CN116206735A - Medical data visualization method based on histogram and nonlinear embedded transfer function - Google Patents

Abstract

The invention discloses a medical data visualization method based on a histogram and a nonlinear embedded transfer function, which comprises the following steps: (1) The medical human tissue data of the patient is converted into volume data for preprocessing; (2) Transfer function edit points TF-lets are associated with the tissue volume data, each node providing TF for transfer function design using TF of the transfer function TF editor corresponding to a TFlet of the original nonlinear transfer function; (3) Synchronously moving several nodes of TF-let up and down to adjust transparency of each organization picture; (4) Synchronously moving a plurality of nodes of the TF-let left and right to adjust the definition of each organization picture; (5) Multiple users with knowledge of different domains can collaboratively browse a single medical data and edit their sub-regions to improve efficiency and accuracy. The invention can rapidly load TF-let and realize result editing in the vertical direction and the horizontal direction of TF-of-TF; and one or more organizations are edited cooperatively according to the domain knowledge of different experts, so that the efficiency and the accuracy can be improved.

Description

Medical Data Visualization Method Based on Histogram and Nonlinear Embedded Transfer Function

technical field

The invention belongs to the technical field of medical data interpretation and visualization, and specifically proposes a medical data visualization method based on a histogram and a nonlinear embedded transfer function.

Background technique

Transfer function design is a traditional approach to volume visualization that assigns each voxel in the volume data a different color and transparency scheme. In recent years, volume visualization has been widely used in many scientific and engineering fields such as oil/gas exploration, atmospheric and ocean simulation, medical diagnosis, etc., and can help understand complex observed or simulated volume data. With advances in graphics hardware and volume visualization algorithms, volume rendering has become faster and more precise. Therefore, the focus of volume rendering has shifted to the design of transfer functions.

In recent years, in order to improve the efficiency and accuracy of volumetric visualization, there are generally two methods for designing transfer functions, namely data-centric methods and image-centric methods. However, these two traditional methods can only load one TF or TF-let at each exploration time, and cannot switch between different visualization results. Moreover, when changing the transparency and clarity of an object, all the nodes need to be moved, and the operation is complicated, so a more complete transfer function design is urgently needed.

Overall, the current transfer function design still has many limitations, such as: not high efficiency, tedious when users explore, can only load one TF at a time, cannot switch between different visualization results, change the organization transparency and clarity The operation is complicated at a certain time. The above limitations are mainly due to the complexity and diversity of human tissues, as well as the diversity of user needs for observing and analyzing tissues.

Contents of the invention

Purpose of the invention: The present invention proposes a medical data visualization method based on histogram and nonlinear embedded transfer function, according to the domain knowledge of different experts, one or more organizations can be collaboratively edited to improve efficiency and accuracy.

Technical solution: The present invention aims at a medical data visualization method based on histogram and nonlinear embedded transfer function, which specifically includes the following steps:

(1) Preprocessing the pre-acquired medical human tissue data to obtain three-dimensional volume data;

(2) In order to edit the transfer function more effectively for users, the design of nonlinear histogram and non-uniform grid is used in the background of constructing the histogram nonlinear embedded transfer function;

(3) In the TF editor, an attribute corresponds to a transfer function similar to a triangular wavelet to extract the TF-let of the tissue in the medical data; move several nodes of the TF-let up and down synchronously to adjust the texture of each tissue image Transparency; the color and transparency of each point between control points are obtained from the difference between the two nearest control points; move the TF-let nodes synchronously left and right to adjust the clarity of each tissue image; the color of each point between control points and sharpness are obtained from the difference between the two nearest control points;

(4) Design the transfer function method TF-of-TF based on the transfer function to provide users with visual cues for exploration, the TF-of-TF is to simply click the corresponding TF-let node to fuse multiple TF-lets;

(5) Multiple users with different domain knowledge can collaboratively browse a single medical data, edit its sub-regions, and improve the efficiency and accuracy of medical data exploration.

