CN107590856A - The three-dimensional visualization application process of anatomical atlas in neurosurgery navigation system - Google Patents
The three-dimensional visualization application process of anatomical atlas in neurosurgery navigation system Download PDFInfo
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Abstract
The invention belongs to medical field, discloses a kind of three-dimensional visualization application process of anatomical atlas in neurosurgery navigation system, using advanced nonlinear interpolation, careful Digital Three-Dimensional reconstruction is carried out to neurotomy collection of illustrative plates；Using three dimensional non-linear registration method, neurotomy collection of illustrative plates is taken the lead in unifying under the same coordinate system, obtains three-dimensional standard digital collection of illustrative plates；Generate three-dimensional colour collection of illustrative plates；Visualization Platform structure is carried out, the brain section that three collection of illustrative plates cross section, coronal-plane and sagittal plane directions are carried out with two dimension is shown, while carries out the display of three-dimensional orthogonal brain section；Establish anatomical organs and dissect the corresponding relation between title, dissection title and colouring information.The two dimensional cross-section that the present invention not only carries out anatomical atlas is shown, the display of the three-dimensional orthogonal section of collection of illustrative plates is also carried out, and the profile of anatomical structure is relatively apparent, subordinate relation becomes apparent from, neuroanatomical teaching is highly suitable for, facilitates neurosurgeon to be learnt.
Description
Technical Field
The invention belongs to the field of medical treatment, and particularly relates to a three-dimensional visualization application method of an anatomical atlas in a neurosurgery navigation system.
Background
Neurosurgery is based on surgery as the main treatment means, and uses a unique neurosurgery research method to research the damage, inflammation, tumor, deformity and some genetic metabolic disorders or dysfunctional diseases of human nervous system, such as brain, spinal cord and peripheral nervous system, and related auxiliary mechanisms, such as skull, scalp, cerebral blood vessel and meninges, etc. the neurosurgery has the following advantages: the etiology and pathogenesis of epilepsy, parkinson's disease, neuralgia and other diseases, and explores a new high, essence and advanced discipline of diagnosis, treatment and prevention technology. However, the existing neuro-anatomical atlas data is inaccurate and is easy to repeat the registration operation; meanwhile, the map color is single and is marked, so that the result dependency relationship cannot be accurately displayed, and the recognition is not facilitated.
The interpolation spline curve/curved surface has a plurality of construction methods and plays an important role in the geometric modeling of medical images. The prior art has now investigated rational cubic splines and their use in shape control and interpolated splines of trigonometric polynomials with some useful results.
However, the conventional spline curve/surface does not interpolate a control vertex or has no interpolation function, and a free curve/surface is generated.
In summary, the problems of the prior art are as follows: the existing neuroanatomy atlas data is inaccurate, and the registration operation is easy to repeat; meanwhile, the color of the map is single, so that the result dependency relationship cannot be accurately displayed, and the recognition is not facilitated; moreover, the existing spline curve/curved surface does not interpolate to control the vertex; and the accurate generation of the geometric model of the image is not facilitated.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a three-dimensional visualization application method of an anatomical atlas in a neurosurgery navigation system.
The invention is realized in such a way that the three-dimensional visualization application method of the anatomical atlas in the neurosurgical navigation system comprises the following steps:
step 1: and performing detailed digital three-dimensional reconstruction on the neuroanatomy map by adopting an advanced nonlinear interpolation method.
Step 2: a three-dimensional nonlinear registration method is adopted, the neuroanatomy maps are firstly unified to the same coordinate system, and a three-dimensional standard digital map is obtained.
And 3, step 3: and generating a three-dimensional color map.
Dividing the obtained three-dimensional standard digital map data into structures in three directions of a cross section, a coronal plane and a sagittal plane, sampling to obtain a plurality of contour coordinate point information of each structure on different layers, generating an original data point set, and recording an anatomical name; interpolating the acquired discontinuous original data point set by using a Cardinal spline interpolation value to generate a contour curve of the cerebral anatomical structure; and filling the color of the region surrounded by the generated contour curve by adopting a point-by-point judgment filling algorithm, filling the color of all the structures in the region according to the different layers of the different brain tissue structures to generate a two-dimensional color map, and then generating a three-dimensional color map by using the two-dimensional color map in the three directions of the cross section, the coronal plane and the sagittal plane according to a fixed layer spacing.
And 4, step 4: and constructing a visualization platform, displaying two-dimensional brain sections in three directions of the cross section, coronal plane and sagittal plane of the atlas, and simultaneously displaying three-dimensional orthogonal brain sections.
And 5: and establishing corresponding relations between the anatomical organs and the anatomical names and between the anatomical names and the color information.
Storing the anatomical names and the color information of the interesting anatomical structures in a one-to-one corresponding mode, establishing the corresponding relation between the anatomical organs and the anatomical names and between the anatomical names and the color information, and storing the generic relation between different anatomical organs and the main area of the anatomical organs in a hierarchical storage mode; the name of the interesting anatomical structure is displayed in real time by reading the color information corresponding to the interesting anatomical structure, and the dependency relationship stored in a hierarchical storage form among the anatomical structures is displayed by using a tree structure window.
Further, the nonlinear interpolation is a nonlinear interpolation based on convolution, and the influence function kernel is a basic spline function:
(b n ) -1 is a B-pattern stripe function filter.
