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WO2016091016A1 - Procédé basé sur une transformation décisive de marqueur de noyau pour séparer des globules blancs collés - Google Patents

Procédé basé sur une transformation décisive de marqueur de noyau pour séparer des globules blancs collés Download PDF

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Publication number
WO2016091016A1
WO2016091016A1 PCT/CN2015/092590 CN2015092590W WO2016091016A1 WO 2016091016 A1 WO2016091016 A1 WO 2016091016A1 CN 2015092590 W CN2015092590 W CN 2015092590W WO 2016091016 A1 WO2016091016 A1 WO 2016091016A1
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Prior art keywords
white blood
blood cell
image
binary image
cell
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PCT/CN2015/092590
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English (en)
Chinese (zh)
Inventor
刘治
刘晶
郑成云
李晓梅
肖晓燕
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山东大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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; CALCULATING OR 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
    • G06T2207/20152—Watershed segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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 belongs to the field of biomedical engineering, and particularly relates to a method for blocking adhesion white blood cells based on a nuclear transformation watershed transformation.
  • the examination of white blood cells is an important part of clinical tests.
  • the inflammation or other diseases in the body can cause the total number of white blood cells and the percentage of various white blood cells to change. Therefore, checking the total number of white blood cells and white blood cell classification counts is an important method for assisting diagnosis.
  • the cell image analysis and recognition system has been studied in recent years. Its main task is to segment the collected image by automatic analysis, segment the individual cells, calculate the relevant characteristic parameters of individual cells, and identify the number of different cells.
  • white blood cell segmentation directly affects the results of the next steps of cell feature extraction and classification.
  • white blood cell segmentation is the most challenging step.
  • image segmentation algorithms based on multi-spectral techniques
  • image segmentation algorithms based on color models, commonly used color models are RGB, HSI, CMYK, etc.
  • Mathematical morphology algorithm image segmentation such as the snake algorithm to segment the cytoplasm on the basis of the nucleus, the traditional watershed algorithm to solve the cell adhesion problem
  • image segmentation algorithm based on fuzzy mathematics, such as fuzzy C-means algorithm, K-means clustering segmentation of white blood cells Wait.
  • fuzzy C-means algorithm K-means clustering segmentation of white blood cells Wait.
  • the splitting process takes a long time.
  • the segmented image is limited by the white blood cell bank.
  • the object of the present invention is to overcome the above-mentioned deficiencies of the prior art, and to provide a method for blocking leukocyte differentiation based on nuclear marker watershed transformation, which solves the problem of peripheral blood adhesion leukocyte segmentation.
  • the algorithm is simple and time-consuming, and it has good robustness to different cell bank different forms of adhesion leukocyte segmentation and precise segmentation of white blood cell nuclei.
  • a method for segmentation of adhesion white blood cells based on watershed transformation of nuclear markers comprising the following steps:
  • the specific method of the step (1) is: observing the grayscale image in the matlab, determining the gray value of the white blood cell nucleus, the red blood cell and the background; calculating the global threshold T of the gray histogram by the graythresh function, and then using the im2bw function pair
  • the grayscale image is thresholded to obtain the binogram of white blood cells and red blood cells.
  • the gray histogram is analyzed.
  • the fraction of the white blood cells is segmented by the im2bw function threshold to obtain the white matter nuclei only.
  • Binary image I analyzing the B component image, analyzing the pixel values of the red blood cell and white blood cell nuclear color in the image, and obtaining the binary image III containing only the white blood cell core and the red blood cell region by global threshold segmentation technology;
  • step (5) The specific method of the step (5) is:
  • the target cell nucleus is determined to be a nucleus nucleus, wherein the area is represented by the number of pixels in the white blood cell area; at this time, the centroid coordinate position of the two target nucleus is obtained, and the two targets are Perform centroid-linked operation to form a nuclear nucleus of the lobulated nucleus as an internal seed point;
  • step (7) The specific method of the step (7) is:
  • the target cell disappears, the cell is not treated; if the target number is increased, the multiple targets after corrosion are used as a new internal seed point to perform a watershed transformation based on distance transformation, and the watershed ridge is displayed on the target adhesion cell, adhesion
  • the cells can be separated;
  • the method is simple to operate, and it takes a short time.
  • the watershed transformation based on the inner nucleus of the nucleus is proposed, which avoids the occurrence of over-segmentation and improves the stability of the watershed transformation adhesion segmentation.
  • a new method for segmentation of peripheral blood adhesion leukocytes is proposed.
  • the algorithm has high segmentation precision and strong stability, which is superior to traditional algorithms.
  • Figure 1 is a flow chart of peripheral blood adhesion leukocyte segmentation system
  • Figure 2 shows images of peripheral blood cells from two different illuminations
  • FIG. 3 shows the white matter nuclear I segmentation binary map
  • FIG. 4 shows the effect of white blood cell and red blood cell II segmentation
  • Figure 5 shows the effect of white blood cell nucleus and red blood cell III segmentation
  • FIG. 6 shows the red blood cell IV segmentation effect
  • Figure 8 shows the segmented nuclear X-value map
  • Figure 9 shows the internal seed point VI binary map
  • Figure 10 shows the preliminary segmentation of the white blood cell Y binary map.
  • FIG 11 shows the watershed ridge obtained by X
  • FIG. 12 shows the result of the first watershed transformation VII-1
  • FIG. 13 shows the result of the first watershed transformation VII-2
  • FIG. 14 shows the separation of the precise white blood cell core Z1
  • FIG. 15 shows the separation of precise white blood cells Z2
  • a specific implementation process of a peripheral blood adhesion white blood cell image segmentation algorithm based on nuclear labeling is as follows:
  • white blood cell recognition medical experts usually distinguish white blood cells and red blood cells according to their characteristics such as color and shape, and discriminate white blood cell types based on information such as texture and space.
  • a multi-user peripheral blood cell formation cell bank was collected, and the white blood cells were segmented from the perspective of color and space. The characteristics of the color channel of the cell bank image were analyzed.
  • White space is precisely segmented by color space and morphological manipulation.
  • Adhesion problems present in leukocytes can be accurately and stably differentiated by leukocyte adhesion by improved nuclear marker-based watershed transformation. The algorithm is simple and easy to operate, and can effectively solve the problem of cell adhesion.
  • the cell images under different illuminations of different cell banks are shown in Fig. 2, which has higher segmentation rate and good robustness.
  • the white blood cell binarized images may have some red blood cells and other impurities or cell adhesions, but the number and morphology of white blood cells remain intact, there is no problem of leukocyte missing or white blood cell defects, and inaccurate white blood cell segmentation. It is realized by binarized image subtraction technology.
  • the segmentation method is: firstly, the original RGB color image is converted into a gray space with a pixel range of 0 to 1. By setting different thresholds, the threshold value of the gray image is 0.5 and the threshold segmentation based on the ostu adaptive threshold segmentation technique is respectively performed.
  • the ostu-based adaptive threshold segmentation can be used to obtain the binary image of the white blood cell nucleus and the red blood cell III, as shown in Fig. 5.
  • the image of erythrocyte IV can be obtained by subtracting the white blood cell nuclear map I from the binary image of the white blood cell nucleus and the red blood cell.
  • the white blood cell and red blood cell binary map II minus the red blood cell binary map V can obtain an inaccurate white blood cell binary image V, as shown in FIG.
  • the internal seeds are also obtained from the nuclear nuclei.
  • the cell nucleus is taken as an internal seed to determine the number of white blood cells and to resolve the adhesion problems of white blood cells.
  • the method of obtaining the nucleus is to first convert the original RGB color image into the HSI space, and extract the G and S channel components of the two spaces respectively. Observe the G component and find that the white blood cell and platelet pixel values are small, and other components have larger pixels. Value, observe the S component can be found, white blood cells and platelets have larger pixel values, other components have smaller pixel values, normalize the two-channel components, and then subtract the pixel values to enhance the nuclear image. .
  • the binarization and morphological processing of the obtained enhanced image can obtain a nuclear binary image X, and the segmentation effect is shown in FIG.
  • the nucleus of the nucleus in the leukocyte is multinuclear
  • the centroid is connected to each other, and the multinucleus becomes a nucleus.
  • the number of white blood cells be determined, but also the internal seed of the watershed transformation.
  • Solve cell adhesion problems For the adhesion of leukocyte nucleus in leukocytes, a flat disc structural element with a radius of 1 is created, and a morphological corrosion operation is performed on the target nuclei to obtain a seed point of the adherent nuclei.
  • the internal seed image obtained by the method is as shown in FIG.
  • a cell nucleus corresponds to a white blood cell
  • an internal seed binary image VI determines the number of white blood cells.
  • a large area of impurity such as excess red blood cells in the image V can be removed.
  • the logical and subsequent images of the two are used as the marker images, and the inaccurate white blood cell image V is used as a mask to perform a morphological reconstruction operation to obtain an accurate white blood cell binarization map.
  • Y is the external seed, as shown in Figure 10.
  • the binary image Z1 can be solved by the binary image VII-2 and the binary image X, and the separated nuclear binary image Z1 is shown in FIG.
  • the binary image VII-2 may contain impurities such as red blood cells, and the binary image Z1 is used as a marker image, and VII-2 is used as a mask to perform a morphological reconstruction operation on the two to obtain a precise white blood cell binary value.
  • Image Z2 as shown in FIG.

