Entropy estimation for segmentation of multi-spectral chromosome images

Schwartzkopf, W.; Evans, B.L.; Bovik, Alan C.

doi:10.1109/iai.2002.999924

Cited by 18 publications

(17 citation statements)

References 14 publications

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“…No chromosome classification method was proposed, and thus classification information could not be used to aid in segmentation. The entropy approach was extended to use entropy estimation for application directly to M-FISH data [47], but it achieved little success.…”

Section: Analysis Of M-fish Imagesmentioning

confidence: 99%

Maximum-likelihood techniques for joint segmentation-classification of multispectral chromosome images

Schwartzkopf

Bovik

Evans

2005

IEEE Trans. Med. Imaging

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The purpose of the research described in this dissertation is to develop new methods for automatic chromosome identification. In particular, I (1) develop a maximum likelihood hypothesis test that uses this multi-spectral information, together with conventional criteria, to select the best segmentation possibility, (2) use this likelihood function to combine chromosome segmentation and classification into a robust chromosome identification system, and (3) show that the proposed likelihood function can also be used as a reliable indicator of errors in segmentation, errors in classification, and the chromosomes anomalies that can be diagnosed with M-FISH imaging. I show that the proposed multispectral joint segmentation-classification method outperforms past grayscale segmentation methods in decomposing touching chromosomes. Furthermore, I show that it outperforms past M-FISH classification techniques that do not use segmentation information. OBJECTIVESThis work investigated what improvements in chromosome analysis could be obtained by using M-FISH multi-spectral images. One aim of this work was to develop algorithms that could take full advantage of the multi-spectral information and to quantify their improvement over grayscale chromosome image analysis methods. In order to achieve effective segmentation and classification, I studied how to combine segmentation and classification to make both more accurate. Furthermore, this work examined how to use this multi-spectral information to detect automatically abnormalities in the chromosomes that could not be detected without such chromosome labeling. CONTRIBUTIONSThe best approach for segmentation and classification of multi-spectral chromosome images is fundamentally different from the best approach for grayscale chromosome images. The additional information provided by multispectral chromosome images is useful for distinguishing touching and 2. This maximum likelihood test is used to propose a method that combines the task of locating and classifying chromosomes for improved performance in both tasks.3. The first two contributions in the dissertation are then used to achieve aberration scoring; that is, giving a score to each segment to indicate the likelihood of abnormalities in that image. 4 NOTATIONThis section gives the mathematical notation that is followed throughout the dissertation. I denote vectors with boldface and scalars with plain font. I use the function ( ) ⋅ p to denote a probability. Further, the functiondenotes the usual Gaussian probability density function. Thus the probability density function of a random vector x , with mean µ and covariance matrix Σ , is ( ) ORGANIZATIONThe structure of this dissertation is as follows. Chapter 2 introduces the features of chromosomes and chromosomes images and shows how these features CHROMOSOMESChromosomes are the body's information carriers. They are the structures that contain genes, which store in strings of DNA all of the data necessary for an organism's development and maintenance. Chromosome...

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Section: Analysis Of M-fish Imagesmentioning

confidence: 99%

Maximum-likelihood techniques for joint segmentation-classification of multispectral chromosome images

Schwartzkopf

Bovik

Evans

2005

IEEE Trans. Med. Imaging

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“…However, it wasnotappliedtochromosomesegmentation,which is also of great significance to the chromosome analysis. Chromosome segmentation previously focused on isolation of individual chromosomes, which was at low resolution (Liang, 1989;Agam and Dinstein, 1997;Schwartzkopf et al, 2001Schwartzkopf et al, , 2002, whereas segmentation of G bands, some special components of the chromosomes, was a high-resolution trial. It will extract feature parameters in the chromosomes more accurately, which substantially improve classification performance (Piper and Granum, 1989).…”

Section: Introductionmentioning

confidence: 99%

Image segmentation of G bands of Triticum monococcum chromosomes based on the model-based neural network

Cai

Xiong

et al. 2004

Pattern Recognition Letters

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“…The visual inspection is thus tedious, time consuming, laborious and an expensive procedure. Hence, many attempts have been made to automate the process of karyotyping [5,10]. Automated Karyotyping systems allows countless clinical advantages such as interactive and graphical environment, faster in the accomplishment of the samples, allowing quality printing, being self explanatory, better interpretation of the image, and it still makes possible the storage of the information in a database for future analysis [15].…”

Section: Introductionmentioning

confidence: 99%

“…Colour information is itself sufficient for the segmentation of the chromosomes but when only colours are used, touchings and overlaps of the same kinds of chromosomes cannot be segmented and segmentation accuracy highly relies on initial pixel classification. The maximum likelihood decomposition methods for MFISH had limited success in separation of touching chromosomes [9][10][11][12].…”

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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Entropy estimation for segmentation of multi-spectral chromosome images

Abstract: In the early 1990s, the state-of-the-art

Cited by 18 publications

References 14 publications

Maximum-likelihood techniques for joint segmentation-classification of multispectral chromosome images

Maximum-likelihood techniques for joint segmentation-classification of multispectral chromosome images

Image segmentation of G bands of Triticum monococcum chromosomes based on the model-based neural network

Automated Karyotyping of Metaphase Cells with Touching Chromosomes

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