Chromosome classification for karyotype composing applying shape representation on wavelet packet transform

Guimarães, Letícia Vieira; Schuck, Adalberto; Elbern, Alwin W.

doi:10.1109/iembs.2003.1279921

Cited by 17 publications

(7 citation statements)

References 5 publications

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“…Finally, the coefficients of the best tree of wavelet packet transform correspondent to the signature of chromosomes are compared in order to classify the chromosomes. The results obtained show that the proposed method provides an effective chromosome classification based on WPT and BBA of the shape signature of the chromosomes [9]. …”

Section: Wavelet Transforms Based Algorithmsmentioning

confidence: 84%

Algorithms for Chromosome Classification

Yan¹,

Bai²

2013

ENG

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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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Section: Wavelet Transforms Based Algorithmsmentioning

confidence: 84%

Algorithms for Chromosome Classification

Yan¹,

Bai²

2013

ENG

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“…Another method, based on the analysis of the dominant points of the contour and on variants for profile extraction is proposed in [6]. A method proposing a chromosome shape representation, which applies wave packet transform, is presented in [7]. As reported in [8], the most discriminating features for the classification are length, centromeric index and density profile.…”

Section: Introductionmentioning

confidence: 98%

Automatic classification of chromosomes in Q-band images

Poletti

Grisan

Ruggeri

2008

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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The manual analysis of the karyogram is a complex, wearing and time-consuming operation. It requires a very meticulous attention to details and calls for well-trained personnel. Even though existing commercial software packages provide a reasonable support to cytogenetists, they very often require human intervention to correct challenging situations. We developed a robust automatic classification system conceived to cope with routine images in which chromosomes are randomly rotated, possibly blurred or also corrupted by overlapping or by dye stains. It consists in a sequence of modules comprising robust feature extraction based on medial axis, chromosome polarization, feature pre-processing, and Neural Network classification followed by a class reassigning algorithm.We show the effectiveness of the proposed method on data comprising karyotypes belonging to slightly different stage of the prometaphase. This dataset contains 119 karyotypes (5474 chromosomes), 70 of which were used for training and validation and 49 for the final testing. In this latter set of images, the system achieved a classification accuracy, as compared to manual ground truth, of 95.6%.

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“…In some studies, a reduced version of the density profile (Lerner et al, 1995) or features extracted from its Fourier or wavelet transformation have been used (Sweeney and Becker, 1997). Using wavelet packet transformation for extraction of features that represent the chromosome shape has recently been reported (Guimaraes et al, 2003). The resulting feature vector is then used with a classification method like the Bayesian classifier (Carothers and Piper, 1994;Qiang and Castleman, 2000), neural network classifier (Cho, 2000;Lerner et al, 1995;Sweeney and Becker, 1997;Lerner, 1998;Graham et al, 1992), or fuzzy classifier (Vanderheydt et al, 1980), nearest neighbor (Groen et al, 1989).…”

Section: Introductionmentioning

confidence: 99%