WO2001002800A2 - Method and multi-purpose imaging system for chromosomal analysis based on color and region information - Google Patents
Method and multi-purpose imaging system for chromosomal analysis based on color and region information Download PDFInfo
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Definitions
- the present invention relates to classification of chromosomes, and in particular to classification of multicolor images of combinatorially labeled probes. More particularly, the present invention relates to a multi-purpose imaging system for multicolor fluorescence in situ hybridization.
- Multicolor fluorescence in situ hybridization allows to identify the twenty-four different human chromosomes in a metaphase spread by the simultaneous hybridization of a set of chromosome-specific DNA probes, each labeled with a different combination of fluorescent dyes.
- MFISH has been shown to readily identify both simple and complex chromosomal abnormalities.
- the application of the new multicolor karyotyping techniques promises to revolutionize the analysis of complex karyotypes with broad applications in pre- and postnatal applications and tumor cytogenetics. Due to the limited cytogenetic resolution of any of the new 24 color hybridization techniques using chromosome specific painting probes hidden structural abnormalities as they frequently occur in apparently normal metaphases from leukaemia and mental retardation might be eluded.
- Multiplex-fluorescence in situ hybridization is a combinatorial staining technique for the simultaneous detection and discrimination of the 22 autosomes and sex chromosomes as well as smaller biological probes.
- Each chromosome (or probe) is labeled in a unique combination of colors, which allows the identification of every chromosome by its spectral composition.
- For unique combinatorial labeling of all 46 chromosomes at least 5 fluorochromes are needed.
- every pixel in the two- or three-dimensional image volume then contains, besides its spatial information, an additional spectral information.
- the dimension of the spectral information in every pixel depends on the number of fluorochromes and image acquisition technique used and is at least five as mentioned above. This information is used to classify every pixel according to its spectral components in order to detect the different biological probes where the labeling scheme of the different probes represents the 'ideal' classes which the pixels have to be classified to. For correct cytogenetic diagnosis a very high degree of correctly classified pixels is obviously necessary.
- Any classification algorithm has to take these variations into account, either in a preprocessing step followed by a subsequent classification procedure, or by using a classifier that depends on direction (albedo) rather than on intensity.
- Every pixel in the image volume has to be assigned to one of the possible classes, which are defined by the labeling scheme. Therefore a distance measure or classifier has to be chosen to assign the pixels to their closest or most probable classes.
- a classification algorithm for multicolor images that is solely based on intensity values in the greylevel images of the different color (spectral) channels could be as follows:
- a smoothing algorithm may be performed as a pre-processing step for every single image to compensate for the intensity variations within homogeneous regions of a chromosome.
- a threshold is either manually or automatically set for every single image by histogram analysis.
- Figure 1 shows the histogram of the FITC image of a multicolor labeled metaphase spread with a manually chosen threshold and the resulting image.
- every pixel is classified to the class which it has the closest (Euclidian) distance to, or additionally binary images could be created before classification.
- every pixel in the multicolor image can be regarded as a point in the n-dimensional Euclidian space where every axis corresponds to one of the colors used for labeling, and thus can be treated as a n-dimensional vector.
- this Euclidian space will be referred to as 'color space'.
- US- A-5 798 262 relates to a method for finding L internal reference vectors for classification of L chromosomes or portions of chromosomes of a cell, the L chromosomes or portions of chromosomes being painted with K different flourophores or combinations thereof, wherein K basic chromosomes or portions of chromosomes of the L chromosomes or portions of chromosomes are each painted with only one of the K different fiuorophores.
- the classification method according to US-A-5 798 262 comprises three techniques, namely (a) a multi- band collection device for measuring a spectral vector for each pixel; (b) a method for computing internal reference vectors for each chromosome (or portion thereof); and (c) classification of all pixels for all chromosomes using those internal reference vectors.
- these internal reference vectors are computed automatically from the data itself.
- US-A-5 798 262 internal reference vectors are computed by using at least one pixel for each chromosome, i.e., the chromosomes are classified on a pixel basis.
- the classification method according to US-A- 5 798 262 is performed in three steps.
- a set of internal reference vectors for the chromosomes are computed (so-called basic chromosomes; preferably those 5 chromosomes being only labelled by one fluorochrome).
- these reference vectors are computed by using at least one basic pixel from each such basic chromosome.
- those basic reference vectors are used to compute internal reference vectors for the remaining chromosome classes.
