A modular framework for the automatic classification of chromosomes in Q-band images

Poletti, Enea; Grisan, Enrico; Ruggeri, Alfredo

doi:10.1016/j.cmpb.2011.07.013

Cited by 28 publications

(28 citation statements)

References 18 publications

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“…A very recent method used for an automatic classification of chromosomes of Q-band images has been proposed by Poletti et al [17]. In this work, they introduced a modular framework that is a very complied with the standard techniques like ANN and feature standardization, employing the robust medial axis extraction, polarization step, and reassignment stage, and out of 5474 chromosomes they achieved 94% of accurate classification.…”

Section: Human Chromosome Classification Using Competitive Neural Netmentioning

confidence: 99%

Human chromosome classification using Competitive Neural Network Teams (CNNT) and Nearest Neighbor

Gagula-Palalic

Can

2014

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)

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This paper presents a novel approach to human chromosome classification. Human cell contains 22 pairs of autosomes and a pair of sex chromosomes. In this research, 22 types of autosomes represent 22 classes to be distinguished. New method of classification is based on the special organized committee of 462 simple perceptrons, called Competitive Neural Network Teams (CNNTs). Each perceptron is trained to differentiate two classes (i.e. two types of chromosome), hence there are 22 x 21 learning machines. Moreover, dummy perceptrons are set to zero for the chromosomes from the same class. The final outcome of the testing data is a 22x22 decision matrix, containing outcomes of each machine. With the special interpretation of these decisions, higher correct classification rate is achieved, reaching over 95%. The method can be further improved when testing is performed on a cell-by-cell basis by using CNNT complemented by Nearest Neighbor technique. The classification is applied to the Copenhagen chromosome data set and Sarajevo chromosome data set.

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Section: Human Chromosome Classification Using Competitive Neural Netmentioning

confidence: 99%

Human chromosome classification using Competitive Neural Network Teams (CNNT) and Nearest Neighbor

Gagula-Palalic

Can

2014

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)

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“…There exists at least one public image database that has been used to develop computational systems to automatically build karyograms. One of these database was made available in Reference [26], and is composed of 119 chromosome studies, including their karyotype. This database was formed in well controlled conditions, producing homogeneous images across every study, and is ideal to test automatic karyotyping systems.…”

Section: Introductionmentioning

confidence: 99%

“…The computer-based automatic construction of karyograms performs two complementary and sequential tasks: image segmentation and segmented chromosomes classification [30,31]. There are several works that segment and classify Q-band chromosomes images with varying results [8,26,32,33].…”

Section: Introductionmentioning

confidence: 99%

“…In Reference [33], the authors approximate the medial axis using transversal lines along the chromosome with different orientations. In a similar way, in Reference [26], the medial axis is computed using transversal lines along the skeleton of the chromosome. They begin by selecting a point on one of the ends of the chromosome; then, transversal lines are traced at increasing angles until the full circle is covered.…”

Section: Introductionmentioning

confidence: 99%

“…In Reference [8], a feature vector is constructed using the chromosome length, the gray intensity levels along 98 transversal lines traced over the medial axis, and the chromosome area, computed by counting the active pixels of the binary chromosome image. The methodology presented in Reference [26] uses a vector composed of 131 features, including area, length, perimeter and 64 gray level intensities extracted over normal lines to the medial axis of the chromosome. These vectors are normalized, making possible to compare chromosomes from different images and improving the performance of the classifier.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Classifiers Evaluation for Automatic Karyogram Generation from G-Banded Metaphase Images

2020

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This work proposes the evaluation of a set of algorithms of machine learning and the selection of the most appropriate one for the classification of segmented chromosomes images acquired using the Giemsa staining technique (G-banding). The evaluation and selection of the best classification algorithms was carried out over a dataset of 119 Q-banding chromosomes images, and the obtained results were then applied to a dataset of 24 G-band chromosomes images, manually classified by an expert of the Laboratory of Cytogenetic of the Children’s Hospital of Tamaulipas. The results of evaluation of 51 classifiers yielded that the best classification accuracy for the selected features was obtained by a backpropagation neural network. One of the main contributions of this study is the proposal of a two-stage classification scheme based on the best classifier found by the initial evaluation. In stage 1, chromosome images are classified into three major groups. In stage 2, the output of phase 1 is used as the input of a multiclass classifier. Using this scheme, 82% of the IGB bank samples and 88% of the samples of a bank of images obtained with a Q-band available in the literature consisting of 119 chromosome studies were successfully classified. The proposed work is a part of an desktop application that allows cytogeneticist to automatically generate cytogenetic reports.

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Adaptive Chromosome Diagnosis Based on Scaling Hierarchical Clusters

Agca

Taştan

Üstün

et al. 2021

Transactions on Computational Science and Computational Intelligence

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A modular framework for the automatic classification of chromosomes in Q-band images

Cited by 28 publications

References 18 publications

Human chromosome classification using Competitive Neural Network Teams (CNNT) and Nearest Neighbor

Human chromosome classification using Competitive Neural Network Teams (CNNT) and Nearest Neighbor

Machine Learning Classifiers Evaluation for Automatic Karyogram Generation from G-Banded Metaphase Images

Adaptive Chromosome Diagnosis Based on Scaling Hierarchical Clusters

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