EP1908009A2 - Procede et dispositif de segmentations de zones - Google Patents

Procede et dispositif de segmentations de zones

Info

Publication number
EP1908009A2
EP1908009A2 EP06760799A EP06760799A EP1908009A2 EP 1908009 A2 EP1908009 A2 EP 1908009A2 EP 06760799 A EP06760799 A EP 06760799A EP 06760799 A EP06760799 A EP 06760799A EP 1908009 A2 EP1908009 A2 EP 1908009A2
Authority
EP
European Patent Office
Prior art keywords
mask
data
filter
values
input data
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP06760799A
Other languages
German (de)
English (en)
Inventor
Rupert Ecker
Georg E. Steiner
Vincent Laurain
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
TISSUEGNOSTICS GmbH
Original Assignee
TISSUEGNOSTICS GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by TISSUEGNOSTICS GmbH filed Critical TISSUEGNOSTICS GmbH
Publication of EP1908009A2 publication Critical patent/EP1908009A2/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • G06V20/695—Preprocessing, e.g. image segmentation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/187—Segmentation; Edge detection involving region growing; involving region merging; involving connected component labelling
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/194—Segmentation; Edge detection involving foreground-background segmentation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10056—Microscopic image
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details
    • G06T2207/20156—Automatic seed setting
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30024—Cell structures in vitro; Tissue sections in vitro

