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Breast carcinoma is after skin carcinoma the second most common form of cancer among women. Multicolour fluorescent in-situ hybridisation (m-FISH) is a common method for staging breast carcinoma. The interpretation of m-FISH images is complicated due to two effects: (i) Spectral overlap in the emission spectra of fluorochrome marked DNA probes and...
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... preparations were excitated with a metal halide lamp X-Cite 120PC (EXFO R , Canada). A high sensitivity 14 bit EMCCD camera iXon (Andor, Ireland) with a resolution of 1004 × 1002 pixels was used to acquire the images. The laboratory measurement setup is shown in fig. 4. The images were preprocessed using spatial two-point calibration to correct the offset and homogenise the sensitivity of each pixel. A dark image, obtained by closing the camera aperture, and a white reference, obtained by imaging a teflon white standard, were used for the calibration. This calibration model corrects the spatial and ...
Citations
... Algunas formas de evitar la autofluorescencia son mediante el empleo de FISH con otra técnica de detección (22), manipulando el sistema de filtros durante la visualización y sistemas que permitan amplificar la señal, o mediante el procesamiento y manejo digital de los espectros obtenidos en la imagen (49). ...
During these recent years, a large number of FISH technique applications have been reported. These techniques have been used in the detection of microorganisms in their own habitat without requiring their previous isolation and purification. The importance of FISH lies in the ability of the DNA probe to detect a specific region of the nucleic acid of microbial cells and to be visualized by epifluorescence microscopy. This review describes the various FISH uses ranging from the identification of the microbiota in aquatic environments and their use in bioremediation, to the detection of pathogens in clinical diagnosis. It also presents some limitations as well as the potential solutions to be applied when the FISH technique is used.
... Second, our system aims to acquire high-resolution FISH images under an image scanning mode, while the most existing systems work on acquisition of lowresolution images in a scanning mode and then acquisition of high-resolution images in a still image mode. Third, our system uses a multi-spectral imaging approach, while some of the other systems used a hyper-spectral imaging approach (in particular for those aimed to acquire images of multiplex fluorescence in-situ hybridization (M-FISH) images of metaphase chromosomes [22]). Although our approach has advantages, it also has disadvantages. ...
Fluorescence in situ hybridization (FISH) tests provide promising molecular imaging biomarkers to more accurately and reliably detect and diagnose cancers and genetic disorders. Since current manual FISH signal analysis is low-efficient and inconsistent, which limits its clinical utility, developing automated FISH image scanning systems and computer-aided detection (CAD) schemes has been attracting research interests. To acquire high-resolution FISH images in a multi-spectral scanning mode, a huge amount of image data with the stack of the multiple three-dimensional (3-D) image slices is generated from a single specimen. Automated preprocessing these scanned images to eliminate the non-useful and redundant data is important to make the automated FISH tests acceptable in clinical applications. In this study, a dual-detector fluorescence image scanning system was applied to scan four specimen slides with FISH-probed chromosome X. A CAD scheme was developed to detect analyzable interphase cells and map the multiple imaging slices recorded FISH-probed signals into the 2-D projection images. CAD scheme was then applied to each projection image to detect analyzable interphase cells using an adaptive multiple-threshold algorithm, identify FISH-probed signals using a top-hat transform, and compute the ratios between the normal and abnormal cells. To assess CAD performance, the FISH-probed signals were also independently visually detected by an observer. The Kappa coefficients for agreement between CAD and observer ranged from 0.69 to 1.0 in detecting/counting FISH signal spots in four testing samples. The study demonstrated the feasibility of automated FISH signal analysis that applying a CAD scheme to the automated generated 2-D projection images.
... Second, our system aims to acquire high-resolution FISH images under an image scanning mode, while the most existing systems work on acquisition of low-resolution images in a scanning mode and then acquisition of highresolution images in a still image mode. Third, our system uses a multi-spectral imaging approach, while some of the other systems used a hyper-spectral imaging approach (in particular for those aimed to acquire images of multiplex fluorescence in-situ hybridization (M-FISH) images of metaphase chromosomes [22]). Although our approach has advantages, it also has disadvantages. ...
