Chromosome Classification Research Articles

Classification of Denver System of Chromosomes Using Similarity Classifier Guided by OWA Operators

This paper proposes a Similarity Classifier guided by Ordered Weighted Averaging (OWA) operators to classify ‘Denver System’ of human chromosomes. A chromosome is assigned to one of the seven groups from A to G, based on the ‘Denver System’ of classification of chromosomes. Chromosomes within the group are difficult to identify, possessing almost identical characteristics including the length and centromere position. The proposed method is applied to the chromosome dataset and the similarity values using the generalized Lukasiewicz structure are calculated. The similarity values are aggregated with the help of Ordered Weighted Averaging (OWA) operators with a Regular Increasing Monotone (RIM) quantifier and the chromosome is assigned to that group for which it has the highest value of similarity. This work evaluates the performance of classifiers including Naive Bayes, Quadratic classifier and a novel, unsupervised, Fuzzy Similarity Classifier guided by OWA Operators, in classifying the Denver Group of chromosomes. A fundamental review on fuzzy similarity based classification is also presented. Experimental results clearly demonstrates that the proposed Fuzzy Similarity Classifier guided by OWA Operators with a RIM quantifier, produces the best classification results with a mean classification accuracy of 96.0028% and variance of 5.0871e-005. Statistical testing with one-way Analysis of Variance (ANOVA) at 95% and 99% level of confidence and Tukey’s post-hoc analysis is performed to validate the selection of the classifier. The proposed Fuzzy Similarity Classifier gives the most promising classification results and can be applied to any large scale biomedical data and other applications. Keywords: Analysis of variance, chromosomes, classification, denver system, fuzzy similarity classifier, lukasiewicz structure, ordered weighted averaging, operators, regular increasing monotone, quantifier, Tukey's post-hoc.

Tropical turtles chromosomes: Kinosternon leucostomum, Trachemys scripta and Staurotypus triporcatus (Testudines: Kinosternidae/Emydidae)

Mexico is a biodiverse country in several taxa as reptiles, that include several species of freshwater and marine turtles. Eventhough most of this group species are under protection, Tabasco State has nine native freshwater turtles, like Kinosternon leucostomum, Trachemys scripta and Staurotypus triporcatus that are very important in traditional dishes. This has resulted in a critical level of their populations, together with little biological knowledge for their conservation. Therefore, this study was dedicated to turtle cytogenetics. The study was conducted using the conventional methods for cytogenetics. The results showed the modal diploid and haploid number for K. leucostomum of 2n = 56 (2n = 56+3 microchromosomes "B") and 1n = 28 chromosomes in mitosis and meiosis, respectively. In T. scripta 2n = 50 chromosomes (2n = 50+2 microchromosomes "B") and 1n = 25 chromosomes were also characterized. Whereas in S. triporcatus we only report the 2 = 54 chromosomes (2n = 54+2 microchromosomes "B"). The karyological formula for K. leucostomum was integrated by 12 metacentric-submetacentric chromosomes "msm"/"A"+22 subtelocentric-telocentric chromosomes "stt"/"B"+22 telocentric chromosomes "T"/"C" with fundamental number (FN) of 90 chromosome arms. While T. scripta karyotype was integrated by 32 "msm/"A"+10 "stt"/"B"+8"T/"C" chromosomes, with FN of 92 arms. S. triporcatus karyotype formula was built up by 20 chromosomes "msm"/"A"+34 chromosomes "T"/"C" with FN of 74. The variation in chromosome classification, the fundamental number and the presence of supernumerary microchromosomes "B" in the studied species, were evidence of a particular chromosome cytotypes in Tabasco. We considered that the presence of microchromosomes "B" probably has different origins, and they may be very important as a pattern for the formation or separation of new species. This study also showed the absence of heterologous chromosomes between the females and males karyotypes from the studied species.

An Efficient Segmentation Method for Overlapping Chromosome Images

are being developed to automate the task of classification of chromosomes. The chromosomes are non- rigid material and they are several times touching each other or they overlap each other in the metaphase images. So different techniques are required to segregate the overlapping chromosomes. This paper presents a novel method for segmenting chromosomes based upon computational geometry. In the proposed approach first the contour line is traced for the overlapping chromosomes and then all the cut points are traced for the overlapping chromosomes. Then based on computational geometry method a specific number of cut points are selected and they are used for separating the two chromosomes. We have found that 87.4% of the images were correctly segmented using the proposed method.

