CN108257202B - Medical image volume reconstruction optimization method based on use scene - Google Patents

Medical image volume reconstruction optimization method based on use scene Download PDF

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CN108257202B
CN108257202B CN201711477207.8A CN201711477207A CN108257202B CN 108257202 B CN108257202 B CN 108257202B CN 201711477207 A CN201711477207 A CN 201711477207A CN 108257202 B CN108257202 B CN 108257202B
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mapping function
histogram feature
medical image
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histogram
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CN108257202A (en
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李均利
魏平
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Sichuan Yunshixin Medical Laboratory Co Ltd
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Sichuan Normal University
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Abstract

The invention discloses a medical image volume reconstruction optimization method based on a use scene, which comprises the steps of establishing a template library containing a plurality of histogram features, wherein each histogram feature is provided with a plurality of mapping functions and a default mapping function; acquiring a medical image input by a user, calculating histogram features of the medical image, matching a histogram feature template library, and performing three-dimensional reconstruction on a new sequence of the medical image by using a matched default mapping function; monitoring a mapping function selected by a user, performing three-dimensional reconstruction on the medical image, updating the use frequency of the mapping function, and setting the mapping function with the highest use frequency as a default mapping function matched with the histogram feature template; and updating the histogram feature template library when the histogram feature template or the mapping function is monitored to be added/modified/deleted. The invention provides a method capable of quickly adapting to personal diagnosis habits of doctors, and volume reconstruction is more and more matched with the use scenes of the doctors by optimizing a mapping function so as to improve film reading efficiency.

