CN112381838A - Automatic image cutting method for digital pathological section image - Google Patents

Automatic image cutting method for digital pathological section image Download PDF

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CN112381838A
CN112381838A CN202011273550.2A CN202011273550A CN112381838A CN 112381838 A CN112381838 A CN 112381838A CN 202011273550 A CN202011273550 A CN 202011273550A CN 112381838 A CN112381838 A CN 112381838A
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image
coordinate
sparse matrix
elements
image block
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CN112381838B (en
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杨一明
郑众喜
周燕燕
袁勇
杨永全
步宏
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West China Precision Medicine Industrial Technology Institute
West China Hospital of Sichuan University
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West China Hospital of Sichuan University
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    • 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
    • G06T3/00—Geometric image transformation in the plane of the image
    • G06T3/40—Scaling the whole image or part thereof
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/187—Segmentation; Edge detection involving region growing; involving region merging; involving connected component labelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/66—Analysis of geometric attributes of image moments or centre of gravity
    • 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
    • G06T2207/20021—Dividing image into blocks, subimages or windows

Abstract

The invention discloses an automatic image cutting method of a digital pathological section image, which comprises the following steps: s1, recognizing and extracting the outline of the tissue area; s2, calculating the centroid coordinate and the length of the long axis of the contour; s3, setting parameters of the image block to be extracted; s4, calculating an intelligent graph cutting factor R; s5, designing a sparse matrix A; s6, calculating a coordinate matrix XY of a non-zero element of A; s7, calculating a coordinate matrix XY' in the 1x diagram; s8, whether the elements in the traversal XY' are inside the outline or not is judged, and whether the image block T is cut or not is judged. The invention has high sampling rate, ensures the accuracy of the cutting chart and has higher efficiency.