- the reference vectors are used for classification of ail pixels.
- the high 'background' results mainly from pixels that are labeled in combinations, which use at least one of the other two colors as well, which will be shown in one of the following sections, where the color space is again visualized after classification based on direction with excellent result, where only those points are plotted, which belong to chromosomes (or classes) that are only labeled in a combinatorial way with the fluorochromes (color space) shown.
- classification will be extended on small probes, the problem of focal shifts will be addressed and a regional approach based on direction as classification algorithm will be developed.
- the results of classification of this algorithm on MFISH images of aberrant metaphase spreads and teiomeres will be presented.
- a is a pixel/voxel/region and b one of the class-vectors, then a is assigned to the class-vector (probe) that it has the highest normalized projection length (smallest angle) with.
- Figure 3 shows the surface plots of the normalized scalar product for the class-vectors (128,0), (128,128) and (0,128).
- the class-vectors the pixels/voxels are classified to represent the differently labeled probes in the image volume. Therefore, classification results will be better, the better class-vectors represent the pixel/voxel/region sets of different probes.
- the labeling scheme only represents the optimal case, where no background, crosstalk and other sources of noise are present. Hence, the class-vectors have to be adjusted to the information content of the multicolor image.
- Figure 4 shows the classification result on the labeling scheme vectors and Figure 4a shows the color space of the first three fluorchromes used, with only those data points plotted, that have been classified to probes that are labeled only in these fluors.
- the present invention suggests an iterative approach.
- the advantage is, that it is dynamical and automated classification feasible.
- class-vectors correspond to the labeling scheme or are slightly modified, taking intensity differences of the spectral windows and background into account.
- start-vectors could be chosen as well.
- Figure 4b shows the improved classification after one iteration and Figure 4c again shows the color space of the first three fluorchromes used, with only those data points plotted, that have been classified to probes that are labeled only in these fluors.
- pre-processing can improve classification results based on direction, if the cluster structure is manipulated in such a way that a better conical clustering of the data points in color space is achieved. Two methods will be described here:
- Nonlinear an-isotropic diffusion filtering is an edge conserving smoothing filtering for noise reduction that is applied to every single color image. It has several control parameters, which have to be carefully adjusted to image content, in order to conserve as much image information of different scales as possible and remove noise at the same time.
- the effect on color space is denser clustering of the data points of different probes.
- spatial information smoothing filters can improve classification results, and may ease the separation of overiapping clusters in color space.
- Figure 6a and b show an example how clustering in color space is (slightly) enhanced by diffusion filtering.
- a direct way to manipulate the clustering in color space is the use of density gradient methods. This iterative algorithm forces points of a cluster to move together to form a denser one. However, it has not been implemented yet to present any results. There are a few control parameters that have to be set, and being an iterative approach, its effect on clusters in color space can be controlled and be much greater than filtering.
- Figures 12 and 13 demonstrate how overlaps of clusters are resolvable by use of spatial information.
- Figure 12a and 12b show a pixel classification image and its FITC, Cy3 and Cy7 color space of a multicolor labeled metaphase spread.
- Figure 13a and 13b show the classification result after application of the region color clustering algorithm, presented in a further section, and classification of region color vectors on the same class vectors used in figures 12a and 12b.
- Figure 13b clearly shows how the overlap between the blue (chr. 9) and yellow (chr. X) cluster is resolved, leading to correct classification, that is not feasible using only color information in a pixel based algorithm.
- the present invention provides a multi-purpose image analysis system which is designed for routine application in clinical diagnostics.
- the present invention provides a method which allows the fully automated analysis both for multicolour karyotyping and for the study of disease specific combinatorially labeled probes. The potential of this technique will be exemplified by the fully automated detection of cryptic translocations in leukaemias with apparently normal karyotypes.
- the pixel classification method is based on the direction, and is applicable to multicolor images of arbitrary dimension. No pre-processing of the images is required to achieve excellent classification results on well hybridized probes. However, as the results depend on image quality and thus on the distribution in color space, a further embodiment of this approach is disclosed that takes the spatial information into account and turns out to be more robust. It will be applicable for classification of chromosomes and small probes as well as detection of translocations in tumor cells simultaneously with very high accuracy.
