Medical data visualization method based on histogram and nonlinear inline transfer function

Abstract

The application discloses a medical data visualization method based on a histogram and a nonlinear embedded transfer function, and comprises the following steps: (1) preprocessing patient medical human tissue data into volume data; (2) a transfer function editing point TF-let is related to tissue volume data, and a TF of a transfer function TF editor provides a TFlet corresponding to an original nonlinear transfer function for each node of the TF, so that the TF is designed; (3) several nodes of the TF-let are synchronously moved up and down to adjust the transparency of each tissue picture; (4) several nodes of the TF-let are synchronously moved left and right to adjust the definition of each tissue picture; (5) multiple users with different domain knowledge can collaboratively browse single medical data and edit sub-regions thereof, so that the efficiency and accuracy are improved. The application can quickly load the TF-let, realize result editing in the vertical direction and the horizontal direction of the TF-of-TF, and according to the domain knowledge of different experts, collaboratively edit one or more tissues, so that the efficiency and accuracy are improved.

Description

Technical Field

[0001] This invention belongs to the field of medical data interpretation and visualization technology, and specifically proposes a medical data visualization method based on histograms and nonlinear embedded transfer functions. Background Technology

[0002] Transfer function design is a traditional method for volume visualization, assigning different color and transparency schemes to each voxel in the volume data. In recent years, volume visualization has been widely applied in various scientific and engineering fields, including oil / gas exploration, atmospheric and ocean simulation, and medical diagnostics, helping to understand 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 focus of volume rendering has shifted to the design of transfer functions.

[0003] In recent years, to improve the efficiency and accuracy of volume visualization, two common design approaches for transfer functions have been adopted: data-centric and image-centric methods. However, these traditional methods can only load one transfer function (TF) or TF-let per exploration time, and cannot switch between different visualization results. Furthermore, changing the transparency and clarity of an object requires moving all nodes, which is complex and necessitates a more sophisticated transfer function design.

[0004] Overall, current transfer function designs have many limitations, such as low efficiency, monotonous user exploration, the ability to load only one transfer function at a time, the inability to switch between different visualizations, and complex operations when changing tissue transparency and clarity. These limitations are mainly due to the complexity and diversity of human tissues, as well as the diverse needs of users in observing and analyzing tissues. Summary of the Invention

[0005] Purpose of the invention: This invention proposes a medical data visualization method based on histograms and nonlinear embedded transfer functions, which collaboratively edits one or more tissues according to the domain knowledge of different experts, thereby improving efficiency and accuracy.

[0006] Technical Solution: This invention aims to provide a medical data visualization method based on histograms and nonlinear embedded transfer functions, specifically including the following steps:

[0007] (1) Preprocess the pre-acquired medical human tissue data to obtain three-dimensional volume data;

[0008] (2) To enable users to edit transfer functions more effectively, a nonlinear histogram and a non-uniform grid design are used in the context of constructing a histogram-based nonlinear embedded transfer function.

[0009] (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 transparency of each tissue image; the color and transparency of each point between control points are obtained by the difference between the two nearest control points; move the nodes of the TF-let left and right synchronously to adjust the clarity of each tissue image; the color and clarity of each point between control points are obtained by the difference between the two nearest control points;

[0010] (4) Design a transfer function-based transfer function method TF-of-TF to provide users with visual cues for exploration. TF-of-TF is to simply click on the corresponding TF-let node to merge multiple TF-lets.

[0011] (5) Multiple users with different domain knowledge can collaborate to browse a single medical data and edit its sub-regions, improving the efficiency and accuracy of medical data exploration.

[0012] Furthermore, the implementation process of step (1) is as follows:

[0013] The medical human tissue data is sliced ​​and organized, with a reflection time interval of 1 millisecond between each slice; the data slices are aligned with the actual physical space according to the reflection time, and the aligned data is the three-dimensional volume data; the three-dimensional volume data is then transferred from the CPU to the GPU.

[0014] Furthermore, the implementation process of step (2) is as follows:

[0015] The vertical axis of the histogram represents opacity, and the horizontal axis uses a non-linear mapping design; the histogram is rendered as a grid to reduce visual clutter; the bin size of the data is calculated during data preprocessing for histogram plotting; the group interval calculation formula is:

[0016]

[0017] Among them, b i This represents the size of each bin, where i represents the bin number; the width of the vertical axis is increased at low scale values ​​and calculated using the following formula:

[0018]

[0019] In a non-linear TF editor, the vertical axis represents opacity, or α value.