Definitions

  • the invention relates to a method according to the preamble of claim 1. Furthermore, the invention relates to a device for carrying out this method according to the preamble of claim 8 and a computer program product.
  • the aim of the invention is to segment the locally limited areas appearing in such images, in particular brightness ranges, in order to have the recorded images or images as well as possible or true to nature or to make them available for further evaluation.
  • a single-stage dilation is applied to the determined segmentation starting points or that discrimination of the gray values or the gray value mask during labeling results in discrimination with a predetermined gray value.
  • a data distribution diagram or a histogram, in particular a brightness histogram of the input data is created for separating the input data into bright foreground data and dark background data.
  • the difference quotient of the smoothed diagram or histogram and the absolute values of the difference quotients are calculated and if necessary a constant value is added.
  • the data obtained are with the Input data correlated, in particular multiplied, and possibly rescaled.
  • the threshold value chosen is the value at which the function obtained adopts a specific, predetermined limit value, in particular the value zero, or has approached this value.
  • the method according to the invention is used particularly advantageously when images or images of cell nuclei or cell membrane agglomerates dyed dark with respect to other cell areas or against the cytoplasm or colored stained nuclei or cell nuclei agglomerates contained in tissue sections are to be segmented. In the following the invention will be explained in more detail with reference to the drawing.
  • Fig. 1 shows an input data set or the recorded areas.
  • FIG. 2 shows a foreground mask or the foreground data.
  • Fig. 3 shows the mask of the segmentation start points.
  • Fig. 4 shows an input data set, Laplace and Gaussian filtered input data and a less restrictive mask.
  • Fig. 5 shows Laplacian and Gaussian filtered input data, a less restrictive mask and a restrictive mask.
  • Figures 6a, 6b, 6c and 6d show a less restrictive mask, a labeled, less restrictive mask, laplaced filtered image data and a restrictive mask.
  • Fig. 7 shows the mask of the input data and the segmentation mask.
  • Fig. 8 shows the procedure for the dilation of the segmentation start points.
  • Fig. 9 shows the flow chart concerning the determination of the threshold value for separating the foreground data from the background data.
  • FIG. 10 shows a device for carrying out the method according to the invention.
  • an image is recorded by means of an image acquisition unit and a corresponding set of digital input data made available.
  • the input data are separated by specifying a brightness threshold value into a foreground mask containing correspondingly high brightness values and a background mask containing lower values.
  • the dark background data is no longer used.
  • the input data are fed to a filter unit 4, in which a correlation, in particular multiplication, of the input data with the foreground mask takes place.
  • a correlation could also be a weighted addition of the data or values or another Type of linkage.
  • the output data of the filter unit 4 are fed to two different filter units, preferably on the one hand to a Gaussian filter 5 and on the other hand to a local maximum filter 6. Instead of a Gaussian filter, especially low-pass filters or filters that reduce sharp contours come into consideration.
  • the output data of the two filters are supplied to a subtraction unit 7, in which the two data sets resulting from the respective filterings are subtracted and which have a threshold value, in particular the value zero approximate data or filter areas at the output of the subtraction unit are provided as segmentation start points.
  • a threshold value in particular the value zero approximate data or filter areas at the output of the subtraction unit are provided as segmentation start points.
  • an edge-detecting filter preferably a high-pass filter, in particular a Laplace filter 9, is provided for the input data, to which at most a median filter and / or Gaussian filter 8 is connected upstream.
  • the edge-detecting filter in the present case a Laplace filter 9, is followed by a threshold value generator 10, in which the data obtained by the filter 9 are subjected to a threshold value setting with a specific threshold value, in particular zero.
  • the zero crossing of the values is an indication of the gradient maximum, so that the best possible differences to neighboring regions can be obtained.
  • the data set obtained with the threshold value value is correlated, in particular multiplied, with the foreground mask, whereby a less restrictive mask is obtained.
  • a labeling unit 12 is connected to the output of the correlator or multiplier 11, in which the outputs of the multiplier 11 corresponding to the less restrictive mask are made with respect to the determination of the local gray values of each of the locally limited areas or clusters and the average gray values of the respective areas are determined.
  • the obtained gray values in each cluster undergo discrimination with a predetermined threshold. This gives a restrictive mask.
  • the data set corresponding to the less restrictive mask is fed to a comparator 13, which is connected downstream of the subtraction unit 7.
  • the segmentation starting points not included in the less restrictive mask are eliminated.
  • a growing unit 14 growth takes place starting from the individual segmentation starting points in the region of the restrictive mask and at most one following growth of these areas in the less restrictive mask and optionally subsequently in the foreground mask, wherein the data sets concerning the restrictive mask are supplied by the labeling unit 12 and the data sets concerning the less restrictive mask are supplied by the multiplier 11 and the discriminator 3, respectively, to the growing unit 14.
  • a memory unit and / or evaluation and / or display unit 15 is connected.
  • a device according to the invention results in a relatively low computational effort to improve the results obtained when the features of claim 10 are provided.
  • cell preparations in particular cell sections, are stained in order to identify the cell nuclei differently from the remaining cell components.
  • the cell nuclei or the nuclear agglomerates have a different coloration to the remaining cell constituents or the image background or other constituents contained in the tissue section, and can be localized in the recorded image.