- the classification method and system according to the present invention is based on region growing that takes spatial and color information into account. It tesselates the image volume into clusters of data points of similar color and spatial information. First, a background removal algorithm is preferably applied to speed up the algorithm and to get rid of regions of near zero intensity that are troublesome for direction. Then a first pixel/voxel/region is picked, if neighboring pixels are of similar direction, i.e., if the normalized projection is larger than a preset value (threshold), they are added, a color vector is calculated for these pixels, and the region is grown and the color vectors are until no neighboring pixels are found that fulfil the chosen similarity criteria.
- a preset value a preset value
- a color vector for this region is calculated based on the color information of the pixels/voxels with a weight that corresponds to its size.
- the next region starts with the next pixel found and is grown alike. Again a weighted region color vector is calculated. This is done iteratively until no more new regions can be found. Alternatively an updated color vector can be calculated during region process, which will lead to slightly larger regions and therefore fewer regions.
- the classification is performed on the weighted region color vectors in color space, instead of a classifying pixels or voxels.
- the similarity criteria of 'similar direction' has to be reasonably set. It is a value between zero and one, defining the threshold for a pixel to be added to a region.
- the cosine of the angle / between two vectors in color space which is the normalized projection of these two vectors, is calculated by their normalized scalar product (equation [1]).
- equation [1] the normalized scalar product
- two vectors can be regarded as of similar direction, if their normalized projection is larger than a predefined value, which corresponds to a certain angle in the color space.
- a small angle (a larger projection) will lead to a large number of small regions, which is a safe strategy, if merging of neighboring regions of different probes with similar color is to be avoided.
- a small projection threshold corresponding to a large angle will lead to a small number of larger regions.
- the adaptation of the class-vectors can either be performed prior to region color clustering on pixel/voxel/region basis, or after region growing on region color vectors.
- the behavior of the class-vectors during iteration region vectors is not yet fully investigated. Preliminary results showed no significant difference to adaptive iteration on pixels. This however, depends on the image set under study.
- n number of fluorochromes used in a multicolor experiment
- n fluorochromes are used and the highest number of simultaneous colors used for a probe is m
- the angle / is smallest between two vectors that differ only in one label, where one is labeled in m and the other in m-1 fluorochromes.
- the main limiting parameter in multicolor images is the number of fluorochromes used. 2.
- the combinatorial labeling scheme should be designed as such that angles between all label vectors are kept as large as possible (see examples 2 and 4 below).
- discrimination could be enhanced significantly in comparison to example 1 by using more fluorochromes, enabling a reduction of simultaneous labels in the labeling scheme.
- a normalized projection length is a similarity criteria in the region color algorithm presented above. It is set according to the smallest angle appearing in
- angle / t r es h ⁇ //2 would be chosen, corresponding to a projection length l>0.953. This angle has to be set as such that good balance between discrimination and number of regions is accomplished. If set too small, region information will be lost, leading to pixei/voxel/region based classification in the limit of thres - 0.
- a straightforward approach is to apply background correction, and optionally pre-processing algorithms to enhance image quality. Then a edge or intensity based segmentation algorithm is applied on every single color image, binary images are created and an overlay image of these is the classification result. For small probes, this method will yield very good results, if probe intensities are high and segmentation is performed carefully, because color information is reliable only for a small part of a segmented region, and the ring effect due to different segmented sizes of the same probe in different color images could easily be removed thereafter, if probes are well separated. Whatever kind of segmentation is performed, it is the essential step and therefore has to be done carefully to isolate the color information from background, which can be very high compared to information content.
- color information for classification of small probes can be desirable, even if color for small probes is highly distorted.
- An approach could be to apply edge or intensity based segmentation on the maximum intensity image of the different color images after noise reduction and application of smoothing filters. Color vectors for the different segmented regions are then calculated, if they are spatially well separated, which is often the case for small probes.
- Figure 9a shows a classification result of region color classification, where the region have been segmented, and a color vector has been calculated for each region.
- Figure 9b shows the data plot in color space in FITC, Cy3, Cy3.5 of all probes labeled only in these fluors. Obviously, a classification solely based on color information will not lead to correct classification.
- Light of different wavelength is focused into slightly different axial spots.
- the difference can be in the order of 100-200 nm.
- the point spread function which describes the three-dimensional intensity distribution in the focal spot is shifted. Hence different spot sizes are imaged for the different multicolor images.
- M-FISH is capable of identifying readily both simple and complex chromosomal abnormalities and has by now widely been used in pre- and postnatal applications and tumor cytogenetics.