[0020] At low scale values, the width of the vertical axis is increased, and the transformation of the control points and nonlinear coordinates is as follows:

[0021]

[0022] Here, α represents the amplification level; the larger α is, the wider the low-value portion of the nonlinear tf is.

[0023] Furthermore, the implementation process of step (3) is as follows:

[0024] When a user obtains the best rendering results through TF-let, all color schemes are serialized to disk using the fast loading and reloading features for subsequent loading;

[0025] When a user decides to serialize and load TF control point data, a TF let node appears, and the vertical coordinate of each node represents the maximum opacity of the corresponding TF let control point.

[0026] TF-lets are associated with tissue volume data. All TF-lets are serialized for reloading and subsequent analysis. TF editors provide a TF-let for transfer function design, where each node of the TF corresponds to a TF-let of the original nonlinear transfer function. When editing TF, volume data are classified according to their distribution in the feature space.

[0027] Furthermore, the implementation process of step (4) is as follows:

[0028] (41) Bind points in the same organization to each other. There are three points abc in the editor of the same organization area. Bind these three points by clicking. Dragging the highest point of these three points will make the three bound points move left and right at the same time without changing the relative position of the three points in the organization, thus improving the efficiency of changing the image clarity and transparency. The three points bound together form the first TF in the TF-of-TF method.

[0029] (42) Take the point b with the highest y-axis value among the three points abc bound together in step (41). The coordinates of b are (x, y). Set the second TF point (x, -y) as the associated point of the three bound points 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.

[0030] (43) The user immediately responds to the mouse message using the mouse press event function and adds an activity variable to determine whether the mouse clicked the control point; the internal relationship of the TF node in the TF-let remains unchanged and moves as a whole. Dragging the control point up and down can also change the value of the entire point, thereby changing the transparency of the organization corresponding to the TF let.

[0031] Beneficial effects: Compared with the prior art, the beneficial effects of the present invention are as follows:

[0032] This invention enables fast loading of TF-lets. Users only need to save the corresponding TF-let previously to obtain the serialized TF-let rendering result. By clicking on the TF-let node, users can effectively view any organization in the dataset. When loading a new dataset, users need to explore the data and find the individual TF-lets of all organizations.

[0033] This invention enables vertical editing of TF-of-TF results: users can adjust combinations by moving individual nodes up and down; when a part needs to be observed carefully and clearly, the transparency of blood vessels needs to be modified; at this time, the transparency can be changed simply by clicking and moving the mouse up and down, which is more time-saving and efficient than traditional methods; in addition, users can also stretch multiple tissues proportionally through transfer functions to obtain better observation results.

[0034] This invention also enables horizontal editing of TF-of-TF results: users can control the resolution of the displayed image by moving TF-of-TF points left and right, such as skin, blood vessels, and bones in a hand dataset; clicking the mouse and adjusting the vertical movement of the skin points can highlight the desired areas for easy observation; in addition, users can quickly switch between different medical tissues by dragging TF-lets left and right, making the operation convenient.

[0035] This invention enables collaborative editing of one or more tissues based on the domain knowledge of different experts, thereby improving efficiency and accuracy. Users can also load updated files to obtain specific fusion visual effects, allowing them to better understand the corresponding tissue parts. The implementation of multi-user operation allows multiple users to fully utilize their different domain knowledge backgrounds. Attached Figure Description

[0036] Figure 1 This is a flowchart of the present invention;

[0037] Figure 2 This is a schematic diagram of the histogram-based nonlinear transfer function editor in this invention;

[0038] Figure 3 This is a schematic diagram illustrating TF-of-TF in this invention;

[0039] Figure 4 This is a schematic diagram of the multi-user fusion method in this invention;

[0040] Figure 5 This is a flowchart illustrating the process of using a function editor for 3D rendering in this invention.

[0041] Figure 6The images show individual TF-lets and their rendering results for different body parts in the CHEST dataset. Specifically, (a) shows a single TF-let for the chest; (b) shows the rendering result of the TF-let on the chest; (c) shows a single TF-let for the sternum; (d) shows the rendering result of the TF-let on the sternum; (e) shows a single TF-let for the hand bones; (f) shows the rendering result of the TF-let on the hand bones; (g) shows a single TF-let for the skull; and (h) shows the rendering result of the TF-let on the skull.