  • the image there is the uncertainty that in cell nuclear agglomerates the cell nuclei are covered or not reproduced in their entirety, or the brightness ranges do not have sharp boundaries or brightness values that are not easily distinguishable, ie. Color values or gray values are present. This should be counteracted according to the invention. From the image area to be examined of a tissue preparation or
  • Tissue sections 1 are placed over the inserted receiving unit 2, e.g. a video camera or a microscope, received digital input data.
  • the input data of the image area in a light foreground data containing foreground mask and a dark background data containing background mask in the discriminator 3 are separated.
  • Fig. 1 shows an example of an input data set.
  • FIG. 2 shows a mask with which the background of the input data can be separated from the foreground or shows the input data record after discrimination with the predefined or computationally determined brightness threshold value.
  • the foreground data or the foreground mask can be correlated in advance with the input data, in particular multiplied.
  • two different filters eg both a Gaussian filter and a local maximum filter, are applied next to one another in the filter unit 4.
  • the filter size of the local maximum filter is advantageously half the size of the Gaussian filter previously applied to the data.
  • the data sets resulting from these two filters are subtracted in the subtraction unit 7 and the image areas fulfilling a threshold criterion, in particular those having the value zero, are regarded or selected as segmentation starting points.
  • the mask or the data record of the segmentation starting points is shown for example in FIG. 3.
  • the segmentation starting points lie within the bright areas of the foreground mask, and the segmentation starting points correspond to the bright areas of the background ground mask.
  • an edge-detecting filter in particular the Laplace filter 9 is applied to the input data corresponding to FIG. 1, if appropriate after median filtering and / or Gaussian filtering.
  • the data obtained, in particular lap data is subjected to a threshold value setting, in particular with a threshold value of zero, and the data set thus obtained is correlated, in particular multiplied, with the data of the foreground mask.
  • a threshold value setting in particular with a threshold value of zero
  • a function could be used to close holes in the mask.
  • the less restrictive mask is subjected to labeling with regard to the determination of the local gray values of each of the locally limited regions or clusters, and the average gray values of the respective regions are determined, whereupon the gray values obtained in each cluster undergo discrimination with a given threshold.
  • This threshold is calculated from the respective data average of the laplaced filtered data within each individual cluster of the less restrictive mask.
  • the cluster becomes a continuous white or bright area in the less restrictive mask.
  • FIG. 5 shows on the left the lap-laced and Gauss-filtered input data corresponding to the middle illustration of FIG. 4.
  • the less restrictive mask is shown in the middle in FIG.
  • the combination of the Laplacian and Gaussian filtered input data and the less restrictive mask results in the restrictive mask which is shown on the right in FIG.
  • each individual contiguous region in the less restrictive mask of FIG. 6 are assigned an ascending number. This process enables a subsequent, separate processing or analysis of each individual region.
  • Fig. 6b the label image is shown.
  • the individual regions are assigned ascending numbers, which in FIG. 6b depict gray values corresponding to the colors from dark blue (SP : ' Niern) to dark red (middle region); /
  • each eh .. * ie region of Fig. 6b is placed successively on the spatially corresponding, laplace filtered image data of Fig. 6c.
  • the mean value is calculated from the laplace filtered image data.
  • Each of these values then serves as a threshold for the respective region.
  • segmented regions shown on the right in Fig. 7 may subsequently be used, e.g. also be filtered interactively with a defined surface area, gray value or probability threshold, in order to improve their further processing or further evaluation.
  • the segmented input data is visually, e.g. printed on a screen or printed or possibly stored with the calculated parameters.
  • a background threshold value for separating the input data into a background and foreground area in the discriminator 3 is as shown in FIG. 9.
  • the background is assumed to be the dark image areas and the foreground the bright image areas.
  • Brightness histogram of the input data is shown in which the optimum range for the background threshold is marked in advance accordingly.
  • This threshold value is determined by means of a procedure explained in more detail with reference to FIG. 7 by analytical processing and processing of the brightness data distribution diagram, in particular histogram, of the input data.
  • a histogram in particular a brightness histogram, of the input data is calculated, the difference quotient of the smoothed histogram is calculated, the absolute values of the difference quotients are calculated and, if necessary, a constant value is added
  • Data are multiplied by the input data and, if necessary, rescaled, and the threshold value chosen is that value at which the function obtained is zero or has approached this value.
  • the background becomes as well as possible from the areas relevant to the segmentation, i. foreground or foreground data, separated.
  • 2 is the mask with which the background of the input data from the foreground can be separated, shown. For the further processing steps or the further segmentation then only the foreground area of the input data is relevant.
  • the inventive device can be realized to achieve high computing speeds with hardware components.
  • the filters used can be of various types.
  • the use of filters acting in the same way is readily possible.
  • the threshold values are selected according to the desired accuracy or the desired data separation and are to be specified.
  • the provided correlation methods may e.g. in a weighted addition of the data or in a multiplication, possibly also in a division or other combination of the data, exist, as long as a corresponding increased distinctness of the data with the correlation is achieved.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Multimedia (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)
  • Studio Circuits (AREA)
  • Editing Of Facsimile Originals (AREA)

