CN-121999117-A - Three-dimensional image processing method and related device

CN121999117ACN 121999117 ACN121999117 ACN 121999117ACN-121999117-A

Abstract

The invention discloses a three-dimensional image processing method and a related device, wherein the three-dimensional image processing method comprises the steps of obtaining model feature data according to a three-dimensional model file of a three-dimensional image, wherein the model feature data comprise a surface set in the three-dimensional image and vertex sets in each surface, determining sharpness of each vertex according to the vertex sets and the surface sets, determining a residual vertex set according to the sharpness of each vertex and a preset cut-off rule, and updating the vertex sets and the surface sets according to the residual vertex sets and the vertex sets in the model feature data to obtain updated model feature data. The invention can effectively reduce unnecessary details or noise in the three-dimensional model and remove sharp protrusions in the three-dimensional model.

Inventors

TIAN YE
CHEN LEI

Assignees

景昱医疗科技（苏州）股份有限公司

Dates

Publication Date: 20260508
Application Date: 20241106

Claims (15)

1. A three-dimensional image processing method, comprising the steps of: according to a three-dimensional model file of the three-dimensional image, model feature data are obtained, wherein the model feature data comprise a surface set in the three-dimensional image and a vertex set in each surface; determining the sharpness of each vertex according to the vertex set and the surface set; determining a residual vertex set according to the sharpness of each vertex and a preset cut-off rule; Updating the vertex set and the face set according to the rest vertex set and the vertex set in the model feature data to obtain updated model feature data; and constructing a new three-dimensional model file according to the updated model characteristic data so as to obtain an optimized three-dimensional image.
2. The method of three-dimensional image processing according to claim 1, wherein determining sharpness of each vertex from the set of vertices and the set of faces comprises: traversing the surface sets to determine a first surface set associated with any vertex; extracting all points in the associated first surface set, taking the vertex as a starting point, and taking other points in the extracted first surface set as end points to construct a first vector set associated with the vertex; The sharpness of the vertex is determined from the first set of vectors.
3. The method of three-dimensional image processing according to claim 2, wherein said determining the sharpness of the vertex from the first set of vectors comprises: Calculating a first average vector of the first set of vectors; Calculating a vector included angle set formed between each first vector in the first vector set and the first average vector; and calculating an included angle average value in the vector included angle set, wherein the included angle average value is the sharpness of the vertex.
4. The method of three-dimensional image processing according to claim 2, wherein said determining the sharpness of each vertex from said first set of vectors comprises: And projecting each first vector in the first vector set onto a spherical surface with any vertex as a sphere center and a radius of 1, and calculating the dispersion of points projected onto the spherical surface to make the dispersion be the sharpness.
5. The method according to claim 1, wherein determining the remaining vertex set according to the sharpness of each vertex and a preset cutoff rule comprises: and determining the rest vertex set according to the preset sharpness threshold value, the sharpness of each vertex and the vertex set.
6. The method according to claim 5, wherein determining the remaining vertex set according to the preset sharpness threshold, the sharpness of each vertex, and the vertex set comprises: and taking the vertex with the sharpness larger than the preset sharpness threshold value as the residual vertex, and writing the residual vertex into a residual vertex set.
7. The method according to claim 5, wherein determining the remaining vertex set according to the preset sharpness threshold, the sharpness of each vertex, and the vertex set comprises: When the ratio of the vertexes with sharpness exceeding a preset sharpness threshold exceeds a first proportion threshold, sorting the vertexes in the vertex set according to the order of sharpness from big to small to obtain a vertex sequence; And taking the vertex of the first previous proportional threshold value in the vertex sequence as a residual vertex, and writing the residual vertex into a residual vertex set.
8. The method of three-dimensional image processing according to claim 1, wherein updating the vertex set and the face set based on the remaining vertex set and the vertex set in the model feature data to obtain updated model feature data comprises: determining adjacent points of each residual vertex according to the residual vertex set and the model characteristic data, wherein the adjacent points represent vertexes connected with the residual vertexes in the vertex set; Determining an optimized coordinate point of each residual vertex according to the coordinates of all adjacent points of each residual vertex to obtain an optimized coordinate point set, and updating the vertex set and the face set according to the optimized coordinate point set; Determining the sharpness of each vertex in the updated vertex set according to the updated vertex set and the surface set; judging whether the sharpness of each vertex in the updated vertex set meets a preset convergence condition or not: if yes, writing the updated vertex set and the updated face set into the updated model feature data; and if not, re-executing the step of determining the residual vertexes of the adjacent points of each residual vertex according to the residual vertex set and the model characteristic data until the preset convergence condition is met.
9. The method according to claim 8, wherein determining the optimal coordinate point of each of the remaining vertices based on coordinates of all neighboring points of each of the remaining vertices comprises: and determining the geometric centers of all corresponding adjacent points according to the coordinates of all adjacent points of any residual vertex, and taking the geometric centers as optimized coordinate points of the residual vertex.
10. The method according to claim 9, wherein determining the geometric center of all neighboring points corresponding to any of the remaining vertices according to the coordinates of all neighboring points comprises: traversing the surface set, and determining a second surface set associated with any of the residual vertexes; Extracting all points in the associated second surface set, taking all extracted points as adjacent points of the residual vertex, taking the residual vertex as a starting point, taking each adjacent point as an end point, constructing a second vector set associated with the residual vertex, and calculating a second average vector of the second vector set; And taking the starting point of the second average vector as the residual vertex, and taking the end point of the second average vector as the geometric center of the adjacent point of the residual vertex.
11. A three-dimensional image processing apparatus, comprising: The building module is used for obtaining model feature data according to a three-dimensional model file of the three-dimensional image, wherein the model feature data comprises a surface set in the three-dimensional image and a vertex set in each surface; a sharpness module for determining sharpness of each vertex from the set of vertices and the set of faces; The cutting module is used for determining a residual vertex set according to the sharpness of each vertex and a preset cutting rule; The updating module is used for updating the vertex set and the surface set according to the residual vertex set and the vertex set in the model characteristic data so as to obtain updated model characteristic data; and the optimizing module is used for constructing a new three-dimensional model file according to the updated model characteristic data so as to obtain an optimized three-dimensional image.
12. An implantable medical system, the implantable medical system comprising: An implantable medical device for implantation within a patient, the implantable medical device comprising a stimulation electrode implanted within a brain of the patient; A first processor for receiving a three-dimensional image acquired by the implantable medical device of a patient and configured to perform the three-dimensional image processing method of any one of claims 1 to 10 to enable processing of the three-dimensional image; The display device is configured to display the three-dimensional image processed by the first processor; The three-dimensional image at least comprises one of a three-dimensional image of brain tissue in the brain of a patient and an electric field three-dimensional image of a stimulation electrode.
13. A medical device comprising a second processor, a memory and a computer program stored on the memory, characterized in that the second processor executes the computer program to carry out the steps of the three-dimensional image processing method according to any one of claims 1 to 10.
14. A computer-readable storage medium having stored thereon a computer program/instruction for implementing the steps of the three-dimensional image processing method according to any one of claims 1 to 10 by a third processor.
15. A computer program product comprising computer programs/instructions which, when executed by a fourth processor, implement the steps of the three-dimensional image processing method of any one of claims 1 to 10.