[0042] Figure 7 The following are evaluation graphs of the datasets HEAD and CHEST in this invention: (a) is a schematic diagram of the rendering result of the dataset HEAD using the traditional linear transfer function method; (b) is a schematic diagram of the rendering result of the dataset HEAD using the proposed method; (c) is a schematic diagram of the rendering result of the dataset CHEST using the linear method; and (d) is a schematic diagram of the rendering result of the dataset CHEST using the method proposed in this invention.

[0043] Figure 8 Evaluation case of dataset HAND in this invention; wherein (a) and (b) are schematic diagrams of the traditional linear transfer function editing results of dataset HAND displayed by two users; and (c) is a schematic diagram of a user using the method proposed in this invention to find blood vessels;

[0044] Figure 9 These are schematic diagrams illustrating how to change the transparency and clarity of skin by moving skin points with a mouse; where (a) is the original image; (b) is a schematic diagram showing how the user of this invention reduces skin transparency by moving the first skin point upwards with the mouse; (c) is a schematic diagram showing how the user of this invention reduces skin transparency by moving the first skin point upwards with the mouse; (d) is a schematic diagram showing how the user of this invention reduces skin transparency by moving the second skin point upwards with the mouse; (e) is a schematic diagram showing how the user of this invention increases skin clarity by moving the second skin point to the right with the mouse; (f) is a schematic diagram showing how the user of this invention increases skin clarity by moving the third skin point to the left with the mouse; and (g) is a schematic diagram showing how the user of this invention increases skin transparency by moving the third skin point downwards with the mouse.

[0045] Figure 10 This invention allows users to view a schematic diagram of blood vessels in the dataset HAND by adjusting the nodes in TF-of-TF;

[0046] Figure 11 This is a schematic diagram of the multi-user fusion results of skin, blood vessels, and bones based on the dataset HAND of this invention;

[0047] Figure 12 The diagram illustrates the fusion process using the present invention; (a) is a schematic diagram of the fusion result of the skin and bones in the HEAD dataset; and (b) is a schematic diagram of the fusion result of the chest (including the lungs) and sternum in the CHEST dataset. Detailed Implementation

[0048] The present invention will now be described in further detail with reference to the accompanying drawings.

[0049] This invention provides a medical data visualization method based on histograms and nonlinear embedded transfer functions, such as... Figure 1 As shown, first, the raw data is preprocessed into a volume; then, the user uses visual cues provided by a non-linear histogram to adjust the non-linear transfer function; after adjustment, all TF-lets are serialized to disk, and they are deserialized for rapid reloading. Finally, the last few TF-lets are merged into the final non-linear transfer function using a focus-and-context approach. Specifically, the steps include:

[0050] Step 1: Preprocess the pre-acquired medical human tissue data to obtain three-dimensional volume data.

[0051] The patient's medical human tissue data is transformed into volumetric data preprocessing, including different attributes (tissues) in the medical data such as blood, fat, soft tissue, and bone; all slices are arranged according to the real physical space to obtain three-dimensional volumetric data; the system loads the processed three-dimensional volumetric data into the system to provide data support for subsequent medical data visualization and exploration.

[0052] Step 2: To allow users to edit transfer functions more effectively, a nonlinear histogram and a non-uniform grid design are used in the background of the histogram-embedded transfer function. The larger the histogram container size, the wider the container width is drawn in the background.

[0053] like Figure 2 As shown, the bin width of the histogram is calculated based on the corresponding bin size. The TF-lets in the diagram can represent an attribute of the volume data (e.g., an tissue). 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 merging.

[0054] The vertical axis of the histogram represents opacity, and the horizontal axis uses a non-linear mapping design; specifically, the histogram is rendered as a grid to reduce visual clutter. The bin size of the data is calculated during data preprocessing and used to plot the histogram. The formula for calculating the group interval is:

[0055]

[0056] Where b i The size of each bin is represented by the following formula:

[0057]

[0058] At low scale values, the width of the vertical axis is increased, and the transformation of the control points and nonlinear coordinates is as follows:

[0059]

[0060] 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 transparency of each tissue image; the color and transparency of each point between control points are obtained by the difference between the two nearest control points; move the nodes of the TF-let left and right synchronously to adjust the sharpness of each tissue image; the color and sharpness of each point between control points are obtained by the difference between the two nearest control points.

[0061] When users achieve optimal rendering results using TF-let, they can serialize all color schemes to disk using the fast loading and reloading features for subsequent loading. The fast loading feature's interface is designed as a two-dimensional area.