In the generating of the contour curve of the cerebral anatomical structure by interpolating the collected discontinuous original data point set by using Cardinal spline interpolation, the generating method of the contour curve comprises the following steps:
1) Given set value point column d 0 ,d 1 ,d 2 ,…,d m Supplement of auxiliary points d -2 ,d -1 8230a and d m+1 ,d m+2 8230the spline junction sequence is:
…≤t -1 ≤a＝t 0 ＜t 1 ＜t 2 ＜…＜t m-1 ＜t m ＝b≤t m+1 ≤…；
will { d } j Taking the curve as a Cardinal control vertex sequence to obtain an n-order B spline curve, and recording the curve as:
wherein N is j,n (t) is an n-th order B-spline basis function whose support is set to an intervalIs a real numberRounding;
constructional curve d I (t) is satisfied withInterpolation conditions are as follows:
d I (t k )＝d k ,k＝0,1,2,…,m；
2) Spline subinterval [ t ] at each interval i ,t i+1 ](i =0,1,2, \ 8230;, m-1) connecting the two end points d (t) of the B-spline curve segment d (t) i ) And d (t) i+1 ) Is marked by a straight line segment of i (t), the equation is:
l i (t)＝(1-Φ i (t))d(t i )+Φ i (t)d(t i+1 ),t i ≤t≤t i+1 ；
and connect two adjacent Cardinal points d i And d i+1 Is marked as L i (t), the equation is:
l i (t)＝(1-Φ i (t))d(t i )+Φ i (t)d(t i+1 ),t i ≤t≤t i+1 ；
L i (t)＝(1-Φ i (t))d i +Φ i (t)d i+1 ,t i ≤t≤t i+1 ；
drawing a curve segment d (t) and a straight line segment l i (t) in the interval [ t i ,t i+1 ]Difference vector of (a):
δ i (t)＝d(t)-l i (t),t i ≤t≤t i+1 ；
the difference vector is expanded and contracted to obtain alpha delta i (t), α > 0, and translating it so that its origin falls on the straight line segmentAt the corresponding point, namely:
d I (t)＝L i (t)+αδ i (t),t i ≤t≤t i+1 ,i＝0,1,2,…,m-1；
or written as:
d I (t)＝[(1-Φ i (t))d i +Φ i (t)d i+1 ]+α[d(t)-(1-Φ i (t))d(t i )-Φ i (t)d(t i+1 )]；
t i ≤t≤t i+1 ,i＝0,1,2,…,m-1；
function phi i (t) satisfies the following condition:
Φ i (t) in the interval [ t i ,t i+1 ]Has a continuous derivative up to order n-2;
obtaining:
Φ i (t) in the interval [ t i ,t i+1 ]Is a monotonically increasing function to avoid straight line segments l i (t) and L i (t) the appearance of a heavy node;
the hierarchical storage mode is adopted to store the generic relation among different anatomical organs and the belonging main area; the method comprises the following steps:
the method I comprises the following steps: in the record cache, selecting record data with the same order of magnitude as the data to be added for replacement; or the second mode: in the record cache, selecting a record cache page with different orders of magnitude from the data to be added, recovering the space occupied by the cache page, distributing a new record cache page for the data to be added by utilizing the recovered space, and writing the data to be added into the new record cache page;
wherein the first mode or the second mode is selected according to the following method:
obtaining the access frequency Frec of the record data with the same order of magnitude as the data to be added and the access frequency Fpage of the record cache page with the different order of magnitude as the data to be added;
judging whether Frec > place _ page _ ratio is true or not, if true, selecting the first mode, and if not, selecting the second mode;
wherein, the replace _ page _ ratio is a preset replacement control parameter, and the replace _ page _ ratio belongs to (0, 1);
the method for obtaining the access frequency Fpage of the record cache page with different orders of magnitude from the data to be added comprises the following steps:
Fpage＝(Fmin+Fmax)/2*N；
wherein Fmin is an access frequency of data with the earliest timestamp in the record cache page, fmax is an access frequency of data with the latest timestamp in the record cache page, and N is a total data record amount of the record cache page;
the method for displaying the name of the interesting anatomical structure in real time by reading the color information corresponding to the interesting anatomical structure comprises the following steps: performing over-segmentation on the image by using a preset over-segmentation algorithm to obtain at least one region, wherein the color values of all pixel points in the same region are the same;
determining a color value and a centroid for each of the regions;
establishing the significance model according to the color values corresponding to the regions and the centroids of the regions;
the significance model is as follows:
wherein S is i1 Is a region R i Significance value of any one pixel point, w (R) j ) Is a region R j Number of pixels in, D S (R i ,R j ) For characterizing said region R i And the region R j Measure of the difference in spatial position between, D C (R i ,R j ) For characterizing said region R i And the region R j The color difference between the two images is measured, N is the total number of the areas obtained after the image is subjected to over-segmentation, D S (R i ,R j ) Comprises the following steps: d S (R i ,R j )＝exp(-(Center(R i )-Center(R j )) 2 /σ s 2 )；Center(R i ) Is the region R i Center of mass of (R) j ) Is the region R j When the coordinates of each pixel point in the image are normalized to [0, 1]]When the current is over;
or classifying each pixel point in the image according to the color value of each pixel point, and classifying the pixel points with the same color value into the same color type;
the saliency model is built from the color values of each color type.
