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WO2014181024A1 - Procédé de reconnaissance et de classification de cellules sanguines anormales mis en oeuvre par ordinateur, et programmes informatiques pour l'exécution dudit procédé - Google Patents

Procédé de reconnaissance et de classification de cellules sanguines anormales mis en oeuvre par ordinateur, et programmes informatiques pour l'exécution dudit procédé Download PDF

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Publication number
WO2014181024A1
WO2014181024A1 PCT/ES2014/070393 ES2014070393W WO2014181024A1 WO 2014181024 A1 WO2014181024 A1 WO 2014181024A1 ES 2014070393 W ES2014070393 W ES 2014070393W WO 2014181024 A1 WO2014181024 A1 WO 2014181024A1
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blood cells
abnormal blood
cells
cytoplasm
nucleus
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English (en)
Spanish (es)
Inventor
Santiago ALFÉREZ BARQUERO
José RODELLAR BENEDÉ
Luis Eduardo MÚJICA DELGADO
Magda RUIZ ORDÓÑEZ
Anna Merino Gonzalez
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Universitat Politecnica de Catalunya UPC
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Universitat Politecnica de Catalunya UPC
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    • 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
    • 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
    • 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
    • G06V20/698—Matching; Classification

Definitions

  • the present invention concerns in a first aspect, in general, a method implemented by computer for recognition and classification of abnormal blood cells, and in particular a method comprising using said abnormal blood cells recognized and classified to perform a diagnostic orientation of diseases.
  • a second aspect of the invention concerns computer programs adapted to perform some of the steps of the method of the first aspect.
  • SP Peripheral blood
  • Chronic Lymphoid Leukemia cells or LLC are typically described as small lymphocytes with pooled chromatin and poor cytoplasm, on the contrary, Tricoleukemia or HCL cells have a low basophilic cytoplasm and abundant irregular or hairy edges. Therefore, the morphological differentiation between various types of lymphoid cells is not trivial, due in large part to the lack of objective values to define cytological variables, which requires high experience and skill.
  • the invention provides, according to a first aspect, a computer-implemented method for recognition and classification of abnormal blood cells, which, like known techniques, comprises classifying cells based on automatic processing techniques.
  • imaging and blood sample analysis techniques which includes acquiring digital images of abnormal or atypical blood cells, for example lymphoid or blast cells, from a plurality of blood cells.
  • the proposed method comprises performing the following steps:
  • a step of pre-processing of said digital images of acquired abnormal blood cells is performed.
  • the variable form of them is taken into account, with different shades and textures both in the nucleus and at the cytoplasm level, and that the periphery of the cytoplasm can show extensions.
  • Cell segmentation can be done by two different techniques. For example, either by using an active contour technique in said digital images of abnormal blood cells using Gradient Vector Flow (GVF) or conversely, by using a technique of grouping the components of different spaces of color and Watershed transformation in such digital images of abnormal blood cells.
  • GVF Gradient Vector Flow
  • the calculation of the intrinsic characteristics of said identified regions of the nucleus, the cytoplasm and the external region of said abnormal blood cells further comprises extracting first-order statistical characteristics, for example, the mean, standard deviation. , statistical asymmetry, etc. of said identified regions based on a histogram of the identified region.
  • the calculation of the intrinsic characteristics of said identified regions of the nucleus, cytoplasm and the external area of the cell of said abnormal blood cells further comprises calculating statistical characteristics of second order, for example, contrast, homogeneity, entropy, etc. of said regions identified based on a co-occurrence matrix of each region identified.
  • said calculation of the intrinsic characteristics of the identified regions of the nucleus, cytoplasm and the external area of the cell of said abnormal blood cells comprises extracting characteristics of granularity of the nucleus and of the cytoplasm identified by the application of a Mathematical morphology technique.
  • said first and second order statistical characteristics, and the granularity characteristics of the nucleus and cytoplasm are calculated in different components of various color spaces.
  • said calculation of the intrinsic characteristics of said regions of the nucleus, cytoplasm and external area of the cell of said abnormal blood cells comprises calculating a parameter of hairiness of the identified cytoplasm, wherein said segmentation is performed by using a technique of grouping the components of different color spaces and the Watershed Transformation into said digital images of abnormal blood cells.
  • the villus parameter is calculated, according to a preferred embodiment, using a threshold segmentation of a green component of the abnormal blood cells by calculating the total number of pixels of said segmented green region of the digital image.
  • the classification of abnormal blood cells comprises at least one classification into five different groups.
  • these five groups will be: N or healthy patients; HCL or patients with Tricoleukemia; LLC or patients with Chronic Lymphoid Leukemia; MCL or patients with lympoma of the leukemic mantle; and B-PLL or patients with Prolymphocytic B-cell leukemia.
