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 particularly provides a medical data visualization method based on a histogram and a nonlinear embedded transfer function.

Background

Transfer function design is a traditional method of volume visualization, assigning different color and transparency schemes to each voxel in the volume data. In recent years, volume visualization has been widely used in many scientific and engineering fields such as oil/gas exploration, atmospheric and marine simulation, medical diagnostics, etc., to aid in understanding complex observed or simulated volume data. With the development of graphics hardware and volume visualization algorithms, volume rendering has become faster and more accurate. Therefore, the emphasis of volume rendering has been transferred to the design of transfer functions.

In recent years, in order to improve the efficiency and accuracy of volume visualization, there are two general transfer function design methods, namely, a data-centered method and an image-centered method. However, each of these two conventional methods can only load one TF or TF-let at each explored time, and cannot switch between different visualizations. In addition, when the transparency and definition of the object are changed, all nodes need to be moved, the operation is complex, and more perfect transfer function design is urgently needed.

In general, current transfer function designs have many limitations, such as: the efficiency is low, the user is tedious in exploration, only one TF can be loaded at a time, and the user cannot switch between different visual results, so that the operation is complex when the tissue transparency and definition are changed. The above limitations are mainly due to the complexity and diversity of human tissue, and the diversity of needs of users to observe and analyze tissue.

Disclosure of Invention

The invention aims to: the invention provides a medical data visualization method based on a histogram and a nonlinear embedded transfer function, which is used for editing one or more tissues in a cooperative manner according to the domain knowledge of different experts, so that the efficiency and the accuracy are improved.

The technical scheme is as follows: the invention aims at a medical data visualization method based on a histogram and a nonlinear embedded transfer function, which specifically comprises the following steps:

(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.

Further, the implementation process of the step (1) is 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.

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; 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 _i 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.

Further, the implementation process of the step (3) is 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.

Further, the implementation process of the step (4) is 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.

The beneficial effects are that: compared with the prior art, the invention has the beneficial effects that:

the invention can rapidly load the TF-Let, and the user can obtain the serialized TF-Let rendering result as long as the user stores the corresponding TF-Let before; by clicking on the TF-Let node, the user can effectively view any organization in the dataset, and when loading a new dataset, the user needs to explore the data and find a single TF-Let for all organizations;

the invention realizes the result editing in the TF-of-TF vertical direction: the user can make a combination adjustment by moving a single node up and down; when careful and clear observation of a portion thereof is required, the user modifies the transparency of the blood vessel; at the moment, the transparency can be changed only by clicking and moving up and down through a mouse, so that the method is more time-saving and efficient than the traditional method; in addition, the user can stretch a plurality of tissues in proportion through the transfer function so as to obtain a better observation effect;

the 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 points left and right, such as skin, blood vessels, bones, etc. of the hand dataset; the required part can be highlighted by clicking the mouse and adjusting the up-and-down movement of the skin point, so that the observation is convenient; in addition, the user can quickly switch different medical tissues by dragging the TF-let left and right, so that the operation is convenient;

according to the invention, 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; the user can also load the updated file to obtain a specific fusion visual effect so that the user can better know the corresponding tissue part; the implementation of multi-user operation enables multiple users to fully exploit their diverse domain knowledge background.

Drawings

FIG. 1 is a flow chart of the present invention;

FIG. 2 is a schematic diagram of a histogram-based nonlinear transfer function editor in accordance with the present invention;

FIG. 3 is a schematic diagram illustrating TF-of-TF in the present invention;

FIG. 4 is a schematic diagram of a method of multiuser fusion according to the invention;

FIG. 5 is a flow chart of the invention for stereoscopic rendering using a function editor;

FIG. 6 is a schematic diagram of individual TF-lets and rendering results for different locations in a dataset CHEST; wherein (a) is a single TF-let schematic of the chest; (b) rendering result schematic diagram of TF-let to chest; (c) a single TF-let schematic of the sternum; (d) rendering result of the TF-let to the sternum; (e) is a single TF-let schematic of the hand bones; (f) is a rendering result schematic diagram of the TF-let on the hand bone; (g) is a single TF-let schematic representation of the skull; (h) rendering result of the TF-let on the skull is shown schematically;