Chromosomal Edge Detection using Modified Bacterial Foraging Algorithm

This paper proposes a modified bacterial foraging algorithm with a probabilistic derivative approach to detect edges in chromosome images. Chromosomal Edge Detection is fundamental for automatic karyotyping for noise reduction and getting useful messages from the edges. Subjected to staining and other imaging constraints, chromosomal banding patterns lack in resolution, contrast and suffer from noise. For this reason, chromosomal edge detection is highly preferred prior to the segmentation and classification of chromosomes. When the chromosomes occlude or overlap, edge detection becomes extremely difficult. Edge detection is highly challenging and this paper presents a Modified Bacterial Foraging Algorithm (MBFA) based on a probabilistic derivative methodology based on Ant Colony Optimization (ACO) for the detection of edges in chromosomes. Bacterium searches for the nutrients in the direction decided by a probabilistic derivative approach derived from ACO and the edge pixels are identified and traversed. The study reveals that MBFA gives the most promising results in detecting chromosomal edges, greatly reducing the computation time and memory requirements. Acceptable values of parameters for performance evaluation like Kappa (K) and Entropy (E) are achieved with the proposed algorithm in comparison to the other conventional methods of edge detection.

Denver Groups Classification of Human Chromosomes Using CANN Teams Supplemented by a Nearest Neighbor Technique CANNT-S

Classification of the human chromosomes based on their lengths and the centromeric index is performed such that chromosomes are classified into seven Denver Groups (A-G). In this article, the novel artificial neural network committee machines technique (CANNT) developed earlier is modified to take into account mixed signals in winning teams of CANNT, and the correct classification rate in Denver Groups Classification of Human Chromosomes raised from 96%, to a level of 97.1%.

Denver Groups Classification of Human Chromosomes Using CANN Teams Supplemented by a Nearest Neighbor Technique CANNT-S

Denver Groups Classification of Human Chromosomes Using CANN Teams Supplemented by a Nearest Neighbor Technique CANNT-S

Denver Groups Classification of Human Chromosomes Using CANN Teams

Unbanded human chromosome can be classified into seven Denver Groups (A-G) based on their lengths and the ratio of the length of the shorter arm to the whole length of the chromosome, which is called the centromere index (CI). In this article, the novel artificial neural network committee machines technique (CANNT) developed earlier, is applied to the Denver Groups and the correct classification rate in Denver Groups Classification of Human Chromosomes raised from 96%, to a level of 98%.

An Organized Committee of Artificial Neural Networks in the Classification of Human Chromosomes

networks are organized in committees to improve the correctness of the decisions created by artificial neural networks (ANN's). In the classification of human chromosomes, it is accustomed to use multilayer perceptrons with multiple (22-24) outputs. Because of the huge number of synaptic weights to be tuned, these classifiers cannot go beyond a level of 92% overall correctness. In this study we represent a special organized committee of 462 simple perceptrons to improve the rate of correct classification of 22 types of human chromosomes. Each of these simple perceptrons is trained to distinguish between two types of chromosomes. When a new data is entered, the votes of these 462 simple perceptrons and additional 22 dummy perceptrons create a decision matrix of the size 22×22. By a special assembling of these votes we get a higher rate of correct classification of 22 types of human chromosomes.

An improved sparse representation model with structural information for Multicolour Fluorescence In-Situ Hybridization (M-FISH) image classification

BackgroundMulticolour Fluorescence In-Situ Hybridization (M-FISH) images are employed for detecting chromosomal abnormalities such as chromosomal translocations, deletions, duplication and inversions. This technique uses mixed colours of fluorochromes to paint the whole chromosomes for rapid detection of chromosome rearrangements. The M-FISH data sets used in our research are obtained from microscopic scanning of a metaphase cell labelled with five different fluorochromes and a DAPI staining. The reliability of the technique lies in accurate classification of chromosomes (24 classes for male and 23 classes for female) from M-FISH images. However, due to imaging noise, mis-alignment between multiple channels and many other imaging problems, there is always a classification error, leading to wrong detection of chromosomal abnormalities. Therefore, how to accurately classify different types of chromosomes from M-FISH images becomes a challenging problem.MethodsThis paper presents a novel sparse representation model considering structural information for the classification of M-FISH images. In our previous work a sparse representation based classification model was proposed. This model employed only individual pixel information for the classification. With the structural information of neighbouring pixels as well as the information of themselves simultaneously, the novel approach extended the previous one to the regional case. Based on Orthogonal Matching Pursuit (OMP), we developed simultaneous OMP algorithm (SOMP) to derive an efficient solution of the improved sparse representation model by incorporating the structural information.ResultsThe p-value of two models shows that the newly proposed model incorporating the structural information is significantly superior to our previous one. In addition, we evaluated the effect of several parameters, such as sparsity level, neighbourhood size, and training sample size, on the of the classification accuracy.ConclusionsThe comparison with our previously used sparse model demonstrates that the improved sparse representation model is more effective than the previous one on the classification of the chromosome abnormalities.