- DEAC, FITC, Cy3, Cy3.5, Cy5, Cy5.5 and Cy7 are used for the labeling and DAPI serves as counterstain. In theory this would allow 127 different combinations. More important is that for 24-color karyotyping the use of seven dyes reduces probe complexity because triple combinations of fluors can be avoided and image analysis is facilitated.
- chromosome-specific multicolor bar codes consisting of multiple combinatorially labeled YAC clones for chromosomes 2, 3, 5, 7, 8, 9, 12, 15, 17, and 20.
- the above described imaging system was applied for an automated evaluation of the YAC signals.
- Fig. 11 demonstrates that in particular in tumor cytogenetics bar codes have an increased potential to unravel complex intrachromosomal rearrangements and are invaluable for accurate break-point mapping.
- Probes were combinatorially labelled using four fluorochromes, with both the short (p) and long (q) arms of each chromosome having the same labelling combination. This allows the identification of 12 pairs of chromosomes in one hybridisation, with a full survey of all telomeric regions possible in only two hybridizations.
- Fig. 9 shows that it is now possible to accurately analyze such metaphases in a fully automated way.
- the methods described here for classification of multi color images are not restricted to two-dimensional images. They can be applied to three-dimensional multicolor images in the same way. Three-dimensional multicolor images of interphase chromosomes in a cell nucleus are not as dense as metaphase chromosomes. This will lead to more diffuse clusters in the color space. However, appropriate filtering, segmentation and application of density gradient functions in combination with region color clustering and classification on color direction, are together powerful set of tools to overcome obstacles in classification of three- dimensional multicolor images of interphase probes.
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Cited By (2)
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CN113378691A (en) * | 2021-06-08 | 2021-09-10 | 湖北简图网络科技有限公司 | Intelligent home management system and method based on real-time user behavior analysis |
CN113378691B (en) * | 2021-06-08 | 2024-05-17 | 衡阳览众科技有限公司 | Intelligent home management system and method based on real-time user behavior analysis |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
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US5798262A (en) * | 1991-02-22 | 1998-08-25 | Applied Spectral Imaging Ltd. | Method for chromosomes classification |
US5817462A (en) * | 1995-02-21 | 1998-10-06 | Applied Spectral Imaging | Method for simultaneous detection of multiple fluorophores for in situ hybridization and multicolor chromosome painting and banding |
US5880473A (en) * | 1997-07-28 | 1999-03-09 | Applied Imaging, Inc. | Multifluor-fluorescence in-situ hybridization (M-FISH) imaging techniques using multiple multiband filters with image registration |
-
2000
- 2000-06-30 WO PCT/EP2000/006138 patent/WO2001002800A2/en active Application Filing
- 2000-06-30 AU AU62668/00A patent/AU6266800A/en not_active Abandoned
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Publication number | Priority date | Publication date | Assignee | Title |
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US5798262A (en) * | 1991-02-22 | 1998-08-25 | Applied Spectral Imaging Ltd. | Method for chromosomes classification |
US5817462A (en) * | 1995-02-21 | 1998-10-06 | Applied Spectral Imaging | Method for simultaneous detection of multiple fluorophores for in situ hybridization and multicolor chromosome painting and banding |
US5880473A (en) * | 1997-07-28 | 1999-03-09 | Applied Imaging, Inc. | Multifluor-fluorescence in-situ hybridization (M-FISH) imaging techniques using multiple multiband filters with image registration |
Non-Patent Citations (1)
Title |
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SCHROECK E ET AL: "MULTICOLOR SPECTRAL KARYOTYPING OF HUMAN CHROMOSOMES" SCIENCE,AMERICAN ASSOCIATION FOR THE ADVANCEMENT OF SCIENCE,,US, vol. 273, 26 July 1996 (1996-07-26), pages 494-497, XP000952836 ISSN: 0036-8075 * |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
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CN113378691A (en) * | 2021-06-08 | 2021-09-10 | 湖北简图网络科技有限公司 | Intelligent home management system and method based on real-time user behavior analysis |
CN113378691B (en) * | 2021-06-08 | 2024-05-17 | 衡阳览众科技有限公司 | Intelligent home management system and method based on real-time user behavior analysis |
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AU6266800A (en) | 2001-01-22 |
WO2001002800A3 (en) | 2001-06-28 |
WO2001002800A9 (en) | 2002-09-06 |
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