Further, the implementation process of the step (1) is as follows:

The medical human body tissue data is sliced and sorted, and the reflection time interval between each slice is 1 millisecond; according to the reflection time alignment data, all data slices are aligned with the actual physical space one by one, and the aligned data is a three-dimensional volume Data; transfer 3D volume data from the CPU to the GPU.

Further, the implementation process of the step (2) is as follows:

The vertical axis of the histogram represents opacity, and the horizontal axis adopts a nonlinear mapping design; render the histogram into a grid to reduce visual confusion; calculate the bin size of the data when preprocessing the data, and use it to draw the histogram; group The distance calculation formula is:

Among them, b _i represents the size of each bin, and i represents the number of groups; when the scale value is low, the width of the vertical axis is enlarged, and the following formula is used to calculate:

The vertical axis of the nonlinear TF editor represents the opacity, that is, the alpha value;

Enlarging the width of the vertical axis at low scale values, the control points are transformed to non-linear coordinates as follows:

Among them, α represents the degree of amplification; the larger α is, the wider the low value part of the nonlinear tf is.

Further, the implementation process of the step (3) is as follows:

When the user obtains the best rendering result through TF-let, all color schemes are serialized to disk with the function of fast loading and reloading for subsequent loading;

When the user decides to serialize TF control point data and load it, a TF let node will appear, and the vertical coordinates of each node represent the maximum opacity of the control point in the corresponding TF let;

TF-lets are associated with tissue data, all TF-lets are serialized for reloading and subsequent analysis; TF using the TF editor provides transfer function design with each node of TF corresponding to one of the original nonlinear transfer function TF-let; when doing TF editing, volumetric data are classified according to their distribution in feature space.

Further, the implementation process of the step (4) is as follows:

(41) Bind the points in the same organization to each other. There are three points abc in the editor of the same organization area. Click to bind these three points, and drag the highest of these three points The point will make the bound three points move left and right at the same time without changing the relative position of the three points on the tissue, improving the efficiency of changing image clarity and transparency; what these three points are bound into is TF-of- The first TF in the TF method;

(42) Take the three points abc bound together in step (41) and take the b point with the highest y-axis value. The coordinates of b are (x, y), and the second TF point (x, -y) is correspondingly set It is the associated point of the bound abc three points, moving the TF point can control the movement of the abc three points, this TF is the second TF in the TF-of-TF method;

(43) The user uses the mouse press event function to immediately respond to the mouse message, and increases the activity variable to determine whether the mouse clicks the control point; keeps the internal relationship of the TF nodes in the TF-let unchanged, and moves as a whole, dragging up and down Animating a control point is also able to change the value of the entire point, thus changing the transparency of the tissue corresponding to the TF let.

Beneficial effect: compared with prior art, the beneficial effect of the present invention:

The invention can quickly load TF-let, and the user can obtain the serialized TF-Let rendering result as long as the user saves the previous corresponding TF-Let; by clicking the TF-Let node, the user can effectively view any organization in the data set , when loading a new dataset, the user needs to explore the data and find a single TF-Let for all tissues;

The present invention realizes the result editing in the vertical direction of TF-of-TF: the user can adjust the combination by moving a single node up and down; when it is necessary to observe a part of it carefully and clearly, the user needs to modify the transparency of the blood vessel; at this time, The transparency can be changed only by clicking and moving up and down with the mouse, which is more time-saving and efficient than traditional methods; in addition, users can stretch multiple tissues proportionally through the transfer function to obtain better observation effects;

The present invention also realizes the result editing in the TF-of-TF horizontal direction: the user can control the resolution of the displayed image by moving the TF-of-TF point left and right, such as the skin, blood vessels and bones of the hand data set; Clicking the mouse and adjusting the up and down movement of the skin points can highlight the required parts for easy observation; in addition, users can quickly switch between different medical organizations by dragging the TF-let left and right, which is easy to operate;

According to the domain knowledge of different experts, the present invention collaboratively edits one or more organizations, which can improve efficiency and accuracy; users can also load updated files to obtain specific fusion visual effects, so that they can better understand the corresponding organizations Part; the realization of multi-user operation enables multiple users to make full use of their knowledge background in different fields.