  • steps a) through c) will be repeated as many times as digital images of abnormal blood cells are acquired.
  • the invention provides several computer programs for carrying out the method of the first aspect of the present invention.
  • Fig. 1 shows a flow chart that includes the different steps of the method proposed by the present invention for an exemplary embodiment
  • Fig. 2 illustrates a flow chart of the general scheme of the method proposed by the invention, in the case of applying a Watershed transformation
  • Fig. 3 illustrates a flow chart of the general methodology for the segmentation of lymphoid cells
  • Fig. 4 shows an example of the segmentation methodology stage, where the region that limits a normal lymphoid cell is obtained
  • Fig. 5 shows an example of the different stages to obtain the transformation
  • Fig. 6 shows an example of several cells of a patient with MCL and its corresponding labeled image obtained after applying the Watershed transformation to obtain the complete regions of each cell;
  • Fig. 7 shows the complete segmentations of the cells used as examples in the previous figures.
  • Fig. 8 shows the general scheme that is followed to perform the extraction of intrinsic characteristics or descriptors according to an embodiment of the present invention
  • Fig. 9 shows the image of a lymphocyte with its respective histogram
  • Fig. 10 shows the creation of the co-occurrence matrix, with a distance of 1 pixel, according to an embodiment example
  • Fig. 11 shows the example of a complete granulometric curve for the case of the normal lymphoid cell of Fig. 9; Y
  • Fig. 12 shows the steps to calculate the villus descriptor of a Tricoleucocyte according to an embodiment of the present invention: where in a) the calculated Watershed lines are shown; in b) the region of interest to describe the villi, in c) the resulting histogram for the region of interest; and in d) the pixels between intensities 70 and 215 of the green component.
  • FIG. 1 A flowchart depicting the basic stages of the proposed method is shown in Fig. 1. These basic steps to perform digital cell image processing include: acquisition, preprocessing, segmentation, description (extraction of the main morphological characteristics), and recognition and classification.
  • the present invention provides a new mechanism implemented and / or executed in a computer to identify descriptors or intrinsic characteristics of abnormal blood cells, such as the lymphoid cell, which enable the classification of different diseases or pathologies. , for example neoplasms.
  • the proposed methodology develops a segmentation that separates the regions of the nucleus, cytoplasm and periphery from the outer edge of the cytoplasm or external area of the cell, from which the calculations of the different variables can be performed.
  • the analysis of the variables is preferably carried out by means of a linear discriminant analysis, where the classes referring to each of the different pathologies are separated.
  • the first step to perform is the preparation of blood samples and the acquisition of digital images.
  • factors that affect the quality of the acquisition and processing of the images derived from the SP smear: a) staining process; b) variations in lighting; c) optical geometric distortions due to the type of lens and microscope; d) format in which images are saved; e) random noise; f) lack of contrast between tone levels, etc.
  • different techniques can be applied, such as: spatial and frequency filtering, color transformations, histogram manipulations and others.
  • a preprocessing step is performed that attenuates or enhances the characteristics of the image, such as edges, limits or you can also modify the contrast.
  • This procedure does not increase the information present in the data, but it does increase the dynamic range of the properties of an image to facilitate its manipulation.
  • Some of the preprocessing techniques that can be used are the filtering of the images through the application of Gaussian filters, medium filters, Wiener filter and / or convolution with Wavelets, in order to reduce the noise level.
  • the images stained with MGG are bluish and purple, so it is also possible to apply a color treatment of the images of the blood cells, which can separate, highlight or extract the best profiles.
  • segmentation of the abnormal cell images is performed to separate the regions of the nucleus, cytoplasm and periphery from the outer edge of the cytoplasm, from which the calculations of the variables can be performed.
  • the shape of the cells is variable, with different shades and textures both in the nucleus and at the cytoplasm level and that the periphery of the cytoplasm can show prolongations.
  • Segmentation in the proposed method can be done according to two different alternatives. For example, either using active contour segmentation or using color segmentation by grouping and Watershed transformation.
  • the method recognizes the cellular components using color information from the original image of the cell and by applying contour detection techniques in an H component of the HSV color space, the entire cell is obtained. Similarly, the entire cell nucleus is also obtained using an RGB color space.
  • the method uses the composition of the different colors in the image of the cell due to the staining that is applied to the blood sample, especially in the colors blue, magenta and pink, seen as a first perceptual approach.
  • the technique developed involves segmentation in a preferred way through color grouping to obtain the nucleus directly and indirectly for the segmentation of the entire lymphoid cell.
