Watershed and Clustering Based Segmentation of Chromosome Images

Manohar, Ruchita; Gawande, Jayanand P.

doi:10.1109/iacc.2017.0145

Cited by 12 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Manohar et al 57 compared the performance of FCM and watershed transform in chromosome segmentation using publicly available M-FISH database. The overall accuracy was 94% and 92% for FCM and watershed transform respectively.…”

Section: Sayed and Hassanienmentioning

confidence: 99%

Automated human chromosome segmentation and feature extraction: Current trends and prospects

2022

View full text Add to dashboard Cite

Automated human chromosome segmentation and feature extraction aim to improve the overall quality of genetic disorder diagnosis by addressing the limitations of tedious manual processes such as expertise dependence, time-inefficiency, observer variability and fatigue errors. Nevertheless, significant differences caused by staining methods, chromosome damage which may occur during imaging, cell and staining debris, inhomogeneity, weak boundaries, morphological variations, premature sister chromatid separation, as well as the presence of overlapping, touching, di-centric and bent chromosomes pose challenges in automated human chromosome segmentation and feature extraction. This review paper extensively discusses how the approaches presented in literature have addressed these challenges, and their strengths and limitations. Human chromosome segmentation algorithms are presented under four broad categories; thresholding, clustering, active contours and convex-concave points-based methods. Chromosome feature extraction methods are discussed under two main categories based on banding-pattern and geometry. In addition, new insights for the improvement of fully automated karyotyping are provided.

show abstract

Section: Sayed and Hassanienmentioning

confidence: 99%

Automated human chromosome segmentation and feature extraction: Current trends and prospects

2022

View full text Add to dashboard Cite

show abstract

“…The process of chromosomal karyotyping is performed by pairing the chromosomes according to the similarity between them. The chromosomes are classified in one of the four classes according to the location of the centromere: metacentric, submetacentric, subtelocentric, and acrocentric [4] [5].…”

Section: Introductionmentioning

confidence: 99%

Segmentation of fish chromosomes in microscopy images: A new dataset

Rodrigues¹,

Pasa²,

Kavalco³

et al. 2020

Anais Do XVI Workshop De Visão Computacional (WVC 2020)

View full text Add to dashboard Cite

The chromosome segmentation is the most important step in automatic karyotype assembling. In this work, we presented a brand new chromosome image dataset and proposed methods for segmenting the chromosomes. Chromosome images are usually low quality, especially fish chromosomes. In order to overcome this issue, we tested three filters to reduce noise and improve image quality. After filtering, we applied adaptive threshold segmentation combined with mathematical morphology and supervised classification methods. Support Vector Machine and k-nearest neighbors were applied to discriminate between chromosomes and image background. The proposed method was applied to segment chromosomes in a new dataset. To enable measure the performance of the methods all chromosomes were manually delineated. The results are evaluated considering the Hausdorff distance and normalized sum of distances between segmented and reference images.

show abstract

“…For the identification of the centromere, the centromere positions can be calculated by convex and concave property 10,12 , and the pixel information of the chromosomes 6,7,13 . The centromere can also be detected by applying multicolor fluorescence in situ hybridization images [14][15][16][17] . Since the morphology of chromosomes is diverse, the conventional methods cannot easily identify different morphologies of chromosomes, limiting the identification accuracy.…”

mentioning

confidence: 99%

High-precision automatic identification method for dicentric chromosome images using two-stage convolutional neural network

Shen

Li³

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Dicentric chromosome analysis is the gold standard for biological dose assessment. To enhance the efficiency of biological dose assessment in large-scale radiation catastrophes, automatic identification of dicentric chromosome images is a promising and objective method. In this paper, an automatic identification method for dicentric chromosome images using two-stage convolutional neural network is proposed based on Giemsa-stained automatic microscopic imaging. To automatically segment the adhesive chromosome masses, a k-means based adaptive image segmentation and watershed segmentation algorithm is applied. The first-stage CNN is used to identify the dicentric chromosome images from all the images and the second-stage CNN works to specifically identify the dicentric chromosome images. This two-stage CNN identification method can effectively detects chromosome images with concealed centromeres, poorly expanded and long-armed entangled chromosomes, and tricentric chromosomes. The novel two-stage CNN method has a chromosome identification accuracy of 99.4%, a sensitivity of 85.8% sensitivity, and a specificity of 99.6%, effectively reducing the false positive rate of dicentric chromosome. The analysis speed of this automatic identification method can be 20 times quicker than manual detection, providing a valuable reference for other image identification situations with small target rates.

show abstract

Watershed and Clustering Based Segmentation of Chromosome Images

Cited by 12 publications

References 4 publications

Automated human chromosome segmentation and feature extraction: Current trends and prospects

Automated human chromosome segmentation and feature extraction: Current trends and prospects

Segmentation of fish chromosomes in microscopy images: A new dataset

High-precision automatic identification method for dicentric chromosome images using two-stage convolutional neural network

Contact Info

Product

Resources

About