Further, the method for generating the n-order B-spline curve includes:
(1) A bicubic B spline interpolation surface is arranged on a rectangular domain, and the rectangular domain [ a, B is given; c, d ] an extended partition:
u -9 ≤u -6 ≤u -3 ≤a＝u 0 ＜u 1 ＜…＜u 3i ＜u 3i+1 ＜…＜u 3m-1 ＜u 3m ＝b≤u 3(m+1) ≤u 3(m+2) ≤u 3(m+3) ，
v -9 ≤v -6 ≤v -3 ≤c＝v 0 ＜v 1 ＜…＜v 3i ＜v 3i+1 ＜…＜v 3n-1 ＜v 3n ＝d≤v 3(n+1) ≤v 3(n+2) ≤v 3(n+3) ；
and Cardinal control grid vertex set:
{d ij ,i＝-1，0,1,…m+1,j＝-1,0,1,…,n+1}；
then the bi-cubic B-spline surface over the rectangular field [ a, B ] × [ c, d ] is noted as:
wherein the cubic B-spline basis function N i,4 (u) andthe spline nodes on the support are respectively as follows:
{u 3(i-2) ,u 3(i-1) ,u 3i ,u 3(i+1) ,u 3(i+2) and { v } and 3(j-2) ,v 3(j-1) ,v 3j ,v 3(j+1) ,v 3(j+2) }；
constructing a bicubic B-spline interpolation surface d I (u, v) are as follows:
wherein:
u 3i ≤u≤u 3i+3 ,v 3j ≤v≤v 3j+3 ,i＝0,1,2,…,m-1,j＝0,1,2,…,n-1；
u and v replace t in the original expression respectively:
h 3i+k ＝u 3i+k+1 -u 3i+k and
respectively substitute for h in the original formula 3i+k ,k＝0,1,2；
(2) A double fourth-order quartic B spline interpolation surface is arranged on a rectangular domain, and the rectangular domain [ a, B is given; c, d ] an expanded partition:
u -6 ≤u -4 ≤u -2 ≤a＝u 0 ＜u 1 ＜…＜u 2i ＜u 2i+1 ＜…＜u 2m-1 ＜u 2m ＝b≤u 2(m+1) ≤u 2(m+2) ≤u 2(m+3) ；
v -6 ≤v -4 ≤v -2 ≤c＝v 0 ＜v 1 ＜…＜v 2i ＜v 2i+1 ＜…＜v 2n-1 ＜v 2n ＝d≤v 2(n+1) ≤v 2(n+2) ≤v 2(n+3) ；
and Cardinal control grid vertex set { d ij I = -1,0,1, \8230m +1, j = -1,0,1, 8230n +1, then rectangular field [ a, b ]]×[c,d]The bicubic B-spline surface above is noted:
wherein the cubic B-spline basis function omega i (u) andthe spline nodes on the support are respectively as follows:
{u 2(i-2) ,u 2(i-1) ,u 2i ,u 2(i+1) ,u 2(i+2) and { v } and 2(j-2) ,v 2(j-1) ,v 2j ,v 2(j+1) ,v 2(j+2) }；
constructing a double fourth-order quartic B-spline interpolation surface r I (u, v) are as follows:
wherein:
u 3i ≤u≤u 3i+3 ,v 3j ≤v≤v 3j+3 ,i＝0,1,2,…,m-1,j＝0,1,2,…,n-1；
u and v respectively replace t in the original expression:
h 2i+k ＝u 2i+k+1 -u 2i+k and
respectively substitute h in the original formula 2i+k ,k＝0,1。
Further, in the segmentation of the three-dimensional standard digital atlas data from the structure in three directions of the transverse plane, the coronal plane and the sagittal plane, the segmentation method comprises the following steps:
firstly, comparing two adjacent frames of images, finding out the changed regions of all the images, and then obtaining a set of non-overlapping rectangular regions with the minimum area according to the coordinates of changed pixel points; only sending image data and corresponding coordinate information contained in the rectangular area set each time;
obtaining a change rectangular area according to the coordinates of the pixel points, wherein the formulas (1) and (2) are formulas for judging the range of the rectangle R according to the change pixel points;
R l ≤P x AND R t ＝P yi (1)
R r ≥P x AND R b ≥P y (2)
wherein R is l And R t The abscissa and ordinate, R, representing the upper left corner of the rectangle r And R b The abscissa and ordinate, P, representing the lower right corner of the rectangle x And P y Abscissa and ordinate, P, representing varying pixel points y0 Representing the ordinate of the first changed pixel.
Further, obtaining the range of the change rectangular area according to the formula (1) and the formula (2); firstly, storing data of two adjacent bitmaps in front and back, and judging whether values of pixels corresponding to the screens of the front frame and the back frame are changed or not; when a changed sample point is detected for the first time, the coordinates (P) of the sample point will change X0 ,P Y0 ) Recording is performed as the coordinate (R) of the upper left corner of the changed rectangular region l ,R t ) And identify row no change as false; continuing the comparison, when different sampling points are detected again, firstly marking the row unchanged as false, and then marking the abscissa P of the sampling point as the abscissa P x The abscissa R of the same rectangle at the upper left corner l Compare and take the minimum while taking the coordinate (R) of the lower right corner of the rectangle r ,R b ) Coordinates (P) of the meeting point x ,P y ) Comparing and taking the maximum value; namely:
R l ＝min(P xi ,R l )(i>1) R t ＝P yi (i＝1)
R r ＝max(R xi ,R r )(i>1) R b ＝max(R yi ,R b )(i>1)
when detecting that the sampling point values of a certain row are all the same, a changed rectangular area block is obtained.
Further, judging whether pixels corresponding to two frames of screen images in the front and rear image buffer areas change by adopting an alternate direct comparison method so as to find out a changed rectangular area; and finding out all the change regions of the next frame image relative to the previous frame image according to the principle of from top to bottom and from left to right, and obtaining a set of non-overlapping rectangular regions with the minimum area based on a rectangular segmentation algorithm.
Further, judging whether pixels corresponding to the front and rear two frames of screen images change or not by adopting an alternate column direct comparison method, extracting pixels corresponding to the front and rear two frames of images from left to right at intervals of N columns in a row unit as sampling points, and comparing whether values of corresponding pixel points are the same or not; according to different application scenes and the requirement of bandwidth, the number N of the interval columns is adjusted, and the smaller the value of N is, the longer the time required by the direct comparison method of the interval columns is for detection.
The invention has the advantages and positive effects that: the invention adopts a nonlinear interpolation method, can improve the data accuracy of the neuroanatomy atlas, and adopts a three-dimensional nonlinear registration method to avoid repeated registration operation on different atlases; meanwhile, the newly generated color SW visualization map can not only display a two-dimensional section of an anatomical map, but also display a three-dimensional orthogonal section of the map, the outline of the anatomical structure is relatively clear, the dependency relationship is clear, and the color SW visualization map is very suitable for teaching of neuroanatomy and is convenient for a neurologist to learn.
The contour curve generation method provided by the invention constructs a bicubic B spline interpolation surface B and a spline interpolation surface which are interpolated on a control vertex mesh in a rectangular domain, and a bicubic B spline interpolation surface, the construction method of the interpolation curve/surface is simple, the geometrical significance is obvious, the problem of interpolation control vertex is solved, and the method has important significance for the reverse engineering of medical images.