FIG. 7 is an evaluation graph of a data set HEAD and a data set CHEST in the present invention, wherein (a) is a schematic diagram of rendering results of the data set HEAD using a conventional linear transfer function method; (b) A result schematic for rendering the dataset HEAD using the proposed method; (c) Rendering result schematic using a linear method for the dataset CHEST; (d) A result diagram for rendering the data set CHEST by using the method provided by the invention;

FIG. 8 is a case of evaluation of the dataset HAND of the invention; wherein (a) (b) is a traditional linear transfer function edit result schematic presented by two users for the dataset HAND; (c) Searching a schematic diagram of a blood vessel for a user by using the method provided by the invention;

FIG. 9 is a schematic diagram of changing the transparency and clarity of skin by mouse clicking on a moving skin point; wherein (a) is an original drawing; (b) A schematic diagram for reducing the transparency of the skin by moving the first skin point upwards through a mouse for the user; (c) A schematic diagram for reducing the transparency of the skin by moving the first skin point upwards through a mouse for the user; (d) A schematic diagram for reducing the transparency of the skin by moving the second skin point upwards through a mouse for the user; (e) A schematic diagram for improving the definition of the skin by moving the second skin point rightward through a mouse for the user; (f) A schematic diagram for improving the definition of the skin by moving a third skin point leftwards by a mouse for the user; (g) A schematic diagram for increasing the transparency of the skin by moving the third skin point downwards through a mouse for the user;

FIG. 10 is a schematic view of a blood vessel in a dataset HAND of the invention by a user adjusting nodes in TF-of-TF;

FIG. 11 is a graphical representation of the skin, vessel and bone multi-user fusion results based on the dataset HAND of the invention;

FIG. 12 is a schematic diagram of fusion employing the present invention; wherein (a) is a schematic representation of the fusion results of skin and bone of dataset HEAD; (b) The results of fusion of the CHEST (along with the lungs) and sternum of the dataset CHEST are shown schematically.

Detailed Description

The invention is described in further detail below with reference to the accompanying drawings.

The invention provides a medical data visualization method based on a histogram and a nonlinear embedded transfer function, as shown in fig. 1, firstly, raw data is preprocessed into a volume; the user then adjusts the nonlinear transfer function using the visual cues provided by the nonlinear histogram; after adjustment all TF-lets are serialized into disk, they will be de-serialized for quick reload. The final TF-lets can be fused in a focus-and-context fashion to a final nonlinear transfer function. The method specifically comprises the following steps:

step 1: preprocessing the medical human tissue data acquired in advance to obtain three-dimensional body data.

Pre-processing medical body tissue data of a patient into volume data, including different attributes (tissue) blood, fat, soft tissue and bone in the medical data; arranging all slices according to a real physical space, thereby obtaining three-dimensional volume data; the system loads the processed three-dimensional data into the system, and provides data support for subsequent medical data visualization and exploration.

Step 2: to edit transfer functions more efficiently to users, a design of non-linear histograms and non-uniform grids is used in constructing a histogram non-linear in-line transfer function background. The larger the histogram bin size, the wider the bin width drawn in the background.

As shown in fig. 2, the bin width of the histogram is calculated for the corresponding bin size. The TF-let in the figure can represent an attribute (e.g., an organization) of the volume data. The leftmost TF-let represents 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 as follows: the histogram is rendered into a grid to reduce visual clutter. The bin size of the data is calculated when the data is preprocessed for drawing the histogram. The group distance calculation formula is:

wherein b _i The size of each bin is expressed and calculated using the following formula:

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

step 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 sharpness of each point between control points is derived from the difference of the two nearest control points.

When the user gets the best rendering result through the TF-let, they can sequence all color schemes into the disk for subsequent loading by means of the fast loading and reloading functions. The interface of the quick load 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 appears, the vertical coordinates (with absolute values) 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 and all TF-lets will be 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.