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  • Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)

Abstract

L'invention concerne un procédé basé sur une transformation décisive de marqueur de noyau pour séparer des globules blancs collés. Premièrement, une image RVB d'origine est entrée, un problème difficile commun lors du traitement d'images de leucocytes du sang périphérique et de leucocytes de la moelle épinière est découvert ; ensuite, des transformations d'espace couleur HIS et LUV et d'espace d'échelle de gris sont réalisées sur l'image d'origine et des caractéristiques d'images de composantes de chaque canal sont analysées ; ensuite, une segmentation de seuil et une soustraction d'image sont réalisées sur une composante B et une image d'échelle de gris pour acquérir une image de globule blanc contenant certaines impuretés ; immédiatement après, un groupe de globules blancs nucléés est acquis au moyen d'une technique d'amélioration d'image pour servir de cible marquée ; puis des opérations morphologiques et des transformations décisives sont réalisées sur le groupe de globules blancs nucléés et l'image de globule blanc contenant les impuretés pour éliminer les impuretés afin de produire une image de globule blanc précise et résoudre le problème d'adhérence de cellule ; et enfin, des globules blancs cibles sont fixés et transformés au niveau d'un espace LUV, l'image de globule blanc est groupée à partir des perspectives d'espace et de couleur pour produire un noyau de globule blanc précis.
PCT/CN2015/092590 2014-12-12 2015-10-23 Procédé basé sur une transformation décisive de marqueur de noyau pour séparer des globules blancs collés WO2016091016A1 (fr)

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CN201410778791.0A CN104392460B (zh) 2014-12-12 2014-12-12 一种基于胞核标记分水岭变换的粘连白细胞分割方法
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