The invention comprises two caches in the same database server, wherein the record cache is used for reading and writing data in a data line unit, and when only a small amount of hot data changes, the record cache can be updated only, so that the utilization rate of the cache of the database server is improved, and the updating frequency of the cache is reduced. In addition, because the record cache and the page cache are both located in the same database server, the user can obtain the data corresponding to the anatomical organ by only sending a query request once, so that the method has high access efficiency and saves network resources.
The invention provides a method for segmenting three-dimensional standard digital map data from structures in three directions of a cross section, a coronal plane and a sagittal plane, which comprises the steps of firstly comparing two adjacent frames of images, finding out the changed regions of all the images, then obtaining a set of non-overlapping rectangular regions with the minimum area according to the coordinates of changed pixel points, and only sending the image data contained in the set of rectangular regions each time so as to reduce the segmented image transmission data of each frame and achieve the purpose of effectively reducing the transmission data quantity. According to the invention, the screen is dynamically divided into the rectangular blocks with different numbers according to the different number of the image change areas of each frame, so that the problems that the number of the screen blocks is difficult to determine and the adaptability is poor in a fixed block image transmission algorithm are solved; the screen is divided into rectangular blocks with different sizes according to the difference of each variation range of each frame of image, so that the problem that the transmission data volume cannot be effectively reduced when the variation area of the screen image is just positioned at the critical point of a plurality of rectangular blocks in the fixed block image transmission algorithm is solved. The invention can effectively reduce the CPU utilization rate, reduce the bandwidth occupancy rate and improve the image transmission performance after image segmentation.
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FIG. 1 is a flow chart of a method for applying three-dimensional visualization of an anatomical atlas in a neurosurgical navigation system provided by the implementation of the 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 with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
The application of the principles of the present invention will be further described with reference to the accompanying drawings and the specific embodiments.
The invention provides a three-dimensional visualization application method of an anatomical atlas in a neurosurgical navigation system, which comprises the following steps of:
s101: and performing detailed digital three-dimensional reconstruction on the neuroanatomy map by adopting an advanced nonlinear interpolation method.
S102: a three-dimensional nonlinear registration method is adopted, the neuroanatomy atlas is firstly unified to the same coordinate system, and a three-dimensional standard digital atlas is obtained.
S103: and generating a three-dimensional color map.
S104: and constructing a visualization platform, displaying two-dimensional brain sections in three directions of the cross section, coronal plane and sagittal plane of the map, and displaying three-dimensional orthogonal brain sections.
S105: and establishing a corresponding relation between the anatomical organ and the anatomical name, and between the anatomical name and the color information.
S103, segmenting the obtained three-dimensional standard digital map data from structures in three directions of a cross section, a coronal plane and a sagittal plane, sampling to obtain a plurality of contour coordinate point information of each structure on different layers, generating an original data point set, and recording an anatomical name; interpolating the collected discontinuous original data point set by adopting a Cardinal spline interpolation value to generate a contour curve of the brain anatomical structure; and filling the color of the region surrounded by the generated contour curve by adopting a point-by-point judgment filling algorithm, filling the color of all the structures in the region according to the different layers of the different brain tissue structures to generate a two-dimensional color map, and then generating a three-dimensional color map by using the two-dimensional color map in the three directions of the cross section, the coronal plane and the sagittal plane according to a fixed layer spacing.
In S105, storing the anatomical names and the color information of the interested anatomical structures in a one-to-one corresponding mode, establishing the corresponding relation between the anatomical organs and the anatomical names and between the anatomical names and the color information, and storing the generic relation between different anatomical organs and the main area to which the anatomical organs belong in a hierarchical storage mode; the name of the interesting anatomical structure is displayed in real time by reading the color information corresponding to the interesting anatomical structure, and the dependency relationship stored in a hierarchical storage form among the anatomical structures is displayed by using a tree structure window.
The nonlinear interpolation adopts convolution-based nonlinear interpolation, and the influence function kernel adopts a basic spline function:
(b n ) -1 is a B-pattern stripe function filter.
In the process of generating the contour curve of the cerebral anatomical structure by interpolating the collected discontinuous original data point set by using the Cardinal spline interpolation value, the generation method of the contour curve comprises the following steps:
1) Given set value point column d 0 ,d 1 ,d 2 ,…,d m Supplement of auxiliary points d -2 ,d -1 8230a and d m+1 ,d m+2 8230the spline junction sequence is:
…≤t -1 ≤a＝t 0 ＜t 1 ＜t 2 ＜…＜t m-1 ＜t m ＝b≤t m+1 ≤…；
will { d } j Taking the curve as a Cardinal control vertex sequence to obtain an n-order B spline curve, and recording the curve as:
wherein N is j,n (t) is an n-th order B-spline basis function whose support is set to an intervalIs a real numberRounding;
structural curve d I (t), the interpolation condition is satisfied:
d I (t k )＝d k ,k＝0,1,2,…,m；
2) At each interval spline subinterval [ t ] i ,t i+1 ](i =0,1,2, \8230;, m-1) connecting two end points d (t) of the B-spline curve segment d (t) i ) And d (t) i+1 ) Is marked by a straight line segment of i (t), the equation is:
l i (t)＝(1-Φ i (t))d(t i )+Φ i (t)d(t i+1 ),t i ≤t≤t i+1 ；
and connect two adjacent Cardinal points d i And d i+1 Is marked by L i (t), the equation is:
l i (t)＝(1-Φ i (t))d(t i )+Φ i (t)d(t i+1 ),t i ≤t≤t i+1 ；
L i (t)＝(1-Φ i (t))d i +Φ i (t)d i+1 ,t i ≤t≤t i+1 ；
drawing a curve segment d (t) and a straight line segment l i (t) in the interval [ t i ,t i+1 ]The difference vector of (a):
δ i (t)＝d(t)-l i (t),t i ≤t≤t i+1 ；