  • Fig. 3 shows the proposed general methodology, which in turn contains sub-algorithms that are used throughout the segmentation. The most important procedures of the mentioned methodology are mentioned below:
  • this algorithm takes the original image as input and produces as an output a labeled image where the regions of the lymphoid cells are separated from the rest.
  • the main part of this algorithm uses the Kernel Spatial Fuzzy C-means (KSFCM) grouping technique to the Cyan and Magenta components of the CMYK color space to isolate the red blood cells from the lymphoid cell. Subsequently, the Watershed transformation is applied to obtain each region that contains cells.
  • KSFCM Kernel Spatial Fuzzy C-means
  • RG approach of the nucleus its input is the original image and produces a simple approximation of the lymphocyte nucleus, using a combination of the red and green components of the RGB space. Its usefulness is that it can be located approximately where the cells of interest are located.
  • ⁇ XYZ core This procedure mainly uses the KSFCM grouping technique on the XYZ color space to isolate the nucleus, cytoplasm and background groups. To precisely segment the nucleus, it requires as input the original image and the region of interest of the lymphoid cells (obtained in Cell Only). As a result, it produces a binary mask of the nucleus (s) of the lymphoid cell (s), the gradient of the Y component of the XYZ color space, as well as the background group of the cluster.
  • Fig. 4 shows the previous process for a normal lymphoid cell: Fig. 4a represents a normal lymphoid cell, Fig. 4b is the binary mask that approaches the nucleus, Fig. 4c shows the labeled matrix that separates the cells in different regions and Fig. 4d is the resulting binary mask that encloses the lymphocyte of interest.
  • the perimeter of the latter region that limits the lymphoid cell (s) is used as the external marker to be used in the Watershed transformation.
  • Fig. 5 shows the different steps applied to a cell (Fig. 5a) of a patient with Mantle Cell Lymphoma.
  • the binary mask of the nucleus is achieved by the XYZ Core subroutine (Fig. 5b), in which the original image and the region surrounding the cells of interest (extO) have been used.
  • the nucleus mask is thinned if it reaches the external marker and this "improvement" is taken as the final segmentation of the nucleus, with some mathematical morphology operations.
  • This same binary mask is used as an internal marker of the WT that will determine the cytoplasm.
  • FIG. 5c shows the overlap of both markers.
  • the external and internal marker are imposed as minimum on the gradient of the Y component (Fig. 5d and Fig. 5e), to then perform a Watershed transformation and thus generate a labeled image where the whole cell (s) is located ( s) lymphoid (s), as shown in Fig. 5f.
  • Fig. 6 shows an example with four lymphocytes of a patient with MCL and its corresponding image labeled product of all the aforementioned stages. Therefore, another stage is created in which the masks are separated from the possible different cells. This procedure is highlighted by the shaded block Masks of Fig. 3, whose final product is the binary masks of the cell, the nucleus and the region surrounding the cell.
  • the algorithm preferably performs at the stage of the Masks block:
  • Fig. 7 compares the segmentation results for the three lymphoid cells shown above.
  • the segmentation process described and summarized in Fig. 3 also includes the creation of a database of binary masks (1 inside the object, 0 outside it) corresponding to the three regions (nucleus, whole cell and region that surrounds the cell) for the whole set of cells.
  • the method comprises the extraction of characteristics or descriptors according to a scheme like the one shown in Fig. 8.
  • the description is the stage in which information about the objects in the image that you want to analyze is extracted.
  • the characteristics that can be calculated depend on both qualitative and quantitative reasoning and the evaluation of abstract mathematical descriptors that can provide information.
  • Table 1 shows all the descriptors used in a characteristic manner by the present invention according to several embodiments.
  • Area defined as the internal area of the object in question, it is calculated by counting the number of pixels that the region contains.
  • Diameter defined as the diameter of a circle with the same area as the region.
  • Conical eccentricity calculated as the eccentricity of an ellipse whose second moment is equal to that of the region.
  • An eccentricity equal to 1 represents a rectilinear segment, while if it is 0 it represents a circle.
  • Perimeter is the length of the entire edge of the region.
  • Core / cytoplasm ratio For the specific case studied, the core - cytoplasm ratio is calculated by dividing the area of the nucleus by the area of the cytoplasm.
  • the CIE L * a * b * color space is the most complete color space created by the International Lighting Commission (Commission internationale de l'éclairage). This describes all the colors visible to the human eye and was created as a reference model independent of the device. Since Lab was created to approximate human vision, it is perceptually uniform and its L component is quite close to the human perception of Luminosity.
  • the histogram is a function that shows for each pixel value i, the number of pixels H (i) (proportional to the frequency) that have that value in the image.
  • i is represented by the horizontal axis
  • H (i) by the vertical.
  • Fig. 9 is an example of the histogram of the green component of an image of a peripheral blood lymphocyte. Since the image is a discretized rectangular matrix, the pixels take the value i in a range [0, Ll] (L is not necessarily the number of values that the pixels can take, but the number of containers under which the histogram frequencies).
