Segmentation of human chromosome images using distance regularized level set evolution

Mouhadjer, Hassan; Mansour, M.; Ouslim, Mohamed; Bouchiba, Bousmaha

doi:10.1109/icabme.2013.6648886

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…. ., m. The roughness index R i for the inner circle is formulated as follows: (8) where R i is the roughness inside the segmented object.…”

Section: Roughness-based Featuresmentioning

confidence: 99%

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A Hybrid Learning Method with High Specificity for Excluding Non-chromosome Objects Problem

Wu¹,

Kao

Hsieh

et al. 2022

Preprint

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Background: For automated analysis of metaphase chromosome images, the chromosome objects on the images need to be segmented in advance. However, the segmentation results often contain a lot of non-chromosome objects in the images. Hence, elimination of non-chromosome objects is an essential process in automated chromosome image analysis. This study aims to exclude non-chromosome objects and preserve as many chromosomes as possible. In this paper, we propose a hybrid deep learning method to exclude non-chromosome objects from metaphase chromosome images. Method: The proposed method consists of two phases. In the first phase, two classification results are obtained from feature-based and image-based convolutional neural networks (CNN) separately (the feature-based CNN uses the features of the images as input; the image-based CNN uses the images as input directly). In the second phase, the prediction results from the above two CNNs are combined and resent to another CNN to obtain final classification results. Results: The proposed method uses 18,757 non-chromosome objects and 43,398 chromosomes (including single and multiple overlapped chromosomes) from 1038 chromosome images to evaluate the performance. The experimental results show that the proposed method can detect and exclude 99.61% (18,683/18,757) of the non-chromosome objects and preserve 99.95% (43,375/43,398) of the chromosomes for further analysis. Conclusions: The proposed method has a high effectiveness on excluding non-chromosome objects and could be used as a preprocessing procedure for chromosome image analysis.

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“…. ., m. The roughness index R i for the inner circle is formulated as follows: (8) where R i is the roughness inside the segmented object.…”

Section: Roughness-based Featuresmentioning

confidence: 99%

“…For automated analysis of metaphase chromosome images, the chromosomes on the images need to be segmented first by performing thresholding [1][2][3][4][5][6] or other techniques [7][8][9]. Meanwhile, the segmentation results often contain several non-chromosome objects, such as interphase cells, stain debris, and other background noise.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Learning Method with High Specificity for Excluding Non-chromosome Objects Problem

Wu¹,

Kao

Hsieh

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In level set method, speed and direction of the propagation depends on image intensity gradient and the curvature. Internal and external energies along the boundary of curve are minimized by deforming the curve along its shape [11].…”

Section: Image Segmentation Based On Level Setmentioning

confidence: 99%

Biometric Foetal Contour Extraction using Hybrid Level Set

Jaiswal¹,

Satarkar²

2020

Computer Science &Amp; Information Technology (CS &Amp; IT)

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In medical imaging, accurate anatomical structure extraction is important for diagnosis and therapeutic interventional planning. So, for easier, quicker and accurate diagnosis of medical images, image processing technologies may be employed in analysis and feature extraction of medical images. In this paper, some modifications to level set algorithm are made and modified algorithm is used for extracting contour of foetal objects in an image. The proposed approach is applied on foetal ultrasound images. In traditional approach, foetal parameters are extracted manually from ultrasound images. Due to lack of consistency and accuracy of manual measurements, an automatic technique is highly desirable to obtain foetal biometric measurements. This proposed approach is based on global & local region information for foetal contour extraction from ultrasonic images. The primary goal of this research is to provide a new methodology to aid the analysis and feature extraction from foetal images.

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Segmentation of overlapping chromosome images using U-Net with improved dilated convolutions

Sun

et al. 2021

IFS

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Chromosome visualization has been used in human chromosome analysis and is a crucial step in clinical diagnosis and drug development. An important step in chromosome visualization is the extraction of chromosomes from chromosome images obtained by light microscopy. Chromosomes often overlap in a complex and variable manner, resulting in significant challenges in chromosome segmentation. The process of chromosome visualization requires manual intervention and is tedious. A method based on a neural network is proposed for the automatic segmentation of overlapping chromosome images to speed up the workflow of visualizing chromosomes. Three improved dilated convolutions are used in the chromosome image segmentation models based on U-Net. The proposed models successfully segment overlapping chromosomes in two publicly available overlapping chromosome data sets. Our models have better performance than existing overlapping chromosome segmentation methods based on U-Net. In summary, it is demonstrated that the improved dilated convolutions can be used for the automatic segmentation of overlapping chromosome images. The proposed improved dilated convolutions have a stable performance improvement, can be easily extended to the segmentation of multiple overlapping chromosomes, and are suitable as general neural network operations to replace standard convolutions in any network.

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Segmentation of human chromosome images using distance regularized level set evolution

Cited by 4 publications

References 12 publications

A Hybrid Learning Method with High Specificity for Excluding Non-chromosome Objects Problem

A Hybrid Learning Method with High Specificity for Excluding Non-chromosome Objects Problem

Biometric Foetal Contour Extraction using Hybrid Level Set

Segmentation of overlapping chromosome images using U-Net with improved dilated convolutions

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