the difference vector is expanded and contracted to obtain alpha delta i (t), α > 0, and translating it so that its origin falls on the straight line segmentAt the corresponding point, namely:
d I (t)＝L i (t)+αδ i (t),t i ≤t≤t i+1 ,i＝0,1,2,…,m-1；
or written as:
d I (t)＝[(1-Φ i (t))d i +Φ i (t)d i+1 ]+α[d(t)-(1-Φ i (t))d(t i )-Φ i (t)d(t i+1 )]；
t i ≤t≤t i+1 ,i＝0,1,2,…,m-1；
function phi i (t) satisfies the following condition:
Φ i (t) in the interval [ t i ,t i+1 ]Has a continuous derivative up to order n-2;
obtaining:
Φ i (t) in the interval [ t i ,t i+1 ]Is a monotonically increasing function to avoid straight line segments l i (t) and L i (t) the appearance of a heavy node;
the hierarchical storage mode is adopted to store the generic relation among different anatomical organs and the belonging main area; the method comprises the following steps:
the first method is as follows: in the record cache, selecting record data with the same order of magnitude as the data to be added for replacement; or the second mode: in the record cache, selecting a record cache page with different orders of magnitude from the data to be added, recovering the space occupied by the cache page, distributing a new record cache page for the data to be added by utilizing the recovered space, and writing the data to be added into the new record cache page;
wherein the first mode or the second mode is selected according to the following method:
obtaining the access frequency Frec of the record data with the same order of magnitude as the data to be added and the access frequency Fpage of the record cache page with the different order of magnitude as the data to be added;
judging whether Frec > replace _ page _ ratio _ Fpage is established or not, if so, selecting the first mode, and otherwise, selecting the second mode;
wherein, the replace _ page _ ratio is a preset replacement control parameter, and the replace _ page _ ratio belongs to (0, 1);
the method for obtaining the access frequency Fpage of the record cache page with different orders of magnitude from the data to be added comprises the following steps:
Fpage＝(Fmin+Fmax)/2*N；
wherein Fmin is an access frequency of data with the earliest timestamp in the record cache page, fmax is an access frequency of data with the latest timestamp in the record cache page, and N is a total data record amount of the record cache page;
the method for reading the color information corresponding to the interesting anatomical structure includes the following steps: performing over-segmentation on the image by using a preset over-segmentation algorithm to obtain at least one region, wherein the color values of all pixel points in the same region are the same;
determining a color value and a centroid for each of the regions;
establishing the significance model according to the color values corresponding to the regions and the centroids of the regions;
the significance model is as follows:
wherein S is i1 Is a region R i Significance value of any pixel point, w (R) j ) Is a region R j Number of pixels in, D S (R i ,R j ) For characterizing said region R i And the region R j Measure of the difference in spatial position between, D C (R i ,R j ) For characterizing said region R i And said region R j The color difference between the two images is measured, N is the total number of the areas obtained after the image is subjected to over-segmentation, D S (R i ,R j ) Comprises the following steps: d S (R i ,R j )＝exp(-(Center(R i )-Center(R j )) 2 /σ s 2 )；Center(R i ) Is the region R i Center of mass of (R) j ) Is the region R j When the coordinates of each pixel point in the image are normalized to [0, 1]]When the current is over;
or classifying each pixel point in the image according to the color value of each pixel point, and classifying the pixel points with the same color value into the same color type;
and establishing the significance model according to the color value of each color type.
The method for generating the n-order B-spline curve comprises the following steps:
(1) B spline interpolation curved surface of bicubic on the rectangular domain, giving the rectangular domain [ a, B; c, d ] an expanded partition:
u -9 ≤u -6 ≤u -3 ≤a＝u 0 ＜u 1 ＜…＜u 3i ＜u 3i+1 ＜…＜u 3m-1 ＜u 3m ＝b≤u 3(m+1) ≤u 3(m+2) ≤u 3(m+3) ，
v -9 ≤v -6 ≤v -3 ≤c＝v 0 ＜v 1 ＜…＜v 3i ＜v 3i+1 ＜…＜v 3n-1 ＜v 3n ＝d≤v 3(n+1) ≤v 3(n+2) ≤v 3(n+3) ；
and Cardinal control grid vertex set:
{d ij ,i＝-1，0,1,…m+1,j＝-1,0,1,…,n+1}；
then the bi-cubic B-spline surface over the rectangular field [ a, B ] × [ c, d ] is noted as:
wherein the cubic B-spline basis function N i,4 (u) andthe spline nodes on the support are respectively as follows:
{u 3(i-2) ,u 3(i-1) ,u 3i ,u 3(i+1) ,u 3(i+2) and { v } 3(j-2) ,v 3(j-1) ,v 3j ,v 3(j+1) ,v 3(j+2) }；
Constructing a bicubic B-spline interpolation surface d I (u, v) are as follows:
wherein:
u 3i ≤u≤u 3i+3 ,v 3j ≤v≤v 3j+3 ,i＝0,1,2,…,m-1,j＝0,1,2,…,n-1；
u and v replace t in the original expression respectively:
h 3i+k ＝u 3i+k+1 -u 3i+k and
respectively substitute for h in the original formula 3i+k ,k＝0,1,2；
(2) A double fourth-order quartic B spline interpolation surface is arranged on a rectangular domain, and a rectangular domain [ a, B; c, d ] an expanded partition:
u -6 ≤u -4 ≤u -2 ≤a＝u 0 ＜u 1 ＜…＜u 2i ＜u 2i+1 ＜…＜u 2m-1 ＜u 2m ＝b≤u 2(m+1) ≤u 2(m+2) ≤u 2(m+3) ；
v -6 ≤v -4 ≤v -2 ≤c＝v 0 ＜v 1 ＜…＜v 2i ＜v 2i+1 ＜…＜v 2n-1 ＜v 2n ＝d≤v 2(n+1) ≤v 2(n+2) ≤v 2(n+3) ；
and Cardinal control grid vertex set { d ij I = -1,0,1, \ 8230m +1, j = -1,0,1, 8230n +1, then rectangular field [ a, b ]]×[c,d]The bicubic B-spline surface above is noted:
wherein the cubic B-spline basis function omega i (u) andthe spline nodes on the support set are respectively as follows:
{u 2(i-2) ,u 2(i-1) ,u 2i ,u 2(i+1) ,u 2(i+2) and { v } 2(j-2) ,v 2(j-1) ,v 2j ,v 2(j+1) ,v 2(j+2) }；
Constructing a double fourth-order quartic B-spline interpolation surface r I (u, v) are as follows:
wherein:
u 3i ≤u≤u 3i+3 ,v 3j ≤v≤v 3j+3 ,i＝0,1,2,…,m-1,j＝0,1,2,…,n-1；
u and v replace t in the original expression respectively:
h 2i+k ＝u 2i+k+1 -u 2i+k and
respectively substitute h in the original formula 2i+k ,k＝0,1。
In the segmentation of the obtained three-dimensional standard digital atlas data from structures in three directions of a transverse plane, a coronal plane and a sagittal plane, the segmentation method comprises the following steps:
firstly, comparing two adjacent frames of images, finding out the changed areas of all the images, and then obtaining a set of non-overlapping rectangular areas with the minimum area according to the coordinates of changed pixel points; only sending image data and corresponding coordinate information contained in the rectangular area set each time;
obtaining a change rectangular area according to the coordinates of the pixel points, wherein the formulas (1) and (2) are formulas for judging the range of the rectangle R according to the change pixel points;
R l ≤P x AND R t ＝P yi (1)
R r ≥P x AND R b ≥P y (2)
wherein R is l And R t The abscissa and ordinate, R, representing the upper left corner of the rectangle r And R b The abscissa and ordinate, P, representing the lower right corner of the rectangle x And P y Abscissa and ordinate, P, representing varying pixel points y0 Representing the ordinate of the first changed pixel.