  • the histogram can also be interpreted as the probability density function.
  • Table 2 shows the first order statistical parameters: mean, standard deviation, asymmetry, kurtosis, energy and entropy, obtained from the histogram.
  • Second-order descriptors can provide more information about the texture of the objects to be analyzed. These parameters are defined from the co-occurrence matrix. This matrix arrangement represents the joint probability that two pixels have an intensity value "/ ' " and "j", respectively, at a distance d in a given direction. The co-occurrence matrix considers not only information on intensity levels, but also the position of pixels with similar intensity values.
  • a possible matrix to be used by the proposed 4x4 size method with three intensity levels is shown.
  • the element at position (1,1) of the co-occurrence matrix indicates that level "0" is next to level "0 "2 times, in the diagonal northwest direction.
  • the element in position (2,3), relates level "1" next to level "2" in the same direction, presenting itself once.
  • Table 3 defines the second order descriptors used mathematically, based on the co-occurrence matrix.
  • Granulometry determines the distribution of particle sizes in an image, for this, mathematical morphology is generally used to estimate the distribution of particles indirectly, without having to identify and measure each of them on the image.
  • N is the dimension of the co-occurrence matrix (N x N).
  • ⁇ is the average of
  • the Granulometry consists in applying openings increasing the size of the structural element of this morphological operation.
  • the basic idea is that opening operations with a particular size should have a greater effect on regions of the image that contain clear particles with a similar size.
  • the sum of the pixel intensity values in the image resulting from operating with the opening is calculated.
  • This sum (when normalized by the sum of the intensities of the pixels of the original region is called the size distribution function for granulometry) should decrease as much as the size of the structural element is increased, since openings reduce the intensity of Shining particles
  • This procedure produces a vector where each element is equal to the sum of the intensities of the pixels of the operated image (with opening) and each location of the vector represents a size of the structural element. To emphasize the changes between successive openings, the difference between adjacent elements of this 1D vector is calculated. To visualize the results you are differences (if The normalized function is the density distribution of the size distribution for granulometry) are plotted, where each peak is an indication of the dominant size distribution on the particles in the image.
  • the method is called antigranulometry.
  • the main difference is that the size distribution of the dark particles is measured. That is to say, as a new closing is executed (increasing the size of the structural element) there is a greater effect of the operation on the dark granules.
  • Granulometry produces information about bright particles, while anti-granulometry produces information about dark particles.
  • One way to take advantage of both methodologies is to join the distribution densities into one.
  • the result is the Granulometric Curve, which reflects the distribution of closing operations, placing them in the negative coordinates (size of the structural element for closing).
  • the distribution for the iterations with opening is left the same and they are placed in the positive coordinates (size of the structural element for the opening).
  • the density value for size zero is zero, so as not to affect the total contribution of the densities in subsequent quantitative measurements.
  • the result is a graph as shown in Fig. 11, with some symmetry and covering all sizes from closing to opening.
  • the proposed method uses, according to an exemplary embodiment, preferably four measures for the extraction of information on the granulometric curve of the nucleus and the cytoplasm of the lymphocytes: the mean, the standard deviation, the statistical asymmetry and the kurtosis.
  • Cytoplasmic profile descriptor A descriptor has been implemented that can estimate the amount of villi present on the outer edge of the cytoplasm by analyzing the region outside the cell, which has been previously segmented next to the nucleus and the cell. The number of pixels in this region that "do not belong to the background" is estimated by the number of pixels of a new thresholding segmentation between, for example, intensity levels 70 and 215 of the green component. In Fig. 12, the different steps for calculating the cytoplasmic profile descriptor of a Tricoleucocyte are shown.
  • all calculated descriptors are stored in a row vector for each cell. That is to say, that for a given set of cells there is a matrix with as many rows as cells to be analyzed. Once the characteristics of each cell have been calculated, these characteristics are used to classify the abnormal cells into 5 different groups. Generally, these five groups will be: N; HCL; LLC; MCL or B-PLL.
  • the proposed method has been made on 1500 digital images of SP lymphoid cells stained with May-Grünwald-Giemsa in the CellaVision DM96 (Cellavision AB, Lund, Sweden).
  • the images correspond to: 180 images characteristic cells of healthy patients (H), 301 images of lymphoid cells characteristic of patients with Tricholeukemia (HCL), 542 images of lymphoid cells characteristic of patients with Chronic Lymphocytic Leukemia (LLC), 401 images of cells characteristics of mantle cell lymphoma (MCL) and 75 images of lymphoid cells characteristic of prolymphocytic leukemia B (B-PLL).