Step 4: a wavelet-like short transfer function (TF-let) is designed that can be quickly serialized and reloaded in subsequent exploration. In addition, a TF-let fusion method is designed to fuse a plurality of TF-lets by simply clicking on the corresponding TF-let node. This approach to fusing multiple TF-lets, known as the transfer function based transfer function approach (TF-of-TF), provides a visual cue for the user to explore.

The user can adjust the visual result through the nonlinear TF editor, and the visual clues for exploration can be provided for the user by using a histogram nonlinear embedded transfer function, namely a transfer function method based on the transfer function. 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 fig. 3, first, the transparency of the tissue can be adjusted by dragging TF-of-TF nodes up and down. The sharpness of the tissue can then be adjusted by dragging the TF-of-TF node left and right. Thus, the user is able to obtain the final visualization result without changing all control points of the original TF-let.

The points in the same tissue are bound with each other, for example, abc three points are arranged in an editor of the same tissue region, the three points are bound by clicking, and dragging the highest point in the three points can enable the bound three points to move left and right at the same time by the design, and the relative positions of the three points on the tissue are not changed, so that the efficiency of changing the image definition and transparency is improved. The three points are bound to the first TF in the TF-of-TF method.

Taking the b point with the highest y-axis value from the three abc points bound together, and assuming that the coordinates of b are (x, y), correspondingly setting a second TF point (x, -y) as the associated point of the three abc points bound, wherein the movement of the three abc points can be controlled by moving the TF point, and the TF is the second TF in the TF-of-TF method.

The user can immediately respond to the mouse message using the mouse down event function and add an activity variable to determine if the mouse clicks on the control point. It keeps the internal relationship of the TF nodes in the TF let unchanged and moves as a whole, and the up-and-down dragging control point can also change the value of the whole point, thereby changing the transparency of the organization corresponding to the TF let.

Step 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. As shown in FIG. 4, a plurality of users edit TF-of-TF nodes corresponding to the organizations of interest to them; the final rendering effect can be fused by fusing the nodes of TF-of-TF.

Such as the transparency and clarity of blood, bones, skin in arm tissue that a plurality of doctor users want to edit separately, this function can be achieved by current software. In the TF-let fusion phase, the user is able to obtain any combination of multiple TF-let rendering results. Multiple focal organizations and their context can be viewed simultaneously using focal context technology. In addition, they can delete any organization by double clicking on the TF-let node.

Focus and context 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 a user wants to explore adjacent soft tissues (contexts) while looking at a blood vessel (focus attribute), the corresponding TF let node can be saved and added to the final fused TF. In the multiuser fusion process, for example, the user #01 saves the TF let corresponding to the skin in a specific folder, the user #02 saves the TF let corresponding to the blood in a specific folder, and the user #03 saves other TF lets corresponding to the bone in the same folder. Three pre-saved TF-lets can be quickly loaded and fused. When the user does not wish to see the corresponding attribute in the final rendering result, the user can delete any of the plurality of TF let nodes from the final fused TF.

Fig. 6 (a) through (h) are schematic views of individual TF-lets and final rendering results for each organization in the dataset of the present invention. If the user stores the corresponding TF-lets before, the user can obtain the rendering result rendered by the serialized TF-lets. Taking medical data as an example. TF-lets for all organizations can be serialized and saved to disk separately. The user can effectively view any organization in the data set by clicking on the TF-let node. However, when a user loads a new data set, it is necessary for the user to explore the data and find a single TF-let for all organizations. A single TF-let of the CHEST in the data set CHEST is shown in fig. 6 (a). By clicking on the corresponding TF-let node, the rendering result as shown in fig. 6 (b) is loaded. Three individual TF-lets in fig. 6 (c), 6 (e) and 6 (g) are the sternum, hand bones and skull, respectively, and their corresponding rendering results in fig. 6 (d), 6 (f) and 6 (h), respectively.