Description

Medical image volume reconstruction optimization method based on use scene
Technical Field
The invention relates to an optimization selection method of mapping functions (including an opacity mapping function and a color mapping function) in a medical image volume reconstruction process, in particular to an optimization method of a medical image volume reconstruction mapping function in a fixed use scene.
Background
Three-dimensional data needs to be rendered and displayed in the volume reconstruction, and an opacity mapping function and a color mapping function are needed in the process. The appropriate opacity mapping function and color mapping function in different region image reconstructions generated by different devices are different, and even if the same image is used, a doctor may want to have different opacity mapping functions and color mappings when watching different tissues so as to observe different pathological features. The using scenes of doctors are relatively fixed (CT machines and personal diagnosis habits), usually three-dimensional processing software initializes a color mapping table, but the mapping table is fixed for all devices and all doctors using the mapping table, so that many initial mapping tables cannot well meet the requirements of doctors, and the adjustment of the mapping table is time-consuming and inefficient operation.
Disclosure of Invention
The invention aims to provide a method which can quickly adapt to personal diagnosis habits of doctors and optimize a mapping function, so that volume reconstruction is more and more matched with the use scenes of the doctors, and the film reading efficiency is improved.
In order to achieve the purpose, the invention adopts the technical scheme that:
a medical image volume reconstruction optimization method based on a use scene is characterized by comprising the following steps:
step S100: creating a histogram feature template library which comprises a plurality of histogram feature templates, wherein each histogram feature template is initialized with a plurality of mapping functions for different display parts and a default mapping function, and the mapping function is an opacity mapping function, or a color mapping function, or a combination of the opacity mapping function and the color mapping function; recording the use frequency of the mapping function; the opacity mapping function maps the pixel values of the sampling points into different opacity values, the color mapping function maps the pixel values of the sampling points into RGB values, and the use frequency is used for recording the cumulative use times of the current mapping function; initializing and setting the default mapping function;
step S101: acquiring a new medical image sequence input by a user, and calculating histogram features of the new medical image sequence;
step S102: matching the histogram features of the new sequence of the medical image with all the histogram feature templates of the stored templates, and selecting the histogram feature template with the highest correlation as a matched histogram feature template;
step S103: searching a default mapping function matched with the histogram feature template, performing three-dimensional reconstruction on the new medical image sequence by using the default mapping function to draw a three-dimensional image, and sequencing the mapping functions matched with the histogram feature template according to the use frequency for selection;
step S104: monitoring user operation;
step S105: judging whether a mapping function is selected, if not, continuing to execute the step S104, and if so, executing the step S106;
step S106: according to the selected mapping function, three-dimensional reconstruction is carried out on the new medical image sequence to draw a three-dimensional image;
step S107: and updating the use frequency of the mapping function in the matched histogram feature template, updating the sequencing result of the mapping function of the matched histogram feature template, and setting the mapping function with the highest use frequency as the default mapping function of the matched histogram feature template.
Preferably, the opacity mapping function is a piecewise linear scalar mapping function, and the opacity corresponding to any one pixel value can be determined by the opacity mapping function; the color mapping function is a scalar to RGB color mapping, and is a piecewise linear mapping function, and the color value corresponding to any pixel value can be determined by the color mapping function.
Preferably, the method for calculating histogram features of the new sequence of medical images in step S101 is as follows: (ii) the pixel value range [ min, max ] of the new sequence of medical images]Equally divided into 100 intervals QkWherein k is 1,2, …, 100;
when k is 1,2, …,99, Qk(min + (max-min) k-100, min + (max-min) k/100) is the post-closure open interval; when k is 100, Q100＝[min+(max-min)*99/100,max]Is a closed interval;
counting the pixel values of the new sequence of the medical image in each pixel value interval QkIs present at a rate v1,v2...v100Then the histogram feature of the new sequence of medical images is V ═ { V ═ V1,v2,…,v100Therein of
Figure GDA0003051936030000021
Preferably, the method for calculating the correlation in step S102 is:
the correlation ρ (V, V ') of any two histogram features V and V', then
Figure GDA0003051936030000031
Wherein v'0＝0,v′1＝0v′101＝0,v′102＝0。
Preferably, after step S104, the following steps are also included: step S108: monitoring whether the histogram feature template or the mapping function is added or modified or deleted, if not, continuing to execute the step S104, and if so, executing the step S109; step S109: and updating the histogram feature template library.
The invention provides an optimized selection method of a transparency function and a color mapping table, an initial histogram feature template and a mapping function template are provided, a doctor can perfect and optimize the mapping function in the using process, and can quickly match the mapping function which accords with the personal diagnosis habit of the doctor, so that the volume reconstruction is more and more matched with the using scene of the doctor.
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FIG. 1 is a flow chart of an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a histogram feature template library according to the present invention.
Detailed Description
The patent is described in further detail below by way of specific embodiments, but not limited to, with reference to the figures.
Fig. 1 is a flowchart of a medical image volume reconstruction optimization method based on a usage scenario according to an embodiment of the present invention, where the method includes:
step S100: and creating a histogram feature template library, setting a plurality of mapping functions and a default mapping function for each histogram feature template, and initializing and setting the default mapping function.