Description of drawings

Fig. 1 is a flowchart of the present invention;

Fig. 2 is a schematic diagram of a nonlinear transfer function editor based on a histogram in the present invention;

Fig. 3 is a schematic illustration of TF-of-TF in the present invention;

FIG. 4 is a schematic diagram of a method for multi-user fusion in the present invention;

Fig. 5 is a schematic flow chart of stereoscopic rendering using a function editor in the present invention;

Figure 6 is a schematic diagram of a single TF-let and rendering results of different parts in the data set CHEST; where (a) is a schematic diagram of a single TF-let of the chest; (b) is a schematic diagram of the rendering results of TF-let on the chest; (c) A schematic diagram of a single TF-let of a sternum; (d) a schematic diagram of the rendering result of TF-let on a sternum; (e) a schematic diagram of a single TF-let of a hand bone; (f) a schematic diagram of the rendering result of a TF-let hand bone; (g) is a schematic diagram of a single TF-let of the skull; (h) is a schematic diagram of the rendering result of TF-let on the skull;

Fig. 7 is the evaluation chart of data set HEAD and data set CHEST in the present invention, wherein (a) is the rendering result schematic diagram of data set HEAD using traditional linear transfer function method; (b) uses the proposed method to carry out data set HEAD A schematic diagram of the rendering result; (c) is a schematic diagram of the rendering result of the dataset CHEST using a linear method; (d) is a schematic diagram of the rendering result of the dataset CHEST using the method proposed by the present invention;

The evaluation case of data set HAND among Fig. 8 the present invention; Wherein (a) (b) is the traditional linear transfer function editing result schematic diagram that two users show for data set HAND; (c) uses the method that the present invention proposes for the user to Find a schematic diagram of blood vessels;

Figure 9 is a schematic diagram of changing the transparency and clarity of the skin by clicking on the mouse to move the skin point; where (a) is the original picture; (b) is the user of the present invention moves the first skin point upwards to reduce the transparency of the skin (c) is a schematic diagram that the user of the present invention moves the first skin point upwards to reduce the transparency of the skin; (d) is the user of the present invention moves the second skin point upwards to reduce the transparency of the skin (e) is a schematic diagram for the user of the present invention to improve the clarity of the skin by moving the second skin point to the right with the mouse; (f) is a schematic diagram for the user of the present invention to improve the clarity of the skin by moving the third skin point to the left with the mouse A schematic diagram of the clarity of the skin; (g) is a schematic diagram of increasing the transparency of the skin by the user of the present invention moving the third skin point downward through the mouse;

Fig. 10 is a schematic diagram of blood vessels in the data set HAND viewed by users of the present invention by adjusting the nodes in TF-of-TF;

Figure 11 is a schematic diagram of the multi-user fusion results of skin, blood vessels and bones based on the data set HAND in the present invention;

Fig. 12 is a schematic diagram of fusion using the present invention; wherein (a) is a schematic diagram of fusion results of skin and bones in data set HEAD; (b) is a schematic diagram of fusion results of chest (together with lungs) and sternum in data set CHEST.

Detailed ways

The present invention will be described in further detail below in conjunction with the accompanying drawings.

The present invention provides a medical data visualization method based on histogram and nonlinear embedded transfer function, as shown in Figure 1, first, the raw data is preprocessed into a volume; then the user uses the visual cue provided by the nonlinear histogram to adjust the nonlinear transfer function; after adjustment all TF-lets are serialized to disk, and they will be deserialized for fast reloading. The last few TF-lets can be fused into the final nonlinear transfer function in a focus-and-contexts manner. Specifically include the following steps:

Step 1: Preprocessing the pre-acquired medical human tissue data to obtain 3D volume data.