Efficient karyotyping of metaphase chromosomes using incremental learning

Automated karyotyping for chromosome classification is an essential task in cytogenetics for diagnosis of genetic disorders and has therefore been an important pattern recognition problem. The existing learning approaches generally discard the previously acquired knowledge and often require retraining, leading to space and time complexities. Incremental learning methods have gained popularity in the current learning scenarios to deal with these issues. This study proposes a novel approach of incremental learning for chromosomes classification for automated karyotyping of metaphase chromosomes. It addresses the issue of catastrophic forgetting with the generation of new class and performs knowledge amassing to classify the chromosomes in Denver groups (A–G). The adaptive nature of the proposed method contributes to its sustained accuracy even for dynamically changing data. An average classification accuracy of 97% is achieved with experimentation on 1800 chromosomes from a publicly available database.

Analysis of human chromosome classification using centromere position

Automated homologous classification of human chromosomes from the metaphase spread of a cell is an essential task to identify various genetic disorders. Karyotyping is the process of visualizing and categorizing the chromosomes within a cell. It is very important to have a good feature extraction scheme for proper chromosome classification. Chromosome classification is performed by identifying the morphological features which mainly include the centromere location. The parameter is essential for classification which helps in determining a karyogarm. In this paper, a comparative analysis of two algorithms for identification of centromere position is done. The two approaches make use of the Medial Axis Transformation and Projection Vector. The analysis helps us to identify the algorithm that performs better in the identification of centromere position. The projection vector approach performs well in obtaining the centromere location in the chromosome with an efficiency of 90%.

Algorithms for Chromosome Classification

Automated chromosome classification has been an important pattern recognition problem for decades. In order to im-prove 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.

Segmentation of Overlapped and Touching Human Chromosome images

Chromosomes are essential genomic information carriers. The identification of chromosome abnormalities is an essential part of diagnosis and treatment of genetic disorders such as chromosomal syndromes and many types of cancer. Currently available cytogenetic imaging software is designed to classify only normal chromosomes. The automation of chromosome analysis is involving segmentation of chromosomes and classification into 24 groups. Segmentation of the overlapped chromosomes is a major step toward the realization of homolog classification. Resolving chromosome overlaps is an unsolved problem in automated chromosome analysis. Current systems for automatic chromosome classification are mostly interactive and require human intervention. In this paper, an automatic procedure is proposed to obtain the separated chromosomes. The separations of overlapped and touching chromosomes are obtained by finding the intersecting (concave and convex) points with the help of Novel algorithm. The intersecting points are located on contour of the image and then the curvature function is used to find out concave points. Then the possible separation lines are plotted by using all concave points and finally construct the hypotheses for possible separation lines between concave points. The segmentation is carried out by means of a curvature function scheme, which proved to be successful.

Análise cromossômica em bulbilhos de sisal (Agave spp.) cultivados em diferentes municípios baianos, Brasil

O plantio de sisal tornou-se uma das atividades econômicas mais importantes na região do semiárido baiano, onde as adversidades ambientais impedem o plantio de outras culturas. Diante da importância econômica, estudos de citogenética são necessários, pois podem fornecer informações que auxiliem na produção de linhagens melhoradas. A análise de bulbilhos, coletados de cinco localidades diferentes da Bahia, mostrou a presença de indivíduos diplóides e pentaplóides, com comprimento cromossômico variando de 24,56 µm até 0,43 µm nos diferentes exemplares. A formulação cariotípica para o híbrido n° 11648 (2n = 2x = 60), coletado no município de Valente, foi de 2mv + 38m + 10sm + 8st + 2t. Já para os pentaplóides (2n = 5x = 142 c.a.) coletadas nos municípios de Conceição do Coité e Valente a classificação cromossômica encontrada foi: 14m + 66sm + 38st + 2a + 22t e 82m + 48sm + 12st, respectivamente. Fatores como maior comprimento genômico, presença de cromossomos acrocêntricos, subtelocêntricos e telocêntricos nos cariótipos bimodais e a diminuição na quantidade de cromossomos grandes com conseqüente aumento no número de cromossomos pequenos podem indicar o andamento de um processo divergente.