Description

Automatic image cutting method for digital pathological section image
Technical Field
The invention relates to the technical field of computer vision correlation, in particular to an automatic image cutting method for a digital pathological section image.
Background
The digital pathology full-section image is mainly a 40x image, the length and width distribution of one digital pathology full-section image is from tens of thousands of pixel units to hundreds of thousands of pixel units, and if the large image of the level needs to be analyzed by an algorithm, the image of a tissue region in the large image needs to be cut out for processing.
The image cutting and extracting method in the prior art has large calculation amount, and cannot give consideration to the image cutting efficiency while ensuring the accuracy.
Disclosure of Invention
The invention aims to provide an automatic image cutting method for a digital pathological section image, which has high sampling rate, ensures the accuracy of image cutting and has high efficiency.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme:
the invention discloses an automatic image cutting method of a digital pathological section image, which comprises the following steps:
s1, reducing the digital pathology full-slice image I from a 40x image to a 1x image I ', extracting a tissue region mask of the 1x image I' through a deep learning FCN network, and generating a contour M;
s2, calculating the centroid coordinate (x) of the contour Mc，yc) And the length of the major axis L;
s3, setting parameters of the image block to be extracted, comprising the following steps:
s31, traversing each image block T in the image I to obtain the ith image block TCenter coordinate (x)i，yi)，
S32, mapping each image block T to the 1x image I 'to obtain the center coordinate (x) of the image block T' on the 1x image I1，y1) Wherein
Figure BDA0002778420980000021
S33, calculating the center coordinate (x) of the image block T1，y1) With coordinates of center of mass (x)c，yc) A distance dist of wherein
Figure BDA0002778420980000022
S4, calculating an intelligent graph cutting factor R with the formula of
Figure BDA0002778420980000023
Wherein f () represents rounding down, max [ x, y ] represents taking the maximum of x and y;
s5, calculating a sparse matrix A, wherein the size of the sparse matrix A corresponds to the image block T, the row elements and the column elements of the sparse matrix A are h and w, R nonzero elements are arranged in the row elements of the sparse matrix A, R nonzero elements are arranged in the column elements of the sparse matrix A, and the nonzero elements in the sparse matrix A are uniformly distributed;
s6, obtaining a coordinate matrix XY of the non-zero elements in the sparse matrix A,
when R is not equal to n, the compound is,
Figure BDA0002778420980000024
wherein x0＝iw，x1＝iw+(w-1)/(n-1)，…，xn＝iw+n*(w-1)/(n-1)，
y0＝ih，y1＝ih+(h-1)/(n-1)，…，yn＝ih+n*(h-1)/(n-1)；
S7, obtaining a coordinate matrix XY 'of the sparse matrix A in which the non-zero elements are mapped to the 1x figure I',
Figure BDA0002778420980000031
s8, whether the traversal coordinate matrix XY' is within the contour M,
if the coordinate is in the outline M, the image block is subjected to image cutting operation,
if the coordinates are not within the contour M, then the operation is discarded.
Preferably, in step S5, R ═ 2,
Figure BDA0002778420980000032
preferably, in step S5, R ═ 3,
Figure BDA0002778420980000033
preferably, in step S6, R ═ 3,
Figure BDA0002778420980000041
wherein x0＝iw，x1＝iw+(w-1)/2，x2＝iw+(w-1)，
y0＝ih，y1＝ih+(h-1)/2，y2＝ih+(h-1)。
The invention has the beneficial effects that:
1. the calculated amount of the whole algorithm is controlled by the intelligent graph cutting factor R in a small amount of calculation to achieve the purpose of judgment, and the efficiency is high.
2. The intelligent image cutting factor R adopts the design idea of detecting the ratio of the distance between the central coordinate of the image block T and the centroid of the tissue outline to the length of the long axis of the outline, and has high accuracy.
3. The invention uses the non-zero element uniform distribution in the sparse matrix A, and improves the sampling efficiency.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the present invention comprises the steps of:
s1, reducing the digital pathology full-slice image I from a 40x image to a 1x image I ', extracting a tissue region mask of the 1x image I' through a deep learning FCN network, and generating a contour M;
s2, calculating the centroid coordinate (x) of the contour Mc，yc) And the length of the major axis L;
s3, setting parameters of the image block to be extracted, comprising the following steps:
s31, traversing each image block T in the image I, obtaining the center coordinate (x) of the ith image block Ti，yi)，
S32, mapping each image block T to the 1x image I 'to obtain the center coordinate (x) of the image block T' on the 1x image I1，y1) Wherein
Figure BDA0002778420980000051
S33, calculating the center coordinate (x) of the image block T1，y1) With coordinates of center of mass (x)c，yc) A distance dist of wherein
Figure BDA0002778420980000052
S4, calculating an intelligent graph cutting factor R with the formula of
Figure BDA0002778420980000053
Wherein f () represents rounding down, max [ x, y ] represents taking the maximum value of x and y, and the value range of R is [2, 10 ];
s5, calculating a sparse matrix A, wherein the size of the sparse matrix A corresponds to the image block T, the row elements and the column elements of the sparse matrix A are h and w, R nonzero elements are arranged in the row elements of the sparse matrix A, R nonzero elements are arranged in the column elements of the sparse matrix A, and the nonzero elements in the sparse matrix A are uniformly distributed;
s6, obtaining a coordinate matrix XY of the non-zero elements in the sparse matrix A,
when R is not equal to n, the compound is,
Figure BDA0002778420980000054
wherein x0＝iw，x1＝iw+(w-1)/(n-1)，…，xn＝iw+n*(w-1)/(n-1)，
y0＝ih，y1＝ih+(h-1)/(n-1)，…，yn＝ih+n*(h-1)/(n-1)；
S7, obtaining a coordinate matrix XY 'of the sparse matrix A in which the non-zero elements are mapped to the 1x figure I',
Figure BDA0002778420980000061
s8, whether the traversal coordinate matrix XY' is within the contour M,
if the coordinate is in the outline M, the image block is subjected to image cutting operation,
if the coordinates are not within the contour M, then the operation is discarded.
For example, in step S5, when R is 2,
Figure BDA0002778420980000062
when R is 3 in step S5,
Figure BDA0002778420980000063
in step S6, for example, R ═ 3,
Figure BDA0002778420980000071
wherein x0＝iw，x1＝iw+(w-1)/2，x2＝iw+(w-1)，
y0＝ih，y1＝ih+(h-1)/2，y2＝ih+(h-1)。
In the invention, the value of the intelligent map cutting factor R is [2, 10]]The number of the non-zero elements of the sparse matrix A is R2The size of the coordinate matrix XY of the non-zero elements of A is R, and whether the number of points in the contour is R needs to be judged2. There are w x h points in block T, and only R is discriminated by using the algorithm2The point can judge whether the block T and the tissue area have intersection, thereby greatly reducing the calculation amount.
Design idea of intelligent graph cutting factor R: the ratio of the distance between the central coordinate of the detection block T and the centroid of the tissue profile to the length of the long axis of the profile is larger, and the larger the L/dist is, the closer the detection block T is to the tissue profile is, and the density of sampling points needs to be improved. The larger the L/dist is, the larger the calculated R value is, the more the number of the non-zero elements of the corresponding sparse matrix A is, the density of the sampling points is improved, and the accuracy of the judgment of the tangent graph is ensured.
The value range [2, 10] of R is limited, thus providing guarantee for reducing the calculated amount.
Design idea of sparse matrix A:
1. the larger the value of the intelligent graph cutting factor R is, the corresponding number R of the non-zero elements of the sparse matrix A is2The more the cutting patterns are, the more the cutting patterns are connected with the design idea of the intelligent cutting pattern factor R, and the accuracy of cutting pattern judgment is ensured.
2. Non-zero elements in the sparse matrix A are uniformly distributed, so that the sampling efficiency is improved, the R value can be effectively sampled in a lower range, and the calculated amount is reduced.
The present invention is capable of other embodiments, and various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the invention.