Obtaining the range of the change rectangular area according to the formula (1) and the formula (2); firstly, storing data of two adjacent bitmaps in front and back, and judging whether the values of pixels corresponding to the two screens in front and back are changed or not; when a changed sample point is detected for the first time, the coordinates (P) of the sample point will change X0 ,P Y0 ) Recording is performed as the coordinate (R) of the upper left corner of the changed rectangular region l ,R t ) And identify row no change as false; continuing comparison, when different sampling points are detected again, firstly marking the row unchanged as false, and then marking the abscissa P of the sampling point x The abscissa R of the same rectangle at the upper left corner l Compare and take the minimum value, while the coordinate (R) of the lower right corner of the rectangle r ,R b ) Coordinates (P) of the meeting point x ,P y ) Comparing and taking the maximum value; namely:
R l ＝min(P xi ,R l )(i>1) R t ＝P yi (i＝1)
R r ＝max(R xi ,R r )(i>1) R b ＝max(R yi ,R b )(i>1)
when detecting that the sampling point values of a certain row are all the same, a changed rectangular area block is obtained.
Further, judging whether pixels corresponding to two frames of screen images in the front and rear image buffer areas change by adopting an alternate direct comparison method so as to find out a changed rectangular area; and according to the principle of from top to bottom and from left to right, finding out all the change regions of the next frame image relative to the previous frame image and obtaining a set of non-overlapping rectangular regions with the minimum area based on a rectangular segmentation algorithm.
Judging whether pixels corresponding to the front and rear two frames of screen images change or not by adopting an alternate column direct comparison method, extracting pixels corresponding to the front and rear two frames of images from left to right at intervals of N columns in a row unit as sampling points, and comparing whether values of corresponding pixel points are the same or not; according to different application scenes and the requirement of bandwidth, the number N of the interval columns is adjusted, and the smaller the value of N is, the longer the time required for detection by the direct comparison method of the interval columns is.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (6)
1. A three-dimensional visualization application method of an anatomical atlas in a neurosurgical navigation system is characterized by comprising the following steps of:
firstly, carrying out detailed digital three-dimensional reconstruction on a neuroanatomy map by adopting an advanced nonlinear interpolation method;
secondly, unifying the neuroanatomy maps into the same coordinate system by adopting a three-dimensional nonlinear registration method to obtain a three-dimensional standard digital map;
step three, generating a three-dimensional color map;
dividing the obtained three-dimensional standard digital map data from structures in three directions of a cross section, a coronal plane and a sagittal plane, sampling to obtain a plurality of contour coordinate point information of each structure on different layers, generating an original data point set, and recording an anatomical name; interpolating the collected discontinuous original data point set by adopting a Cardinal spline interpolation value to generate a contour curve of the brain anatomical structure; filling the color of the region surrounded by the generated contour curve by adopting a point-by-point judgment filling algorithm, filling the color of the region of all the structures according to the different layers of the brain tissue structures to generate a two-dimensional color map, and then generating a three-dimensional color map by using the two-dimensional color map in the three directions of the cross section, the coronal plane and the sagittal plane according to a fixed layer spacing;
constructing a visual platform, displaying two-dimensional brain sections in three directions of the cross section, coronal plane and sagittal plane of the atlas, and simultaneously displaying three-dimensional orthogonal brain sections;
establishing corresponding relations between the anatomical organs and anatomical names and between the anatomical names and color information;
storing the anatomical names and the color information of the interesting anatomical structures in a one-to-one corresponding mode, establishing the corresponding relation between the anatomical organs and the anatomical names and between the anatomical names and the color information, and storing the generic relation between different anatomical organs and the main area of the anatomical organs in a hierarchical storage mode; the method comprises the steps of reading color information corresponding to an interested anatomical structure to display the name of the interested anatomical structure in real time, and displaying the subordination relation stored in a hierarchical storage form among the anatomical structures by using a tree structure window;
the nonlinear interpolation adopts convolution-based nonlinear interpolation, and the influence function kernel adopts a basic spline function:
(b n ) -1 is a type B spline function filter;