Abstract

L'invention concerne un procédé pour segmenter des zones localement délimitées. Selon ladite invention, les données d'entrée obtenues de la zone d'image à examiner sont séparées en un masque de premier plan et un masque d'arrière-plan par spécification d'une valeur seuil. Pour déterminer des points de départ de segmentation, deux opérations de filtrage différentes sont appliquées aux données d'entrée ; les enregistrements qui en résultent sont soustraits et les zones d'image sont considérées comme des points de départ de segmentation. Pour établir un masque moins restrictif, une opération de filtrage est appliquée au données d'entrée et les données résultantes sont soumises à un abaissement de valeur seuil. Pour établir un masque restrictif, le masque moins restrictif est soumis à un étiquetage par rapport à la détermination des valeurs locales et les valeurs moyennes sont déterminées. Des points de départ de segmentation non contenus dans le masque moins restrictif sont éliminés. Une croissance dans les zones respectives du masque restrictive est mise en oeuvre et les zones obtenues dans le masque restrictif sont considérées comme les zones de luminosité segmentées.
EP06760799A 2005-07-26 2006-07-24 Procede et dispositif de segmentations de zones Withdrawn EP1908009A2 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
AT0125405A AT503459B1 (de) 2005-07-26 2005-07-26 Verfahren und einrichtung zur segmentierung von bereichen
PCT/AT2006/000312 WO2007012098A2 (fr) 2005-07-26 2006-07-24 Procede et dispositif de segmentations de zones

Publications (1)

Publication Number Publication Date
EP1908009A2 true EP1908009A2 (fr) 2008-04-09

Family

ID=37683684

Family Applications (1)

Application Number Title Priority Date Filing Date
EP06760799A Withdrawn EP1908009A2 (fr) 2005-07-26 2006-07-24 Procede et dispositif de segmentations de zones

Country Status (7)

Country Link
US (1) US8023737B2 (fr)
EP (1) EP1908009A2 (fr)
AT (1) AT503459B1 (fr)
AU (1) AU2006274475B2 (fr)
CA (1) CA2616011A1 (fr)
WO (1) WO2007012098A2 (fr)
ZA (1) ZA200800131B (fr)

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AT503459B1 (de) * 2005-07-26 2007-10-15 Tissuegnostics Gmbh Verfahren und einrichtung zur segmentierung von bereichen
US8184175B2 (en) * 2008-08-26 2012-05-22 Fpsi, Inc. System and method for detecting a camera
JP6019798B2 (ja) * 2012-06-22 2016-11-02 ソニー株式会社 情報処理装置、情報処理システム及び情報処理方法
EP2864467B1 (fr) 2012-06-22 2019-04-03 Leica Biosystems Nussloch GmbH Dispositif de transport d'échantillon de tissu de biopsie
JP6487320B2 (ja) 2012-06-22 2019-03-20 ライカ ビオズュステムス ヌスロッホ ゲーエムベーハー 組織サンプル容器及び方法
CA2845830C (fr) 2013-03-15 2020-10-27 Leica Biosystems Nussloch Gmbh Cassette de tissu a element retractable
CA2845832C (fr) 2013-03-15 2020-09-22 Leica Biosystems Nussloch Gmbh Cassette de tissu a element de sollicitation
US9052256B2 (en) 2013-03-15 2015-06-09 Leica Biosystems Nussloch Gmbh Method for processing and embedding tissue
TW201510934A (zh) 2013-09-13 2015-03-16 Novatek Microelectronics Corp 影像銳化方法與影像處理裝置
CN105243652B (zh) * 2015-11-19 2019-06-07 Tcl集团股份有限公司 图像降噪的方法及装置
JP6774813B2 (ja) * 2016-08-12 2020-10-28 日本電子株式会社 画像処理装置、画像処理方法、および分析装置
US10456113B2 (en) * 2016-09-08 2019-10-29 B-K Medical Aps Wall-to-wall vessel segmentation in US imaging using a combination of VFI data and US imaging data
WO2019025514A2 (fr) * 2017-08-04 2019-02-07 Ventana Medical Systems, Inc. Procédés et systèmes automatisés permettant de détecter des cellules dans des images d'échantillons colorées
CN114205583B (zh) * 2022-01-20 2024-01-16 深圳市瑞驰信息技术有限公司 一种基于h265的视频编码方法、系统及电子设备

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Also Published As

Publication number Publication date
WO2007012098A3 (fr) 2007-05-03
US8023737B2 (en) 2011-09-20
AU2006274475A1 (en) 2007-02-01
ZA200800131B (en) 2008-10-29
WO2007012098A2 (fr) 2007-02-01
AU2006274475B2 (en) 2010-10-21
CA2616011A1 (fr) 2007-02-01
AT503459A4 (de) 2007-10-15
US20080193014A1 (en) 2008-08-14
AT503459B1 (de) 2007-10-15

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