[0062] When a user decides to serialize and load TF control point data, a TF let node appears, and the vertical coordinate (with absolute value) of each node represents the maximum opacity of the corresponding TF let control point.

[0063] TF-lets are associated with tissue volume data, and all TF-lets are serialized for reloading and subsequent analysis. Using the TF editor, TF provides a TF let for each node of the original nonlinear transfer function; during TF editing, volumetric data are classified according to their distribution in the feature space.

[0064] Step 4: Design a short transfer function (TF-let) similar to a wavelet for rapid serialization and reloading in subsequent explorations. Furthermore, a TF-let fusion method is designed to merge multiple TF-lets by simply clicking the corresponding TF-let node. This method of fusion, called the transfer function-of-TF (TF-of-TF) method, provides users with visual cues for exploration.

[0065] Users can adjust the visualization results through the non-linear TF editor, using histogram-based non-linear embedded transfer functions—that is, a transfer function-based approach—to provide visual cues for exploration. In the TF editor, an attribute corresponds to a wavelet-like transfer function, referred to as a TF let in this invention. Figure 3 As shown, firstly, the transparency of the organization can be adjusted by dragging the TF-of-TF nodes up and down. Then, the clarity of the organization can be adjusted by dragging the TF-of-TF nodes left and right. Therefore, users can obtain the final visualization result without changing any of the control points of the original TF-let.

[0066] This method binds points within the same organization. For example, if there are three points (a, b, c) in the editor's same organization area, clicking on these three points binds them together. Dragging the highest point of these three points moves all three bound points horizontally without changing their relative positions within the organization, thus improving the efficiency of adjusting image sharpness and transparency. These three points form the first TF (Transformer-of-TF) in this method.

[0067] Take the point b with the highest y-axis value among the three bound points a, b, and c. Assuming the coordinates of b are (x, y), set the second TF point (x, -y) as the associated point of the three bound points a, b, and c. Moving this TF point can control the movement of the three points a, b, and c. This TF is the second TF in the TF-of-TF method.

[0068] Users can immediately respond to mouse messages using the mouse press event function and increment the activity variable to determine whether the mouse clicked a control point. It keeps the internal relationships of TF nodes within a TF let unchanged and moves them as a whole; dragging a control point up or down also changes the value of the entire point, thereby altering the transparency of the organization corresponding to the TF let.

[0069] Step 5: Multiple users with different domain knowledge can collaborate to browse individual medical data points and edit their sub-regions, improving the efficiency and accuracy of medical data exploration. For example... Figure 4 As shown, multiple users can edit the TF-of-TF nodes corresponding to the organizations they are interested in; the final rendering effect can be achieved by merging the TF-of-TF nodes.

[0070] For example, if multiple doctors want to edit the transparency and clarity of blood, bones, and skin in arm tissue separately, the current software can achieve this. During the TF-let fusion stage, users can obtain any combination of multiple TF-let rendering results. They can use focus context technology to simultaneously view multiple focus tissues and their context. Furthermore, they can delete any tissue by double-clicking a TF-let node.

[0071] Focus and context visualization techniques explore volumetric data through TF-lets fusion. To improve detection efficiency, a multi-user fusion function was designed. Taking medical data as an example, if a user wants to explore adjacent soft tissue (context) while viewing blood vessels (focus attribute), they can save the corresponding TF-lets and add them to the final fused TF. In the multi-user fusion process, for example, user #01 saves the TF-lets corresponding to skin to a specific folder, user #02 saves the TF-lets corresponding to blood to a specific folder, and user #03 saves other TF-lets corresponding to bones to the same folder. These three pre-saved TF-lets can be quickly loaded and fused. When a user does not want to see a corresponding attribute in the final rendering result, they can delete any one of the multiple TF-lets from the final fused TF.

[0072] Figure 6 Figures (a) to (h) illustrate individual TF-lets of various tissues in the dataset of this invention and the final rendering results. If the user has previously saved the corresponding TF-lets, they can obtain the rendering results from the serialized TF-lets. Taking medical data as an example, the TF-lets of all tissues can be serialized and saved individually to disk. Users can effectively view any tissue in the dataset by clicking on a TF-let node. However, when a user loads a new dataset, they need to explore the data and find the individual TF-lets of all tissues. Figure 6 Image (a) shows a single TF-let for the chest in the CHEST dataset. Clicking the corresponding TF-let node loads the following content: Figure 6 (b) shows the rendering result. Figure 6 (c) Figure 6 (e) and Figure 6 The middle (g) represents three individual TF-lets from the sternum, hand bones, and skull. Figure 6 (d) Figure 6 (f) and Figure 6 The values ​​in the middle (h) represent the corresponding rendering results.