Classification of chromosomes using nearest neighbor classifier

This paper addresses automated classification of human chromosomes using k nearest neighbor classifier. k nearest neighbor classifier classifies objects according to the closest training sample in the feature space. Various distance functions can be used in computation of how close the object is to the training sample. In this work various different distance functions are used to compare the performance of each. It was found that Euclidean distance function produces the best results.

Application of Ensemble Machines of Neural Networks to Chromosome Classification

This work presents approaches to the automatic classification of metaphase chromosomes using several perceptron neural network techniques on neural networks function as committee machines. To represent the banding patterns, only chromosome gray level profiles are exploited. The other inputs to the ensemble machines of the network are the chromosome size and centromeric index. It is shown that, without much effort, the classification performances of the four networks are found to be similar to the ones of a well-developed parametric classifier. Four parallel networks trained for the four different aspects of the data set, the gray level profile vector, Fourier coefficients of gray level profiles, 3D data of chromosome length – centromeric index – total gray levels, and 4D data obtained by the addition of average gray levels. Then the classification results of differently trained neural networks (i.e., experts), are combined by the use of a genuine ensemble-averaging to produce an overall output by the combiner. We discuss the flexibility of the classifier developed, its potential for development, and how it may be improved to suit the current needs in karyotyping.

Marker Chromosomes

Marker chromosomes are a morphologically heterogeneous group of structurally abnormal chromosomes that pose a significant challenge in prenatal diagnosis. Phenotypes associated with marker chromosomes are highly variable and range from normal to severely abnormal. Clinical outcomes are very difficult to predict when marker chromosomes are detected prenatally. In this review, we outline the classification, etiology, cytogenetic characterization, and clinical consequences of marker chromosomes, as well as practical approaches to prenatal diagnosis and genetic counseling.

Segmentation of M-FISH Images for Improved Classification of Chromosomes With an Adaptive Fuzzy C-means Clustering Algorithm

An adaptive fuzzy c-means algorithm was developed and applied to the segmentation and classification of multicolor fluorescence in situ hybridization (M-FISH) images, which can be used to detect chromosomal abnormalities for cancer and genetic disease diagnosis. The algorithm improves the classical fuzzy c-means algorithm (FCM) by the use of a gain field, which models and corrects intensity inhomogeneities caused by a microscope imaging system, flairs of targets (chromosomes), and uneven hybridization of DNA. Other than directly simulating the inhomogeneousely distributed intensities over the image, the gain field regulates centers of each intensity cluster. The algorithm has been tested on an M-FISH database that we have established, which demonstrates improved performance in both segmentation and classification. When compared with other FCM clustering-based algorithms and a recently reported region-based segmentation and classification algorithm, our method gave the lowest segmentation and classification error, which will contribute to improved diagnosis of genetic diseases and cancers.

Unexpected results in the constitution of small supernumerary marker chromosomes

Traditional approaches for the classification of Small Supernumerary Marker Chromosomes (sSMC), mostly based on FISH techniques, are time-consuming and not always sufficient to fully understand the true complexity of this class of rearrangements. We describe four supernumerary marker chromosomes that, after array-CGH, were interpreted rather differently in respect to the early classification made by conventional cytogenetics and FISH investigations, reporting two types of complex markers which DNA content was overlooked by conventional approaches: 1. the sSMC contains non-contiguous regions of the same chromosome and, 2. the sSMC, initially interpreted as a supernumerary del(15), turns out to be a derivative 15 to which the portion of another chromosome was attached. All are likely derived from partial trisomy rescue events, bringing further demonstration that germline chromosomal imbalances are submitted to intense reshuffling during the embryogenesis, leading to unexpected complexity and changing the present ideas on the composition of supernumerary marker chromosomes.

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

Abstract The manual analysis of the karyogram is a complex and time-consuming operation, as it requires meticulous attention to details and well-trained personnel. Routine Q-band laboratory images show chromosomes that are randomly rotated, blurred or corrupted by overlapping and dye stains. We address here the problem of robust automatic classification, which is still an open issue. The proposed method starts with an improved estimation of the chromosome medial axis, along which an established set of features is then extracted. The following novel polarization stage estimates the chromosome orientation and makes this feature set independent on the reading direction along the axis. Feature rescaling and normalizing techniques take full advantage of the results of the polarization step, reducing the intra-class and increasing the inter-class variances. After a standard neural network based classification, a novel class reassignment algorithm is employed to maximize the probability of correct classification, by exploiting the constrained composition of the human karyotype. An average 94% of correct classification was achieved by the proposed method on 5474 chromosomes, whose images were acquired during laboratory routine and comprise karyotypes belonging to slightly different prometaphase stages. In order to provide the scientific community with a public dataset, all the data we used are publicly available for download.