome Classification Based on Wavelet Neural Network

Oskouei

Shanbehzadeh²

2010

2010 International Conference on Digital Image Computing: Techniques and Applications

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Karyotyping, manual chromosome classification is a difficult and time consuming process. Many automated classifiers have been developed to overcome this problem. These classifiers either have high classification accuracy or high training speed. This paper proposes a classifier that performs well in both areas based on wavelet neural network (WNN), combining the wavelet into neural network for classification of chromosomes in group E (chromosomes 16, 17 and 18). The nonlinear characteristic of the network which is derived from wavelet specification improves the training speed and accuracy of the nonlinear chromosome classification. The network inputs are nine dimensional feature space extracted from the chromosome images and the outputs are three classes. The simulation result on the chromosomes in the Laboratory of Biomedical Imaging 1 shows that the success rate of WNN was 0.93%, that is comparable to the traditional neural network (ANN) with 0.85% success rate. The number of iterations for training to reach 0.04% error rate is only 200 where it is 3500 iterations for ANN. According to the experimental results WNN achieves high accuracy with minimum training time, which makes it suitable for real-time chromosome classification in the laboratory.

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Chromosome classification based on the band profile similarity along approximate medial axis

Cited by 39 publications

References 21 publications

Interactive Structured Output Prediction: Application to Chromosome Classification

Interactive Structured Output Prediction: Application to Chromosome Classification

Cytogenetic Techniques in Diagnosing Genetic Disorders

Chromosome Classification Based on Wavelet Neural Network

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