Extracting Gray Level Profiles of Human Chromosomes by Curve Fitting

Gagula-Palalic, Sadina; Can, Mehmet

doi:10.21533/scjournal.v1i2.63

Cited by 3 publications

(3 citation statements)

References 14 publications

(16 reference statements)

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“…Band pattern intensity profile is generated as a vector of numbers containing scaled values of the intensity (i.e. 0-1) by the special technique described in [19].…”

Section: A Sarajevo Chromosome Data Setmentioning

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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“…Band pattern intensity profile is generated as a vector of numbers containing scaled values of the intensity (i.e. 0-1) by the special technique described in [19].…”

Section: A Sarajevo Chromosome Data Setmentioning

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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“…It is important to define the optimal chromosome features to obtain a good accuracy. In the literature, shape description, length, centromere position, and the banding pattern have been used as chromosomes descriptors [12,18,30,31,[34][35][36]. In Reference [8,33], the authors annotate that these characteristics are useful to determine if the chromosome was correctly segmented, but they can not be used as the only descriptors of the chromosome.…”

Section: Introductionmentioning

confidence: 99%

“…Through the analysis of the medial axis it is possible to obtain the length of the chromosome, and some other features, as the gray level intensities along this axis, and its corresponding band profile [18,34]. 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.…”

Section: Introductionmentioning

confidence: 99%

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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Survey on Chromosome Image Analysis for Abnormality Detection in Leukemias

Vijayan¹,

Remya²,

Sabeena³

2015

IJRET

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Development of an automated system for the chromosome classification and abnormality detection in the case of Leukemias has significant importance in the field of cytogenetics. Such systems can be effectively used to find the genetic disorders and used in treatment evaluation procedures, prognosis prediction and in follow up treatment with the help of karyotyping. This is possible because the leukemia's are generally characterized by numerical and structural chromosomal abnormalities. During the last three decades, several developments in the field of automated chromosome analysis have been occurred. Different preprocessing, segmentation, feature extraction and classification methods are applied to develop an automatic karyotyping system. But still now, the classification and the abnormality detection of chromosomes are done in the laboratories with the help of human intervention which is time consuming and labor intensive. Further research is needed in this field to develop completely automated abnormality detection systems which are capable of effectively identify the abnormalities without the human intervention. Here we have done a survey on different methods of karyotyping existing so far that leads to efficient systems for abnormality detection.

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Extracting Gray Level Profiles of Human Chromosomes by Curve Fitting

Cited by 3 publications

References 14 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

Survey on Chromosome Image Analysis for Abnormality Detection in Leukemias

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