Fluorescence in situ hybridization (FISH) tests provide promising molecular imaging biomarkers to more accurately and reliably detect and diagnose cancers and genetic disorders. Since current manual FISH signal analysis is low-efficient and inconsistent, which limits its clinical utility, developing automated FISH image scanning systems and computer-aided detection (CAD) schemes has been attracting research interests. To acquire high-resolution FISH images in a multi-spectral scanning mode, a huge amount of image data with the stack of the multiple three-dimensional (3-D) image slices is generated from a single specimen. Automated preprocessing these scanned images to eliminate the non-useful and redundant data is important to make the automated FISH tests acceptable in clinical applications. In this study, a dual-detector fluorescence image scanning system was applied to scan four specimen slides with FISH-probed chromosome X. A CAD scheme was developed to detect analyzable interphase cells and map the multiple imaging slices recorded FISH-probed signals into the 2-D projection images. CAD scheme was then applied to each projection image to detect analyzable interphase cells using an adaptive multiple-threshold algorithm, identify FISH-probed signals using a top-hat transform, and compute the ratios between the normal and abnormal cells. To assess CAD performance, the FISH-probed signals were also independently visually detected by an observer. The Kappa coefficients for agreement between CAD and observer ranged from 0.69 to 1.0 in detecting/counting FISH signal spots in four testing samples. The study demonstrated the feasibility of automated FISH signal analysis that applying a CAD scheme to the automated generated 2-D projection images.
... This dataset is extremely noisy in both spectral and spatial domains. The last dataset contains 64 channels of size 1002*1004 of MFISH (Multicolor Fluorescence In Situ Hybridization) [14]. Our goal is to evaluate the performance of edge detection result on extracting nuclei's edges from background. ...
Edge detection in hyperspectral images is an in- trinsic difficult problem as the gray value intensity images related to single spectral bands may show dif- ferent edges. The few existing approaches are either based on a straight forward combining of these in- dividual edge images, or on finding the outliers in a region segmentation. We propose as an alternative a clustering of all image pixels in a feature space con- structed by the spatial gradients in the spectral bands. An initial comparative study of various hyperspectral datasets shows the differences and properties of these approaches and makes clear that the proposal has in- teresting properties that may be studied further.
... A hyper-spectral imaging system measures the spectrum at each pixel in the image. The information content of these hyper-spectral images is higher than in standard color images enabling SU methods to unmix the overlapping emission spectra more effectively and reduce tissue auto-fluorescence by 55% [3]. This allows classification algorithms to count characteristic fluorescent signals more reliably and support experts in their diagnosis. ...
... To optimize the classification of the HER-2/neu signals and the CEP 17 signals spectral unmixing was used to reduce the effects of noise and autofluorescence. The used SU method reduces the percentage of ambiguously assigned pixels to 0.1% of the total pixels in an image [3]. This section gives an overview of the used spectral unmixing (SU) method. ...
Fluorescence microscopy plays an important role in the classification of cancerous tissue. Fluorescent in situ hybridization (FISH) is a common method for marking different cell components (e.g. nucleus, cytoplasm, proteins) as well as specific DNA positions or whole DNA sequences. The FISH method/variant used in this paper facilitates a special combination of filters (DAPI/Orange/Green) and corresponding fluorescent dyes to discriminate between the different parts of a cell. FITC marks chromosome 17, Spectrum Orange R marks HER-2/neu receptors and DAPI is used to coun-terstain the cell nucleus. Upon excitation, each marked chromosome emits a fluorescent signal (spot). A common problem with multi-color FISH (M-FISH) preparations is the crosstalk between the channels caused by the overlap of the emission spectra of the different fluorophores. This crosstalk is one of the reasons that the evaluation and classification of M-FISH preparations is difficult and requires ex-perienced experts. The crosstalk cannot be resolved on the filter level (excitation/emission), and not by specialized fluorophores (which have different emission spectra). However, the crosstalk can be eliminated if spectral imaging techniques are used to acquire hyper-spectral image data of M-FISH preparations and employ spectral unmixing methods to "algorithmically reduce" the spectral overlap of the emission spectra. Spectral imaging is the combination of computer vision and spectroscopy. And due to the fact that every object of interest consists of more than one pixel, every pixel is dependent on its neighboring pixels. Thus the spatial context of the image contains useful information for a classification and increase the sensitivity and specificity of a spectral classification. Keywords—spectral imaging (SI), fluorescence microscopy, fluo-rescent in situ hybridization (FISH), contextual classification.