Fig. 7 (a) - (d) show evaluation cases of the data set HEAD and the data set CHEST according to the present invention. Some interesting results are easily obtained by the proposed method, while similar results are difficult to obtain by the user by the linear method. The corresponding results presented by the conventional linear transfer function method and the proposed method of the present invention are shown in fig. 7 (a) - (b), respectively. It is difficult to find clusters in the data set CHEST that may be some lesions of the CHEST surface because of the small range of properties of these tissues, as shown in fig. 7 (c) - (d). The rendering results of the data set HEAD using the conventional linear transfer function in fig. 7 (a), and the rendering results of the data set HEAD using the method proposed by the present invention in fig. 7 (b), the outline of various tissues in the red circle are much clearer. Fig. 7 (c) shows the result of rendering the data set CHEST using the linear method, fig. 7 (d) shows the result of rendering the data set CHEST using the method according to the present invention, and the user can find some clusters that may be lesions in the red circle on the CHEST surface.

Fig. 8 (a) - (c) show evaluation cases of the data set HAND according to the present invention. Fig. 8 (a) - (b) show the results of editing the data set HAND presented by two users with a conventional linear transfer function. They have attempted to acquire blood vessels in a trial-and-error manner, but they have found that it is difficult to obtain optimal results, and it is time consuming to obtain both results. Fig. 8 (c) shows a method of searching for a blood vessel by a user according to the present invention. The user can easily obtain very clear results compared with the results extracted by the conventional method. In addition, through fig. 8 (a) - (c) (upper panel), it was also found that the contexts on the blood vessels are clearer (i.e., outline and structure of bones).

For an average user, if each towel is to be adjusted to a condition that is easily visible, the adjustment is required from outside to inside. A method is provided for a user to change the transparency and clarity of skin by moving a skin point by mouse click. Fig. 9 (a) - (b) show that the user moves the first skin point upward by the mouse to reduce the transparency of the skin. Fig. 9 (b) - (c) show that the user moves the first skin point upward by the mouse to reduce the transparency of the skin. Fig. 9 (c) - (d) show that the user moves the second skin point upward by the mouse to reduce the transparency of the skin. Fig. 9 (d) - (e) show that the user moves the second skin point to the right by the mouse to improve the clarity of the skin. Fig. 9 (e) - (f) show that the user moves the third skin point leftwards by the mouse to improve the clarity of the skin. Fig. 9 (f) - (g) show that the user moves the third skin point downward by the mouse to increase the transparency of the skin. Changes in skin transparency and clarity are shown in fig. 9 (a) - (g) (upper diagram), and in fig. 9 (a) - (g) (lower diagram), the user moves skin points to change the transparency and clarity of the skin.

In addition to serving common users, the invention also meets the needs of the expert to view part of the organization. If an expert wants to see a certain blood vessel, he needs to adjust the transparency and definition of the skin, and can first adjust the definition of the skin by moving the mouse left and right, as shown in fig. 10 (lower left and lower middle). The transparency of the skin can then be adjusted by moving the mouse up and down, as shown in fig. 10 (lower middle and lower right). As shown in fig. 10 (upper diagram), the blood vessel to be observed is highlighted for easy observation. Assuming that the user wants to view the vessels in the dataset HAND, only the nodes in TF-of-TF (bottom of TF editor) need to be adjusted.

FIG. 11 is a graph showing the fusion of multiple single results compiled by different users from the data set HAND of the invention. Three single results in fig. 11 (left side) relate to skin, blood vessels and hand bone tissue. Fig. 11 (upper right) shows the fusion of these three single results. Furthermore, if the user wants to explore the bone with blood vessels, but not the skin, they can remove the editing of the skin properties by simply clicking on the corresponding TF-of-TF junction in the transfer function, as in fig. 11 (bottom right).

As shown in fig. 12 (a), the final fusion result of the skin (upper left panel) and bone (lower left panel) of the dataset HAND is shown in fig. 12 (a) (upper right panel). As shown in fig. 12 (b), the final fusion result of the CHEST (upper left panel) and sternum (lower left panel) of the data set CHEST is shown in fig. 12 (b) (upper right panel). In fact, the user can obtain any fusion combination by effectively designating a plurality of focus organizations and a plurality of contexts, and the user can only click corresponding TF-let-TF nodes, namely, in FIG. 12 (a), the user can only click two TF-let nodes of skin and bones to realize a final fusion result; the user in fig. 12 (b) also only needs to click on the sternum and two TF-let nodes of 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