Transform the patient's medical human tissue data into volume data preprocessing, including different attributes (tissue) blood, fat, soft tissue and bone in medical data; arrange all slices according to the real physical space to obtain three-dimensional volume data; system Load the processed 3D volume data into the system to provide data support for subsequent medical data visualization and exploration.

Step 2: In order to allow users to edit the transfer function more efficiently, the design of nonlinear histogram and non-uniform grid is used in the background of constructing histogram nonlinear embedded transfer function. The larger the histogram bin size, the wider the bin width is drawn in the background.

As shown in Figure 2, the corresponding bin size computes the bin width of the histogram. A TF-let in a graph can represent an attribute of volume data (eg, an organization). The leftmost TF-let represents the air, and the rightmost TF-let represents the skeleton. Each TF-let has its own TF-node for fast loading and fusion.

The vertical axis of the histogram represents opacity, and the horizontal axis adopts a nonlinear mapping design; the specific implementation method is: render the histogram into a grid to reduce visual clutter. When preprocessing the data, calculate the size of the bin of the data, which is used to draw the histogram. The group distance calculation formula is:

Where _bi represents the size of each bin, calculated with the following formula:

Enlarging the width of the vertical axis at low scale values, the control points are transformed to non-linear coordinates as follows:

Step 3: In the TF editor, an attribute corresponds to a transfer function similar to a triangular wavelet to extract the TF-let of the tissue in the medical data; move several nodes of the TF-let up and down synchronously to adjust the image of each tissue Transparency; the color and transparency of each point between control points are obtained from the difference between the two nearest control points; move the TF-let nodes synchronously left and right to adjust the clarity of each tissue image; the color of each point between control points and sharpness are obtained from the difference between the two nearest control points.

When users achieve the best rendering results with TF-let, they can serialize all color schemes to disk for subsequent loading with the help of fast loading and reloading functions. The interface of the quick loading function is designed as a two-dimensional area.

When the user decides to serialize the TF control point data and load it, a TF let node will appear, and the vertical coordinate (with absolute value) of each node represents the maximum opacity of the control point in the corresponding TF let.

TF-lets are associated with tissue data, and all TF-lets will be serialized for reloading and subsequent analysis. TF using the TF editor provides transfer function design with each node of TF corresponding to a TF let of the original nonlinear transfer function; when doing TF editing, the volume data is classified according to its distribution in the feature space.

Step 4: Design a wavelet-like short transfer function (TF-let) that can be quickly serialized and reloaded in subsequent explorations. Furthermore, a TF-let fusion method is designed to fuse multiple TF-lets by simply clicking the corresponding TF-let nodes. This method of fusing multiple TF-lets, called transfer function-based transfer function approach (TF-of-TF), provides visual cues for users to explore.

Users can adjust the visualization results through the nonlinear TF editor, and use the histogram nonlinear embedded transfer function, that is, the transfer function method based on the transfer function can provide users with visual clues for exploration. In the TF editor, an attribute corresponds to a wavelet-like transfer function, which is called a TF let in the present invention. As shown in Figure 3, first, the transparency of the tissue can be adjusted by dragging the TF-of-TF node up and down. You can then adjust the clarity of the tissue by dragging the TF-of-TF node left and right. Therefore, users are able to obtain the final visualization results without changing all the control points of the original TF-let.

Bind the points in the same organization, for example, there are three points abc in the editor of the same organization area, bind these three points by clicking, and drag the highest point of the three points to This design enables the three bound points to move left and right simultaneously without changing the relative positions of the three points on the tissue, thereby improving the efficiency of changing image clarity and transparency. These three points are bound into the first TF in the TF-of-TF method.