Creating a histogram feature template library, wherein the library is provided with N histogram feature templates, each histogram feature template is initialized with a plurality of mapping functions for different display parts and is provided with a default mapping function, and the mapping function in the embodiment is a combination of an opacity mapping function and a color mapping function, namely, a user can respectively adjust the opacity mapping function and the color mapping function; and recording the use frequency of the mapping function, and recording the accumulated use times of the current mapping function.
As shown in FIG. 2, the N histogram feature templates are ViI-1, 2, …, N, the ith histogram feature corresponds to K different opacity mapping functions ai,jAnd a color mapping function Ci,jUsing frequency Ti,jWhere j is 1,2, …, K. The histogram feature library is stored in the database in the following two tables. T isi,jThe initialization is 0.
Figure GDA0003051936030000041
Figure GDA0003051936030000042
The opacity mapping function is a piecewise linear scalar mapping function and is used for mapping pixel values of sampling points to different opacity values, the initially set opacity mapping function can be input after being adjusted by a user, and can also be generated into fixed sets of functions by a program, and the initially set opacity mapping function is only used as a reference in the invention.
The color mapping function is a piecewise linear scalar mapping function, the pixel value of the sampling point is mapped to RGB value, the initially set color mapping function can be input after being adjusted by a user, or fixed sets of functions can be generated by a program, and the initially set color mapping function is only used as reference in the invention.
And selecting the mapping function with the highest use frequency from the created mapping functions, setting the mapping function as a default mapping function (the opacity mapping function and the color mapping function), and selecting the mapping function created finally when the use frequencies are the same.
Step S101: and acquiring a new sequence of medical images input by a user, and calculating histogram features of the new sequence.
When a doctor starts to read a film, a new sequence of medical images input by a user needs to be read, the histogram features of the new sequence are calculated according to the following method:
the pixel value range [ min, max ] of the medical image]Equally divided into 100 intervals QkWhere k is 1,2, …, 100.
Q when k is 1,2, …,99k(vi) [ min + (max-min) × (k-1)/100, ] is the post-closure open interval; when k is 100, it is a closed interval, i.e. Q100＝[min+(max-min)*99/100,max]。
Counting the pixel values of the medical image sequence in each pixel value interval QkIs present at a rate v1,v2...v100Wherein
Figure GDA0003051936030000051
Obtaining a new sequence of medical images with a histogram feature of V ═ V1,v2,…,v100}。
Step S102: and matching histogram features, and finding a matched histogram feature template.
And matching the histogram features of the new sequence of the medical image obtained by calculation with all histogram feature templates of the stored templates, and selecting the histogram feature template with the highest correlation as a matched histogram feature template.
The correlation ρ (V, V ') of the two histogram features V and V' is defined as:
Figure GDA0003051936030000052
v′0＝0,v′1＝0v′101＝0,v′102the definition of correlation described above, which is 0, makes the similarity of histogram features robust to a certain pixel value shift. In addition, it is noted that the above definition of correlation does not take v1This is to avoid a large number of background pixel point pairsThe calculation of the similarity causes interference.
Step S103: and searching a default mapping function matched with the histogram feature template, performing three-dimensional reconstruction by using the mapping function, and sequencing the mapping functions matched with the histogram feature template for selection.
Searching a default mapping function matched with the histogram feature template, performing three-dimensional reconstruction on a new sequence of the medical image by using the default mapping function to draw a three-dimensional image, and sequencing the mapping functions matched with the histogram feature template according to the use frequency for a user to select;
step S104: user operation is monitored.
When the doctor watches different tissues, the doctor selects different opacity mapping functions and color mappings according to the sorted mapping functions provided in step S103 and the self-interpretation habit so as to observe different pathological features, and meanwhile, when the selected mapping function does not meet the requirements, the mapping function needs to be adjusted.
Two operations of the user therefore need to be monitored: (1) whether a mapping function matching the histogram feature template is selected; (2) whether a histogram feature template or mapping function is added/modified/deleted.
Step S105: judging whether a mapping function is selected, if not, continuing to execute the step S104, and if so, executing the step S106;
step S106: according to the mapping function selected by the user, three-dimensional reconstruction is carried out on the current new medical image sequence to draw a three-dimensional image;
step S107: and updating the use frequency of the mapping function in the matched histogram feature template, updating the sequencing result of the mapping function of the matched histogram feature template, and setting the mapping function with the highest use frequency as the default mapping function of the matched histogram feature template.
Step S108: monitoring whether a histogram feature template or a mapping function is added/modified/deleted, if not, continuing to execute the step S104, and if so, executing the step S109;
if a new histogram feature template is created, a histogram feature, corresponding sets of mapping functions (opacity mapping function and color mapping function) are added, and the corresponding mapping function usage frequency is initialized. In this case, the histogram feature library has N +1 histogram features, where N is N + 1.
And if the original histogram feature template is monitored to be modified/deleted, modifying/deleting operation is carried out on a certain histogram feature.
And if the original mapping function is monitored to be added/modified/deleted, adding/modifying/deleting the transparency function and the color mapping function corresponding to a certain histogram feature.
Step S109: the histogram feature template library is updated immediately after step S108 is performed.
According to the invention, through quickly matching the histogram feature library template and recording the habit of using the mapping function when a doctor reads the film (medical image), the doctor can perfect and optimize the mapping function in the using process, so that the volume reconstruction is more and more matched with the using scene of the doctor, and after the method disclosed by the invention is used for multiple times, the mapping function recommended to the doctor is more in line with the personal diagnosis habit of the doctor, the repeated adjustment of the mapping table is avoided, and the film reading efficiency is greatly improved.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present patent shall be included in the protection scope of the present patent.

