Separation of overlapped chromosomes and pairing of similar chromosomes for karyotyping analysis

Devaraj, Somasundaram; Vijaykumar, V. R.

doi:10.1016/j.measurement.2013.11.024

Cited by 30 publications

(16 citation statements)

References 11 publications

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“…Several applications of automated shape analysis often suffer from a problem of multiple object overlapping that effectively complicates the task, for example [1], [4], [12], [13]. Overlapping objects must be separated into individuals before performing measurement or analysis.…”

Section: Related Workmentioning

confidence: 99%

“…At each cut point the boundary is segmented into several contour segments which are then recombined to form boundary of individual objects. Most commonly used criteria for combining the contour segments are based on geometric models, such as eclipsefitting [14], rectangle hypothesis testing [15], and parallel line construction [13]. These algorithms work successfully when a shape of objects is nearly convex, e.g.…”

Section: Related Workmentioning

confidence: 99%

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A skeleton reconstruction algorithm for identifying individual fish fry in a population image

Boonchuaychu

Wattuya

Taparhudee

2015

2015 IEEE International Conference on Imaging Systems and Techniques (IST)

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Due to rapid advance of computer vision technology, computer assisted image analysis starts to play an important role in several areas including aquaculture. In recent years several computer vision-based methods have been applied to many major operations, e.g. automated fish counting, inspection, and measurement. In this paper we address a problem of overlapping objects in a population image that frequently occurs when objects under investigation are allowed to move freely during operations. We proposed a new skeleton reconstruction algorithm for identifying and isolating individual objects in a cluster of overlapping objects. The algorithm re-assembles initial skeleton of an object cluster based on combination of edge and geometric measures, in order to form correct skeletons of individual objects in a cluster. Skeletons produced by our algorithm will be used as a basis for further automated inspection and measurement tasks. In this paper we apply our algorithm in a field of aquaculture for automated identifying individual fish fry in an overlappingfry cluster. Our algorithm can achieve 93.33 percent accuracy for skeleton reconstruction of each individual fry in a cluster of 2 -7 overlapping fry. The results also show the effectiveness of our algorithm in dealing with various overlapping patterns.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A skeleton reconstruction algorithm for identifying individual fish fry in a population image

Boonchuaychu

Wattuya

Taparhudee

2015

2015 IEEE International Conference on Imaging Systems and Techniques (IST)

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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%

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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“…Mousavi et al introduced a multiclass feature analysis for chromosomes based on the centromere position. Chromosome overlapping segmentation and separation using multiple object‐based contour and B‐spline algorithm were discussed, and to straight the bend chromosomes were also discussed . The band level analysis based on the threshold values was discussed in ref.…”

Section: Introductionmentioning

confidence: 99%

Machine learning approach for homolog chromosome classification

Devaraj¹

2018

Int J Imaging Syst Tech

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Automated analysis of human chromosomes is a necessary procedure to attain karyotyping and it is highly effective in cytology analysis to detect birth defects in metaspread chromosomes. In this, chromosomes are partitioned into “abnormal” and “normal” categories. However, the success of most traditional classification methods relies on the presence of accurate chromosome segmentation. Despite many years of research in this field, accurate segmentation and classification remains a challenge in the presence of cell clusters and pathologies. Many classification methods focused on hand crafted features, such as length, centromere positions. In this manuscript, proposed method focused on chromosome classification based on deep features using convolutional neural network. It is subsequently trained on various chromosome datasets consisting of adaptively resampled image patches. In the testing phase, average the prediction scores of a similar set of image patches is performed. The proposed method is evaluated on different overlapped, nonoverlapped chromosomes and normal, abnormal datasets. Proposed method better performs than previous algorithms in classification accuracy with 98.7%, area under the curve AUC is 0.97 values, and abnormality detection accuracy is 98.4%.

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Separation of overlapped chromosomes and pairing of similar chromosomes for karyotyping analysis

Cited by 30 publications

References 11 publications

A skeleton reconstruction algorithm for identifying individual fish fry in a population image

A skeleton reconstruction algorithm for identifying individual fish fry in a population image

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

Machine learning approach for homolog chromosome classification

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