Automatic segmentation of chromosomes in Q-band images

Grisan, Enrico; Poletti, Enea; Tomelleri, C.; Ruggeri, Alfredo

doi:10.1109/iembs.2007.4353594

Cited by 16 publications

(14 citation statements)

References 16 publications

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“…2 (a). Additionally, we tested our algorithm for chromosomes from a high quality dataset from Ruggeri et al, [4] the results of geometrical correction have been shown, Fig. 3 (c).…”

Section: Resultsmentioning

confidence: 97%

“…Use of MAT [1], [6]- [7] and infinite thinning [8] has been previously used to obtain a medial axis to correct the shape of the chromosome. Different methods of geometric correction using vessel-tracking algorithm [4], and by segmenting the chromosome into polygons have also been proposed [3]. Most of these algorithms obtain an initial guess and extrapolate it to obtain the medial axis, which is then used for geometric correction.…”

Section: Introductionmentioning

confidence: 99%

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Geometric correction of deformed chromosomes for automatic Karyotyping

Khan

DSouza

Sanches

et al. 2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Automatic Karyotyping is the process of classifying chromosomes from an unordered karyogram into their respective classes to create an ordered karyogram. Automatic karyotyping algorithms typically perform geometrical correction of deformed chromosomes for feature extraction; these features are used by classifier algorithms for classifying the chromosomes. Karyograms of bone marrow cells are known to have poor image quality. An example of such karyograms is the Lisbon-K(1) (LK(1)) dataset that is used in our work. Thus, to correct the geometrical deformation of chromosomes from LK(1), a robust method to obtain the medial axis of the chromosome was necessary. To address this problem, we developed an algorithm that uses the seed points to make a primary prediction. Subsequently, the algorithm computes the distance of boundary from the predicted point, and the gradients at algorithm-specified points on the boundary to compute two auxiliary predictions. Primary prediction is then corrected using auxiliary predictions, and a final prediction is obtained to be included in the seed region. A medial axis is obtained this way, which is further used for geometrical correction of the chromosomes. This algorithm was found capable of correcting geometrical deformations in even highly distorted chromosomes with forked ends.

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“…2 (a). Additionally, we tested our algorithm for chromosomes from a high quality dataset from Ruggeri et al, [4] the results of geometrical correction have been shown, Fig. 3 (c).…”

Section: Resultsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Geometric correction of deformed chromosomes for automatic Karyotyping

Khan

DSouza

Sanches

et al. 2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

show abstract

“…Metaphase is the stage of cell division at which the chromosomes are most suitable for analysis. [5,9]. A normal human diploid cell contains 22 pairs of chromosomes, autosomes of classes 1-22 and 2 sex chromosomes, either XX or XY.…”

Section: Introductionmentioning

confidence: 99%

Semi automated segmentation of chromosomes in metaphase cells

Munot

Joshi

Mandhawkar

2012

IET Conference on Image Processing (IPR 2012)

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Image segmentation plays a crucial role in many medical imaging applications by automating or facilitating the delineation of anatomical structures and other regions of interest. Since the birth of the automated karyotyping systems by the aid of computers, building a fully automated chromosome analysis system has been an ultimate goal. Along with many other challenges, accurate segmentation of the chromosomes has been a major challenge especially due to the non rigid nature of the chromosomes. The earlier reported approaches for the segmentation have limited success as they are sensitive to scale variation, experimented only on gray images, unable to segment the clusters and the highly bent chromosomes. This work, describes an effective approach of segmentation of chromosomes in Metaphase images using Random Walker Algorithm [RWA] which is yet unexplored and not reported in the literature. The efforts are also done to compare the results with traditional methods so as to prove the efficiency of the implemented RWA algorithm. The algorithm is tested on publically available database and has shown encouraging and acceptable results.

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“…Metaphase is the stage of cell division at which the chromosomes are most suitable for analysis. [2,3,5]. A normal human diploid cell contains 22 pairs of chromosomes, autosomes of classes 1-22 and 2 sex chromosomes, either XX or XY.…”

Section: Introductionmentioning

confidence: 99%

Automated Karyotyping of Metaphase Cells with Touching Chromosomes

Munot¹,

Joshi²,

Sharma³

2011

IJCA

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Since the birth of the automated karyotyping systems by the aid of computers, building a fully automated chromosome analysis system has been an ultimate goal. Along with many other challenges, automating chromosome classification and segmentation has been a major challenge especially due to overlapping and touching chromosomes. The earlier reported methods have limited success as they are sensitive to scale variations, computationally complex, use only color information in case of multispectral imaging and had challenges in segmentation. The proposed technique addresses the challenge of separating the touching chromosomes using initially the modified snake algorithm to disentangle the cluster of touching chromosomes from the metaphase image and then a greedy approach based on combinatorial computational geometry of the pixels on the boundary of the cluster is used to identify and resolve the set of touching chromosomes. Contribution and novelty of this work lies in the ability of the algorithm to successfully separate the clusters of any number of touching chromosomes. System performance was tested and analyzed using a variety of metaphase images exhibiting various levels of touching chromosomes giving an overall accuracy of 100 % for resolving the cluster with 2 touching chromosomes and 95 % for separating a cluster of 3, 4 touches. The overall time was 2.4 seconds.

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Automatic segmentation of chromosomes in Q-band images

Cited by 16 publications

References 16 publications

Geometric correction of deformed chromosomes for automatic Karyotyping

Geometric correction of deformed chromosomes for automatic Karyotyping

Semi automated segmentation of chromosomes in metaphase cells

Automated Karyotyping of Metaphase Cells with Touching Chromosomes

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