in the generating of the contour curve of the cerebral anatomical structure by interpolating the collected discontinuous original data point set by using Cardinal spline interpolation, the generating method of the contour curve comprises the following steps:
1) Given set value point column d 0 ,d 1 ,d 2 ,…,d m Supplementary auxiliary point d -2 ,d -1 8230a and d m+1 ,d m+2 8230the spline junction sequence is:
…≤t -1 ≤a＝t 0 ＜t 1 ＜t 2 ＜…＜t m-1 ＜t m ＝b≤t m+1 ≤…；
will { d } j Taking the curve as a Cardinal control vertex sequence to obtain an n-order B spline curve, and recording the curve as:
wherein N is j,n (t) is an n-th order B-spline basis function whose support is set to an interval Is a real numberGetting the whole;
constructional curve d I (t), the interpolation condition is satisfied:
d I (t k )＝d k ,k＝0,1,2,…,m；
2) Spline subinterval [ t ] at each interval i ,t i+1 ](i =0,1,2, \ 8230;, m-1) connecting the two end points d (t) of the B-spline curve segment d (t) i ) And d (t) i+1 ) Is denoted by l i (t), the equation is:
l i (t)＝(1-Φ i (t))d(t i )+Φ i (t)d(t i+1 ),t i ≤t≤t i+1 ；
and connect two adjacent Cardinal points d i And d i+1 Is marked by L i (t), the equation is:
l i (t)＝(1-Φ i (t))d(t i )+Φ i (t)d(t i+1 ),t i ≤t≤t i+1 ；
L i (t)＝(1-Φ i (t))d i +Φ i (t)d i+1 ,t i ≤t≤t i+1 ；
drawing a curved line segment d (t) and a straight line segment l i (t) in the interval [ t i ,t i+1 ]Difference of direction ofQuantity:
δ i (t)＝d(t)-l i (t),t i ≤t≤t i+1 ；
the difference vector is expanded and contracted to obtain alpha delta i (t), α > 0, and translating it so that its origin falls on the straight line segmentAt the corresponding point, namely:
d I (t)＝L i (t)+αδ i (t),t i ≤t≤t i+1 ,i＝0,1,2,…,m-1；
or written as:
d I (t)＝[(1-Φ i (t))d i +Φ i (t)d i+1 ]+α[d(t)-(1-Φ i (t))d(t i )-Φ i (t)d(t i+1 )]；
t i ≤t≤t i+1 ,i＝0,1,2,…,m-1；
function phi i (t) satisfies the following condition:
Φ i (t) in the interval [ t i ,t i+1 ]Has a continuous derivative up to order n-2;
Φ i (t i )＝0，Φ i (t i+1 )＝1，
obtaining:
d I (t k )＝d k ,
Φ i (t) in the interval [ t i ,t i+1 ]Is a monotonically increasing function to avoid straight line segments l i (t) and L i (t) the appearance of a heavy node;
the hierarchical storage mode is adopted to store the generic relation among different anatomical organs and the belonging main area; the method comprises the following steps:
the method I comprises the following steps: in the record cache, selecting record data with the same order of magnitude as the data to be added for replacement; or the second mode: in the record cache, selecting a record cache page with different orders of magnitude from the data to be added, recovering the space occupied by the cache page, distributing a new record cache page for the data to be added by utilizing the recovered space, and writing the data to be added into the new record cache page;
wherein the first mode or the second mode is selected according to the following method:
obtaining the access frequency Frec of the record data with the same order of magnitude as the data to be added and the access frequency Fpage of the record cache page with the different order of magnitude as the data to be added;
judging whether Frec > place _ page _ ratio is true or not, if true, selecting the first mode, and if not, selecting the second mode;
wherein, the replace _ page _ ratio is a preset replacement control parameter, and the replace _ page _ ratio belongs to (0, 1);
the method for obtaining the access frequency Fpage of the record cache page with different orders of magnitude from the data to be added comprises the following steps:
Fpage＝(Fmin+Fmax)/2*N；
wherein Fmin is an access frequency of data with the earliest timestamp in the record cache page, fmax is an access frequency of data with the latest timestamp in the record cache page, and N is a total data record amount of the record cache page;
the method for reading the color information corresponding to the interesting anatomical structure includes the following steps: performing over-segmentation on the image by using a preset over-segmentation algorithm to obtain at least one region, wherein the color values of all pixel points in the same region are the same;
determining a color value and a centroid for each of the regions;
establishing the significance model according to the color values corresponding to the regions and the centroids of the regions;
the significance model is as follows:
wherein S is i1 Is a region R i Significance value of any pixel point, w (R) j ) Is a region R j Number of pixels in, D S (R i ,R j ) For characterizing said region R i And said region R j Measure of the difference in spatial position between, D C (R i ,R j ) For characterizing said region R i And the region R j The color difference between the two images is measured, N is the total number of the areas obtained after the image is subjected to over-segmentation, D S (R i ,R j ) Comprises the following steps:Center(R i ) Is the region R i Center of mass of (R) j ) Is the region R j When the coordinates of each pixel point in the image are normalized to [0, 1]]When the current is over;
or classifying each pixel point in the image according to the color value of each pixel point, and classifying the pixel points with the same color value into the same color type;
and establishing the significance model according to the color value of each color type.