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Abstract

Ce procédé réalise une classification des cellules au moyen de techniques de traitement automatique et d'analyse d'échantillons de sang, incluant l'acquisition, d'images numériques de cellules sanguines anormales à partir de cellules sanguines, et la segmentation desdites images numériques de cellules anormales pour l'obtention de régions identifiées du noyau, du cytoplasme, et de la surface cellulaire externe desdites cellules sanguines anormales dans lesdites images numériques; le calcul des caractéristiques intrinsèques de chacune desdites régions identifiées du noyau, du cytoplasme, et de la surface cellulaire externe desdites cellules sanguines anormales par calcul des caractéristiques géométriques desdites régions identifiées; la reconnaissance et le classement automatiques des cellules sanguines anormales en fonction desdites caractéristiques intrinsèques; et l'utilisation desdites cellules sanguines anormales reconnues et classées pour réaliser une orientation diagnostique vers des maladies hématologiques.
PCT/ES2014/070393 2013-05-09 2014-05-09 Procédé de reconnaissance et de classification de cellules sanguines anormales mis en oeuvre par ordinateur, et programmes informatiques pour l'exécution dudit procédé Ceased WO2014181024A1 (fr)

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ES201330671A ES2428215B2 (es) 2013-05-09 2013-05-09 Método implementado por ordenador para reconocimiento y clasificación de células sanguíneas anormales y programas informáticos para llevar a cabo el método

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RU2659217C1 (ru) * 2017-12-12 2018-06-28 федеральное государственное автономное образовательное учреждение высшего образования "Национальный исследовательский ядерный университет МИФИ" (НИЯУ МИФИ) Способ распознавания структуры ядер бластов крови и костного мозга с применением световой микроскопии в сочетании с компьютерной обработкой данных для определения В- и Т-линейных острых лимфобластных лейкозов
CN113516061B (zh) * 2021-06-23 2022-04-29 南开大学 一种基于机器学习的pc12细胞状态识别的方法
CN114898866B (zh) * 2022-05-24 2024-03-15 广州锟元方青医疗科技有限公司 一种甲状腺细胞辅助诊断方法、设备和存储介质

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