[0073] Figure 7In Figures (a)-(d), we see evaluation examples of the HEAD and CHEST datasets used in this invention. The proposed method easily yields some interesting results, while users often struggle to obtain similar results using linear methods. Figure 7 Tables (a)-(b) show the results presented by the traditional linear transfer function method and the method proposed in this invention, respectively. It is difficult to find masses in the CHEST dataset that may be lesions on the surface of the chest because the properties of these tissues are very limited, such as... Figure 7 As shown in (c)-(d). Figure 7 The rendering result of dataset HEAD in (a) using a traditional linear transfer function. Figure 7 In the middle (b), the rendering result of the dataset HEAD using the method proposed in this invention is shown. The outlines of various tissues in the red circle are much clearer. Figure 7 (c) shows the rendering result of the CHEST dataset using a linear method. Figure 7 In the middle (d), the rendering result of the CHEST dataset using the method proposed in this invention is shown. Users can find some potentially lesion-like lumps in the red circles on the surface of the chest.

[0074] Figure 8 (a)-(c) are evaluation cases of the dataset HAND in this invention. Figure 8 In the middle (a)-(b), two users are shown the results of editing the dataset HAND using a traditional linear transfer function. They attempted to obtain blood vessels through trial and error, but found it difficult to achieve optimal results, and obtaining these two results was time-consuming. Figure 8 (c) shows a user using the method proposed in this invention to locate blood vessels. Compared to the results obtained using traditional methods, the user can easily obtain very clear results. Furthermore, through... Figure 8 (a)-(c) (above), it was also found that the contexts on the blood vessels were clearer (i.e. the outline and structure of the bones).

[0075] For the average user, adjusting each tissue to a easily visible state requires working from the outside in. This solution provides a method where users can change the transparency and clarity of the skin by clicking and moving skin points with the mouse. Figure 9 In (a)-(b), the user moves the first skin point upwards with the mouse to reduce the skin's opacity. Figure 9 In (b)-(c), the user moves the first skin point upwards with the mouse to reduce the skin's opacity. Figure 9 In (c)-(d), the user moves the second skin point upwards with the mouse to reduce the skin's opacity. Figure 9 In the middle (d)-(e), the user moves the second skin point to the right using the mouse to improve the skin's clarity. Figure 9In the middle (e)-(f), the user of this invention moves the third skin point to the left using the mouse to improve the clarity of the skin. Figure 9 In the middle (f)-(g), the user of this invention increases the transparency of the skin by moving the third skin point downwards with the mouse. Figure 9 The images (a)-(g) (above) show the changes in skin transparency and clarity. Figure 9 (a)-(g) (below) show how users move skin points to change the skin's transparency and clarity.

[0076] In addition to serving ordinary users, this invention also meets the needs of experts to view specific tissues. If an expert wants to see a particular blood vessel, they need to adjust the skin's transparency and clarity. They can first adjust the skin's clarity by moving the mouse left and right, such as... Figure 10 (As shown in the bottom left and bottom middle images). Then you can adjust the skin's transparency by moving the mouse up and down, as... Figure 10 (As shown in the lower middle and lower right images). Figure 10 As shown in the image above, the blood vessels that need to be observed are highlighted for easy viewing. If a user wants to view the blood vessels in the HAND dataset, they only need to adjust the nodes in TF-of-TF (at the bottom of the TF editor).

[0077] Figure 11 The dataset HAND of this invention is a fusion result of multiple single results edited by different users. Figure 11 The three single results (on the left) involve tissues of the skin, blood vessels, and hand bones. Figure 11 (Top right) shows the fused result of these three individual results. Furthermore, if users want to explore the skeleton with blood vessels but not the skin, they can remove skin attribute editing by simply clicking the corresponding TF-of-TF node in the transfer function, such as... Figure 11 (Bottom right)

[0078] like Figure 12 As shown in (a), the final fusion result of the skin (top left) and bones (bottom left) of the dataset HAND is as follows. Figure 12 As shown in (a) (top right figure). Figure 12 As shown in (b), the final fusion result of the chest (top left) and sternum (bottom left) of the CHEST dataset is as follows. Figure 12 (b) (top right figure) shows that users can actually achieve any blending combination by effectively specifying multiple focus groups and multiple contexts; they only need to click the corresponding TF-let-TF node. Figure 12 In (a), the user only needs to click on the two TF-let nodes of the skin and bone to achieve the final fusion result; Figure 12In (b) mode, users only need to click on the two TF-let nodes of the sternum and the sternum to obtain the final fusion result.