2. The method for three-dimensional visualization of an anatomical atlas in a neurosurgical navigation system of claim 1, wherein the method for generating the n-order B-spline curve comprises:
(1) A bicubic B spline interpolation surface is arranged on a rectangular domain, and the rectangular domain [ a, B is given; c, d ] an expanded partition:
u -9 ≤u -6 ≤u -3 ≤a＝u 0 ＜u 1 ＜…＜u 3i ＜u 3i+1 ＜…＜u 3m-1 ＜u 3m ＝b≤u 3(m+1) ≤u 3(m+2) ≤u 3(m+3) ，
v -9 ≤v -6 ≤v -3 ≤c＝v 0 ＜v 1 ＜…＜v 3i ＜v 3i+1 ＜…＜v 3n-1 ＜v 3n ＝d≤v 3(n+1) ≤v 3(n+2) ≤v 3(n+3) ；
and Cardinal control grid vertex set:
{d ij ,i＝-1，0,1,…m+1,j＝-1,0,1,…,n+1}；
then the bi-cubic B-spline surface over the rectangular field [ a, B ] × [ c, d ] is noted as:
wherein the cubic B-spline basis function N i,4 (u) andthe spline nodes on the support set are respectively as follows:
{u 3(i-2) ,u 3(i-1) ,u 3i ,u 3(i+1) ,u 3(i+2) and { v } and 3(j-2) ,v 3(j-1) ,v 3j ,v 3(j+1) ,v 3(j+2) }；
constructing a bicubic B-spline interpolation curved surface d I (u, v) are as follows:
wherein:
u 3i ≤u≤u 3i+3 ,v 3j ≤v≤v 3j+3 ,i＝0,1,2,…,m-1,j＝0,1,2,…,n-1；
u and v respectively replace t in the original expression:
h 3i+k ＝u 3i+k+1 -u 3i+k and
respectively substitute h in the original formula 3i+k ,k＝0,1,2；
(2) A double fourth-order quartic B spline interpolation surface is arranged on a rectangular domain, and the rectangular domain [ a, B is given; c, d ] an expanded partition:
u -6 ≤u -4 ≤u -2 ≤a＝u 0 ＜u 1 ＜…＜u 2i ＜u 2i+1 ＜…＜u 2m-1 ＜u 2m ＝b≤u 2(m+1) ≤u 2(m+2) ≤u 2(m+3) ；
v -6 ≤v -4 ≤v -2 ≤c＝v 0 ＜v 1 ＜…＜v 2i ＜v 2i+1 ＜…＜v 2n-1 ＜v 2n ＝d≤v 2(n+1) ≤v 2(n+2) ≤v 2(n+3) ；
and Cardinal control mesh vertex set { d ij I = -1,0,1, \ 8230m +1, j = -1,0,1, 8230n +1, then rectangular field [ a, b ]]×[c,d]The bicubic B-spline surface above is noted:
wherein the cubic B-spline basis function omega i (u) andthe spline nodes on the support set are respectively as follows:
{u 2(i-2) ,u 2(i-1) ,u 2i ,u 2(i+1) ,u 2(i+2) and { v } 2(j-2) ,v 2(j-1) ,v 2j ,v 2(j+1) ,v 2(j+2) }；
Constructing a dual fourth-order quartic B-spline interpolation surface r I (u, v) are as follows:
wherein:
u 3i ≤u≤u 3i+3 ,v 3j ≤v≤v 3j+3 ,i＝0,1,2,…,m-1,j＝0,1,2,…,n-1；
u and v replace t in the original expression respectively:
h 2i+k ＝u 2i+k+1 -u 2i+k and
respectively substitute h in the original formula 2i+k ,k＝0,1。
3. The method for applying three-dimensional visualization of an anatomical atlas in a neurosurgical navigation system according to claim 1, wherein the three-dimensional standard digitized atlas data is obtained from segmentation of structures in three directions, a transverse plane, a coronal plane and a sagittal plane, the segmentation method comprising:
firstly, comparing two adjacent frames of images, finding out the changed regions of all the images, and then obtaining a set of non-overlapping rectangular regions with the minimum area according to the coordinates of changed pixel points; only sending image data and corresponding coordinate information contained in the rectangular area set each time;
obtaining a change rectangular area according to the coordinates of the pixel points, wherein the formulas (1) and (2) are formulas for judging the range of the rectangle R according to the change pixel points;
R l ≤P x AND R t ＝P yi (1)
R r ≥P x AND R b ≥P y (2)
wherein R is l And R t The abscissa and ordinate, R, representing the upper left corner of the rectangle r And R b The abscissa and ordinate, P, representing the lower right corner of the rectangle x And P y Abscissa and ordinate, P, representing varying pixel points y0 Representing the ordinate of the first changed pixel.
4. The method for three-dimensional visualization of an anatomical atlas in a guidance system for neurosurgery according to claim 3, wherein the variation is obtained according to equations (1) and (2)The range of the rectangular area is formed; firstly, storing data of two adjacent bitmaps in front and back, and judging whether values of pixels corresponding to the screens of the front frame and the back frame are changed or not; when a changed sample point is detected for the first time, the coordinates (P) of the sample point will change X0 ,P Y0 ) Recording is performed as the coordinate (R) of the upper left corner of the changed rectangular region l ,R t ) And identify row no change as false; continuing the comparison, when different sampling points are detected again, firstly marking the row unchanged as false, and then marking the abscissa P of the sampling point as the abscissa P x The abscissa R of the same rectangle at the upper left corner l Compare and take the minimum while taking the coordinate (R) of the lower right corner of the rectangle r ,R b ) Coordinates (P) of meeting point x ,P y ) Comparing and taking the maximum value; namely:
R l ＝min(P xi ,R l ) (i>1) R t ＝P yi (i＝1)
R r ＝max(R xi ,R r ) (i>1) R b ＝max(R yi ,R b ) (i>1)
when detecting that the sampling point values of a certain row are all the same, a changed rectangular area block is obtained.
5. The method for three-dimensional visualization of an anatomical atlas in a neurosurgical navigation system of claim 3, wherein an alternate direct comparison method is used to determine whether the pixels corresponding to two frames of screen images in the front and back image buffers change so as to find out a changed rectangular region; and according to the principle of from top to bottom and from left to right, finding out all the change regions of the next frame image relative to the previous frame image and obtaining a set of non-overlapping rectangular regions with the minimum area based on a rectangular segmentation algorithm.
6. The three-dimensional visualization application method of the anatomical atlas in the neurosurgical navigation system of claim 5, wherein a separation direct comparison method is adopted to judge whether the pixels corresponding to the front and rear two frames of screen images change, the pixels corresponding to the front and rear two images are extracted from left to right at intervals of N columns in a row unit as sampling points, and the values of the corresponding pixel points are compared to judge whether the values are the same; according to different application scenes and the requirement of bandwidth, the number N of the interval columns is adjusted, and the smaller the value of N is, the longer the time required by the direct comparison method of the interval columns is for detection.
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