Take the point b with the highest y-axis value among the three points of abc bound together, assuming that the coordinates of b are (x, y), and correspondingly set the second TF point (x, -y) as the bound three points of abc , moving this TF point can control the movement of the three points abc, this TF is the second TF in the TF-of-TF method.

Users can use the mouse down event function to immediately respond to mouse messages and increment the active variable to determine whether the mouse clicked on a control point. It keeps the internal relationship of TF nodes in TF let unchanged and moves as a whole, and dragging the control point up and down can also change the value of the whole point, thereby changing the transparency of the organization corresponding to TF let.

Step 5: Multiple users with different domain knowledge are able to collaboratively browse a single medical data, edit its sub-regions, and improve the efficiency and accuracy of medical data exploration. As shown in Figure 4, multiple users edit the TF-of-TF nodes corresponding to the tissues they are interested in; the final rendering effect can be fused by fusing the TF-of-TF nodes.

For example, multiple doctor users want to edit the transparency and clarity of blood, bone, and skin in arm tissue separately, and this function can be realized through the current software. In the TF-let fusion stage, users can obtain any combination of multiple TF-let rendering results. Ability to view multiple focus groups and their contexts simultaneously using focus context technology. Also, they were able to delete any tissue by double-clicking on the TF-let node.

Focal and contextual visualization techniques explore volumetric data through TF-lets fusion. In order to improve the detection efficiency, a multi-user fusion function is designed. Taking medical data as an example, if the user wants to explore the adjacent soft tissue (context) while viewing blood vessels (focus attribute), the corresponding TF let node can be saved and added to the final fused TF. Multi-user fusion process, for example, user #01 saves the TF let corresponding to skin in a specific folder, user #02 saves the TF let corresponding to blood in a specific folder, and user #03 saves other TF let corresponding to bones in in the same folder. Three pre-saved TF let can be quickly loaded and integrated. When the user does not want to see the corresponding attribute in the final rendering result, the user can delete any one of the multiple TF let nodes from the final fused TF.

(a) to (h) in Fig. 6 are schematic diagrams of a single TF-let of each organization in the dataset of the present invention and the final rendering results. If the user has saved the corresponding TF-lets before, he can get the rendering result rendered by the serialized TF-lets. Take medical data, for example. All organized TF-lets can be serialized and saved to disk individually. Users can effectively view any organization in the dataset by clicking on a TF-let node. However, when a user loads a new dataset, the user is required to explore the data and find all organized individual TF-lets. Figure 6(a) shows a single TF-let for chest in the dataset CHEST. By clicking the corresponding TF-let node, the rendering result shown in Figure 6(b) is loaded. Figure 6(c), Figure 6(e) and Figure 6(g) are three single TF-lets of sternum, hand bone and skull respectively, Figure 6(d), Figure 6(f) and Figure 6 (h) in 6 are their corresponding rendering results.

(a)-(d) in Fig. 7 are the evaluation cases of the data set HEAD and the data set CHEST in the present invention. It is easy to obtain some interesting results by the proposed method, while it is difficult for users to obtain similar results by linear methods. (a)-(b) in Fig. 7 respectively show the corresponding results presented by the traditional linear transfer function method and the method proposed by the present invention. It is difficult to find clumps that may be some lesions on the surface of the chest in the dataset CHEST because the attributes of these tissues are in a small range, as shown in (c)-(d) in Figure 7. In Fig. 7 (a) data set HEAD uses the rendering result of traditional linear transfer function, in Fig. 7 (b) is the data set HEAD uses the rendering result of the method proposed by the present invention, the outline of various tissues in the red circle should be clear many. (c) in Figure 7 is the rendering result of the data set CHEST using the linear method, and (d) in Figure 7 is the rendering result of the data set CHEST using the method proposed by the present invention. The user can find some possibilities in the red circle on the chest surface. It is a mass of disease.