Claims (5)

1. A medical image volume reconstruction optimization method based on a usage scenario is characterized by comprising the following steps:
step S100: creating a histogram feature template library which comprises a plurality of histogram feature templates, wherein each histogram feature template is initialized with a plurality of mapping functions for different display parts and a default mapping function, and the mapping function is an opacity mapping function, or a color mapping function, or a combination of the opacity mapping function and the color mapping function; recording the use frequency of the mapping function; the opacity mapping function maps the pixel values of the sampling points into different opacity values, the color mapping function maps the pixel values of the sampling points into RGB values, and the use frequency is used for recording the cumulative use times of the current mapping function; initializing and setting the default mapping function;
step S101: acquiring a new medical image sequence input by a user, and calculating histogram features of the new medical image sequence;
step S102: matching the histogram features of the new sequence of the medical image with all the histogram feature templates of the stored templates, and selecting the histogram feature template with the highest correlation as a matched histogram feature template;
step S103: searching a default mapping function matched with the histogram feature template, performing three-dimensional reconstruction on the new medical image sequence by using the default mapping function to draw a three-dimensional image, and sequencing the mapping functions matched with the histogram feature template according to the use frequency for selection;
step S104: monitoring user operation;
step S105: judging whether a mapping function is selected, if not, continuing to execute the step S104, and if so, executing the step S106;
step S106: according to the selected mapping function, three-dimensional reconstruction is carried out on the new medical image sequence to draw a three-dimensional image;
step S107: and updating the use frequency of the mapping function in the matched histogram feature template, updating the sequencing result of the mapping function of the matched histogram feature template, and setting the mapping function with the highest use frequency as the default mapping function of the matched histogram feature template.
2. The medical image volume reconstruction optimization method based on the usage scenario as claimed in claim 1, wherein: the opacity mapping function is a piecewise linear scalar mapping function, and the opacity corresponding to any pixel value is determined by the opacity mapping function; the color mapping function is a scalar to RGB color mapping, and is a piecewise linear mapping function, and the color value corresponding to any one pixel value is determined by the color mapping function.
3. A medical image volume reconstruction optimization method based on usage scenarios according to any of claims 1-2, characterized by: the method for calculating the histogram feature of the new sequence of the medical images in step S101 is as follows:
(ii) the pixel value range [ min, max ] of the new sequence of medical images]Equally divided into 100 intervals QkWherein k is 1,2, …, 100;
when k is 1,2, …,99, Qk(min + (max-min) k-100, min + (max-min) k/100) is the post-closure open interval; when k is 100, Q100＝[min+(max-min)*99/100,max]Is a closed interval;
counting the pixel values of the new sequence of the medical image in each pixel value interval QkIs present at a rate v1,v2...v100Then the histogram feature of the new sequence of medical images is V ═ { V ═ V1,v2,…,v100Therein of
Figure FDA0003051936020000021
4. A medical image volume reconstruction optimization method based on usage scenarios according to any of claims 1-2, characterized by: the correlation calculation method in step S102 is:
the correlation ρ (V, V ') of any two histogram features V and V', then
Figure FDA0003051936020000022
Wherein, v'0＝0,v′1＝0, v′101＝0,v′102＝0；vkPixel values of a histogram feature V representing a new sequence of medical images in a pixel value interval Qk(iii) the occurrence of (c); v'k+lThe pixel value of histogram feature V' of each histogram feature template in the stored template is in the pixel value regionM Qk+lThe occurrence of (c).
5. A medical image volume reconstruction optimization method based on usage scenarios according to any of claims 1-2, characterized by: the method also comprises the following steps after the step S104:
step S108: monitoring whether the histogram feature template or the mapping function is added or modified or deleted, and if not, continuing to execute the step S104; if yes, go to step S109;
step S109: and updating the histogram feature template library.
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