Description

Three-dimensional image processing method and related device Technical Field The present invention relates to the field of three-dimensional image processing technologies, and in particular, to a three-dimensional image processing method and a related device. Background In the field of three-dimensional modeling and medical image processing, the storage and conversion of three-dimensional model files are indispensable key links. Common three-dimensional model file types, such as ply, stl, obj, etc., store geometric information of points, lines, planes, etc., in their unique formats. The information of the points is mainly composed of index numbers and x, y, z three-dimensional coordinates, and the information of the faces is composed of index numbers associated with the points constituting the polygonal faces (most triangular faces) by the index numbers. The data structure of this information provides a solid basis for the construction, rendering and analysis of three-dimensional models. However, in practical applications, particularly in the process of converting a medical image NIfTI file into a three-dimensional model file, the converted three-dimensional model file often generates some unnecessary sharp protrusions due to limitations of file format conversion, data compression or processing algorithms. These protrusions are not true reflections of medical science, but are abnormal products in the computer process, and these sharp protrusions not only affect the visual effect of the three-dimensional model, but may also mislead subsequent medical diagnosis, surgical planning, etc. applications. To remove these sharp protrusions, researchers have developed various algorithms such as Laplacian smoothing algorithm, taubin smoothing algorithm, bilateral filtering algorithm, non-local mean filtering algorithm, remove gadget algorithm, morphological operations, curvature flow smoothing algorithm, adaptive smoothing algorithm, and laplace beltermi operator, etc. However, these algorithms have various degrees of limitations in practical applications: (1) The global influence is that the sharp bulge and the non-sharp area cannot be accurately distinguished in the processing process, so that the size and the shape of other non-sharp areas are changed while the sharp bulge is removed, and the integral structure and detail characteristics of the three-dimensional model are damaged. (2) The parameter adjustment is complex, and although some algorithms can remove sharp protrusions to a certain extent, the algorithm parameters need to be dynamically adjusted according to specific cases so as to achieve the best effect. The complexity of parameter adjustment not only increases the application difficulty of the algorithm, but also makes the algorithm unable to uniformly process all three-dimensional models related to a class of tasks, and limits the generality and practicality of the algorithm. Therefore, the present application provides a three-dimensional image processing method and related apparatus. Disclosure of Invention The invention aims to provide a three-dimensional image processing method and a related device, which can effectively reduce unnecessary details or noise in a three-dimensional model and remove sharp protrusions in the three-dimensional model. The invention adopts the following technical scheme: In one aspect, the present invention provides a three-dimensional image processing method, including the steps of: according to a three-dimensional model file of the three-dimensional image, model feature data are obtained, wherein the model feature data comprise a surface set in the three-dimensional image and a vertex set in each surface; determining the sharpness of each vertex according to the vertex set and the surface set; determining a residual vertex set according to the sharpness of each vertex and a preset cut-off rule; Updating the vertex set and the face set according to the rest vertex set and the vertex set in the model feature data to obtain updated model feature data; and constructing a new three-dimensional model file according to the updated model characteristic data so as to obtain an optimized three-dimensional image. The method has the advantages that unnecessary details or noise in the three-dimensional model can be effectively reduced by calculating the sharpness of each vertex and carrying out truncation processing according to the sharpness, sharp protrusions in the three-dimensional model are removed, so that three-dimensional images are smoother and more consistent visually, furthermore, the calculated amount in rendering can be remarkably reduced by the optimized three-dimensional model file due to the fact that the number of the unnecessary vertices and the number of the patches are reduced, and therefore the rendering speed and the rendering efficiency are improved, and in addition, the volume of the three-dimensional model file represented by updated model feature data can be