(a)-(c) in Fig. 8 are evaluation cases of the data set HAND in the present invention. (a)-(b) in Figure 8 are the results of editing the data set HAND shown by two users with a traditional linear transfer function. They tried to get the blood vessels by trial and error, but they found it difficult to get the best results, and it was time-consuming to get both results. (c) in FIG. 8 shows that the user uses the method proposed by the present invention to find blood vessels. Compared with the results extracted by traditional methods, users can easily get very clear results. In addition, through Figure 8(a)-(c) (above), it is also found that the contexts on blood vessels are clearer (ie, the outline and structure of bones).

For ordinary users, if you want to adjust each tissue to an easy-to-see state, you need to adjust it from the outside to the inside. Provides a way for the user to change the transparency and clarity of the skin by moving the skin points with mouse clicks. (a)-(b) in Fig. 9 shows that the user moves the first skin point up with the mouse to reduce the transparency of the skin. (b)-(c) in Fig. 9 shows that the user moves the first skin point up with the mouse to reduce the transparency of the skin. (c)-(d) in FIG. 9 is that the user moves the second skin point upwards through the mouse to reduce the transparency of the skin. (d)-(e) in FIG. 9 shows that the user moves the second skin point to the right through the mouse to improve the clarity of the skin. (e)-(f) in FIG. 9 shows that the user of the present invention moves the third skin point to the left through the mouse to improve the clarity of the skin. (f)-(g) in Fig. 9 shows that the user of the present invention moves the third skin point downward through the mouse to increase the transparency of the skin. Figure 9 (a)-(g) (upper picture) shows the change of skin transparency and clarity, and Figure 9 (a)-(g) (lower picture) shows that the user moves the skin point to change the transparency and clarity of the skin Spend.

In addition to serving ordinary users, the present invention also meets the needs of experts to view part of the organization. If an expert wants to see a certain blood vessel, he needs to adjust the transparency and clarity of the skin. First, he can adjust the clarity of the skin by moving the mouse left and right, as shown in Figure 10 (lower left and lower middle). Then the transparency of the skin can be adjusted by moving the mouse up and down, as shown in Figure 10 (lower middle and lower right). As shown in Figure 10 (upper image), the blood vessels that need to be observed are highlighted for easy observation. Suppose the user wants to view the blood vessels in the dataset HAND, just adjust the nodes in TF-of-TF (at the bottom of the TF editor).

Fig. 11 is the fusion result of multiple single results edited by different users based on the data set HAND of the present invention. The three single results in Figure 11 (left side) relate to the organization of the skin, blood vessels, and bones of the hand. Figure 11 (top right) shows the fusion of these three single results. Furthermore, if users want to explore bones with blood vessels instead of skin, they can remove the skin property edits by simply clicking on the corresponding TF-of-TF node in the transfer function, as shown in Figure 11 (bottom right ).

As shown in (a) in Figure 12, the final fusion result of the skin (upper left image) and bones (lower left image) of the data set HAND is shown in (a) (upper right image) in Figure 12. As shown in Figure 12(b), the final fusion results of the chest (upper left image) and sternum (lower left image) of the dataset CHEST are shown in Figure 12(b) (upper right image). In fact, users can effectively specify multiple focus organizations and multiple contexts to obtain arbitrary fusion combinations, and they only need to click on the corresponding TF-let-TF node to achieve, that is, in Figure 12 (a) the user only needs to You need to click the two TF-let nodes of the skin and bones to achieve the final fusion result; in (b) in Figure 12, the user only needs to click the two TF-let nodes of the sternum and the sternum to obtain the final fusion result .