Claims

1. A medical data visualization method based on histograms and nonlinear embedded transfer functions, characterized in that, Includes the following steps: (1) Preprocess the pre-acquired medical human tissue data to obtain three-dimensional volume data; (2) To enable users to edit transfer functions more effectively, a nonlinear histogram and a non-uniform grid design are used in the context of constructing a histogram-embedded nonlinear transfer function; (3) In the TF editor, an attribute corresponds to a transfer function similar to a triangular wavelet, which extracts the TF-let of tissue in medical data; The TF-let nodes are moved vertically to adjust the transparency of each tissue image; the color and transparency of each control point are obtained by the difference between the two nearest control points. The TF-let nodes are moved horizontally to adjust the sharpness of each tissue image; the color and sharpness of each control point are obtained by the difference between the two nearest control points. (4) Design a transfer function-based transfer function method TF-of-TF to provide users with visual cues for exploration. The TF-of-TF method is to simply click on the corresponding TF-let node to merge multiple TF-lets. (5) Multiple users with different domain knowledge can collaborate to browse a single medical data and edit its sub-regions, improving the efficiency and accuracy of medical data exploration; The implementation process of step (4) is as follows: (41) Bind points in the same organization to each other. There are three points abc in the editor of the same organization area. Bind these three points by clicking. Dragging the highest point of these three points will make the three bound points move left and right at the same time without changing the relative position of the three points in the organization, thus improving the efficiency of changing the image clarity and transparency. The three points bound together form the first TF in the TF-of-TF method. (42) Take the point b with the highest y-axis value among the three points abc bound together in step (41). The coordinates of b are (x, y). Set the second TF point (x, -y) as the associated point of the three bound points 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. (43) The user immediately responds to the mouse message using the mouse press event function and adds an activity variable to determine whether the mouse clicked the control point; the internal relationship of the TF node in the TF-let remains unchanged and moves as a whole. Dragging the control point up and down can also change the value of the entire point, thereby changing the transparency of the organization corresponding to the TF let.

2. The medical data visualization method based on histograms and nonlinear embedded transfer functions according to claim 1, characterized in that, The implementation process of step (1) is as follows: The medical human tissue data is sliced ​​and organized, with a reflection time interval of 1 millisecond between each slice; the data slices are aligned with the actual physical space according to the reflection time, and the aligned data is the three-dimensional volume data; the three-dimensional volume data is then transferred from the CPU to the GPU.

3. The medical data visualization method based on histograms and nonlinear embedded transfer functions according to claim 1, characterized in that, The implementation process of step (2) is as follows: The vertical axis of the histogram represents opacity, and the horizontal axis uses a non-linear mapping design; the histogram is rendered as a grid to reduce visual clutter; the bin size of the data is calculated during data preprocessing for histogram plotting; the group interval calculation formula is: ； in, This represents the size of each bin, where i represents the bin number; the width of the vertical axis is increased at low scale values ​​and calculated using the following formula: ； In a non-linear TF editor, the vertical axis represents opacity, or α value. At low scale values, the width of the vertical axis is increased, and the transformation of the control points and nonlinear coordinates is as follows: ； Here, α represents the amplification level; the larger α is, the wider the low-value portion of the nonlinear tf is.

4. The medical data visualization method based on histograms and nonlinear embedded transfer functions according to claim 1, characterized in that, The implementation process of step (3) is as follows: When a user obtains the best rendering results through TF-let, all color schemes are serialized to disk using the fast loading and reloading features for subsequent loading; When a user decides to serialize and load TF control point data, a TF let node appears, and the vertical coordinate of each node represents the maximum opacity of the corresponding TF let control point. TF-lets are associated with tissue data. All TF-lets are serialized for reloading and subsequent analysis. TF editors provide a TF-let for transfer function design, where each node of the TF corresponds to a TF-let of the original nonlinear transfer function. When performing TF editing, volume data is classified according to its distribution in the feature space.

Images