Claims (4)

1. An automatic image cutting method for digital pathological section images is characterized by comprising the following steps:
s1, reducing the digital pathology full-slice image I from a 40x image to a 1x image I ', extracting a tissue region mask of the 1x image I' through a deep learning FCN network, and generating a contour M;
s2, calculating the centroid coordinate (x) of the contour Mc，yc) And the length of the major axis L;
s3, setting parameters of the image block to be extracted, comprising the following steps:
s31, traversing each image block T in the image I, obtaining the center coordinate (x) of the ith image block Ti，yi)，
S32, mapping each image block T to the 1x image I 'to obtain the center coordinate (x) of the image block T' on the 1x image I1，y1) Wherein
Figure FDA0002778420970000011
S33, calculating the center coordinate (x) of the image block T1，y1) With coordinates of center of mass (x)c，yc) A distance dist of wherein
Figure FDA0002778420970000012
S4, calculating an intelligent graph cutting factor R with the formula of
Figure FDA0002778420970000013
Wherein f () represents rounding down, max [ x, y ] represents taking the maximum of x and y;
s5, calculating a sparse matrix A, wherein the size of the sparse matrix A corresponds to the image block T, the row elements and the column elements of the sparse matrix A are h and w, R nonzero elements are arranged in the row elements of the sparse matrix A, R nonzero elements are arranged in the column elements of the sparse matrix A, and the nonzero elements in the sparse matrix A are uniformly distributed;
s6, obtaining a coordinate matrix XY of the non-zero elements in the sparse matrix A,
when R is not equal to n, the compound is,
Figure FDA0002778420970000021
wherein x0＝iw，x1＝iw+(w-1)/(n-1)，...，xn＝iw+n*(w-1)/(n-1)，
y0＝ih，y1＝ih+(h-1)/(n-1)，...，yn＝ih+n*(h-1)/(n-1)；
S7, obtaining a coordinate matrix XY 'of the sparse matrix A in which the non-zero elements are mapped to the 1x figure I',
Figure FDA0002778420970000022
s8, whether the traversal coordinate matrix XY' is within the contour M,
if the coordinate is in the outline M, the image block is subjected to image cutting operation,
if the coordinates are not within the contour M, then the operation is discarded.
2. The automatic graph cutting method according to claim 1, wherein: in step S5, R is 2,
Figure FDA0002778420970000023
3. the automatic graph cutting method according to claim 1, wherein: in step S5, R is 3,
Figure FDA0002778420970000031
4. the automatic graph cutting method according to claim 1, wherein: in step S6, R is 3,
Figure FDA0002778420970000032
wherein x0＝iw，x1＝iw+(w-1)/2，x2＝iw+(w-1)，
y0＝ih，y1＝ih+(h-1)/2，y2＝ih+(h-1)。
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