Claims (5)

1. A medical data visualization method based on a histogram and a nonlinear embedded transfer function, comprising the steps of:

(1) Preprocessing medical human tissue data acquired in advance to obtain three-dimensional data;

(2) To edit transfer functions more efficiently for users, designs using non-linear histograms and non-uniform meshes in constructing a histogram non-linear in-line transfer function background;

(3) Extracting, in the TF editor, TF-lets of the tissue in the medical data, with an attribute corresponding to a transfer function similar to a triangular wavelet; synchronously moving several nodes of TF-let up and down to adjust transparency of each organization picture; the color and transparency of each point between the control points are obtained by the difference value of the two nearest control points; synchronously moving the nodes of the TF-let left and right to adjust the definition of each organization picture; the color and definition of each point between the control points are obtained by the difference value of the two nearest control points;

(4) Designing a transfer function method TF-of-TF based on a transfer function, which provides explored visual clues for users, and fusing a plurality of TF-lets by simply clicking corresponding TF-let nodes;

(5) Multiple users with knowledge in different fields can cooperatively browse single medical data, edit the subareas of the single medical data, and improve the efficiency and accuracy of medical data exploration.

2. The method for visualizing data based on histogram and nonlinear embedded transfer function as in claim 1, wherein said step (1) is implemented as follows:

medical human tissue data are subjected to slice arrangement, and the reflection time interval between each slice is 1 millisecond; aligning all data slices with the actual physical space one by one according to the reflection time-aligned well data, wherein the aligned data are three-dimensional data; three-dimensional volume data is transferred from the CPU to the GPU.

3. The method for visualizing data based on a histogram and a nonlinear in-line transfer function as in claim 1, wherein said step (2) is implemented as follows:

the vertical axis of the histogram represents opacity, and the horizontal axis adopts a nonlinear mapping design; rendering the histogram into a grid to reduce visual clutter; calculating the size of bin of the data when preprocessing the data, and drawing a histogram; the group distance calculation formula is:

wherein b _u The size of each bin is represented, and i represents the number of groups; the width of the vertical axis is scaled up at low scale values, calculated as:

the vertical axis of the nonlinear TF editor represents opacity, i.e. the alpha value;

the width of the vertical axis is enlarged at a low scale value, and the control point is transformed to nonlinear coordinates as follows:

wherein α represents the degree of magnification; the larger alpha, the wider the low value portion of the non-linearity tf.

4. The method for visualizing data based on histogram and nonlinear embedded transfer function as in claim 1, wherein said step (3) is implemented as follows:

when the user obtains the best rendering result through the TF-let, all color schemes are serialized into the disk by virtue of the functions of quick loading and reloading so as to be loaded subsequently;

when the user decides to serialize the TF control point data and load it, a TF let node appears, the vertical coordinates of each node representing the maximum opacity of the control point in the corresponding TF let;

the TF-lets are related to the tissue volume data, all TF-lets are serialized for reloading and subsequent analysis; using the TF of the TF editor to provide a TF-let for the transfer function design for each node of the TF corresponding to the original nonlinear transfer function; at the time of TF editing, the volume data is classified according to its distribution in the feature space.

5. The method for visualizing data based on histogram and nonlinear embedded transfer function as in claim 1, wherein said step (4) is implemented as follows:

(41) Binding points in the same tissue with each other, binding the three points in an editor of the same tissue area in a clicking mode, and dragging the highest point in the three points can enable the three bound points to move left and right at the same time without changing the relative positions of the three points on the tissue, so that the efficiency of changing the definition and transparency of the image is improved; the three points are bound to the first TF in the TF-of-TF method;

(42) Taking the b point with the highest y-axis value from the abc three points bound together in the step (41), wherein the coordinates of b are (x, y), correspondingly setting a second TF point (x, -y) as the associated point of the bound abc three points, and moving the TF point can control the movement of the abc three points, wherein the TF is the second TF in the TF-of-TF method;

(43) The user immediately responds to the mouse message by using the mouse-down event function and adds an activity variable to determine whether the mouse clicks a control point; the internal relation of TF nodes in the TF-let is kept unchanged and the TF-let moves as a whole, and the up-and-down dragging control point can change the value of the whole point, so that the transparency of the organization corresponding to the TF-let is changed.

Images