CN111507978A - Intelligent digital image processing system for urology surgery - Google Patents
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- 238000001356 surgical procedure Methods 0.000 title claims abstract description 17
- 230000003902 lesion Effects 0.000 claims abstract description 29
- 238000003745 diagnosis Methods 0.000 claims abstract description 23
- 210000000056 organ Anatomy 0.000 claims abstract description 22
- 238000005259 measurement Methods 0.000 claims abstract description 15
- 238000007781 pre-processing Methods 0.000 claims abstract description 7
- 230000011218 segmentation Effects 0.000 claims abstract description 4
- 238000005065 mining Methods 0.000 claims description 8
- 238000001514 detection method Methods 0.000 claims description 5
- 230000002485 urinary effect Effects 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000000354 decomposition reaction Methods 0.000 claims description 3
- 238000002059 diagnostic imaging Methods 0.000 claims description 3
- 238000001914 filtration Methods 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 abstract description 2
- 210000004204 blood vessel Anatomy 0.000 description 3
- 238000004195 computer-aided diagnosis Methods 0.000 description 3
- 210000005036 nerve Anatomy 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
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Abstract
The invention discloses an intelligent digital image processing system for urology surgery, which comprises a medical image acquisition module, a data processing module and a data processing module, wherein the medical image acquisition module is used for acquiring medical image data; the image preprocessing module is used for completing denoising processing of the medical image and realizing automatic threshold segmentation to obtain a binary image; the lesion region identification module is used for identifying a lesion region in the medical image binary image based on the Dssd-inclusion-V3 model and outputting a corresponding primary diagnosis result; the three-dimensional reconstruction module is used for reconstructing a three-dimensional model of a human organ according to the medical image data and the preliminary diagnosis result; the size measurement module is used for calling a corresponding measurement scale to acquire three-dimensional size data of the focal zone; and the diagnosis report generation module is used for filling the preliminary diagnosis result and the corresponding three-dimensional size data of the focus area into a prefabricated template to generate a diagnosis report. The invention realizes automatic accurate identification, positioning and comprehensive evaluation of the focus area.
Description
Technical Field
The invention relates to the field of medical image data processing, in particular to an intelligent digital image processing system for urology surgery.
Background
At present, the traditional computer-aided measurement (CAM) and computer-aided diagnosis (CAD) technologies in the existing medical image data processing system for urology surgery have more limitations, the detection result is unilateral and non-intuitive, comprehensive and accurate analysis on the condition of human organs cannot be realized in a true sense, and meanwhile, the traditional CAM and CAD technologies have great dependence on the working experience of detection personnel.
Disclosure of Invention
In order to solve the problems, the invention provides an intelligent digital image processing system for urology surgery, which realizes automatic accurate identification, positioning and comprehensive evaluation of a focus area.
In order to achieve the purpose, the invention adopts the technical scheme that:
an intelligent digital image processing system for urinary surgery comprises
The medical image acquisition module is used for acquiring medical image data through medical imaging equipment;
the image preprocessing module is used for completing denoising processing of the medical image and performing automatic threshold segmentation on the image subjected to denoising processing by adopting an Otsu algorithm to obtain a binary image;
the lesion region identification module is used for identifying a lesion region in the medical image binary image based on the Dssd-inclusion-V3 model and outputting a corresponding primary diagnosis result;
the three-dimensional reconstruction module is used for reconstructing a three-dimensional model of a human organ according to the medical image data and the preliminary diagnosis result;
the size measurement module is used for calling a corresponding measurement scale to acquire three-dimensional size data of the focal zone;
and the diagnosis report generation module is used for filling the obtained preliminary diagnosis result and the corresponding three-dimensional size data of the focus area into a prefabricated template to generate a diagnosis report.
Further, the image preprocessing module performs wavelet decomposition on the medical image, and then performs median filtering processing on the sub-images of each frequency band by using different thresholds (a random threshold and a hard threshold).
Further, the Dssd-inclusion-V3 model is trained on historical image sample data by using a Dssd-target detection algorithm.
Further, the three-dimensional reconstruction module firstly realizes reconstruction of a three-dimensional model of the human organ according to the medical image data and the preliminary diagnosis result, and then defines a focus area on the three-dimensional model according to the recognition result of the focus area.
Further, still include:
the lesion area coordinate generating module is used for constructing a three-dimensional coordinate system on the three-dimensional model of the human organ by taking the central point of the human organ as an origin, acquiring the coordinates of the central point of six surfaces of the lesion area, and is used for realizing the accurate positioning of the lesion area so as to facilitate medical staff to formulate a corresponding operation scheme;
further, still include:
and the lesion area surrounding environment mining module is used for mining surrounding environment parameters of the lesion area on the human organ three-dimensional model, so that medical personnel can conveniently make a corresponding operation scheme.
Furthermore, the size measurement module firstly identifies the graph of the focus area based on the length-width ratio of the connected component circumscribed rectangle, then divides the focus area into a plurality of areas formed by regular graphs according to the graph identification result, then calls corresponding measurement scales according to the division result to finish the acquisition of the size parameters of the regular graphs one by one, and then finishes the calculation of the size parameters according to a preset formula to obtain the three-dimensional size data of the focus area.
The invention has the following beneficial effects:
based on the Dssd-inclusion-V3 model, the automatic identification of the lesion region in the medical image binary image is realized, and the identification efficiency of the lesion region can be greatly improved while the identification accuracy of the lesion region is improved.
Based on the three-dimensional reconstruction module and the corresponding dimension measurement module, automatic acquisition of three-dimensional dimension data of the focal zone is realized, and the parameters of the surrounding environment of the focal zone are excavated on the three-dimensional model of the human organ by combining the mining module of the surrounding environment of the focal zone, so that the comprehensive analysis of the condition of the human organ can be realized.
Drawings
Fig. 1 is a system block diagram of an intelligent digital image processing system for urology surgery according to an embodiment of the present invention.
Detailed Description
In order that the objects and advantages of the invention will be more clearly understood, the invention is further described in detail below with reference to examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in FIG. 1, the present invention provides an intelligent digital image processing system for urology surgery, comprising
The medical image acquisition module is used for acquiring medical image data through medical imaging equipment;
the image preprocessing module is used for completing denoising processing of the medical image and performing automatic threshold segmentation on the image subjected to denoising processing by adopting an Otsu algorithm to obtain a binary image;
the lesion region identification module is used for identifying a lesion region in the medical image binary image based on the Dssd-inclusion-V3 model and outputting a corresponding primary diagnosis result;
the three-dimensional reconstruction module is used for reconstructing a three-dimensional model of a human organ according to the medical image data and the preliminary diagnosis result;
the size measurement module is used for calling a corresponding measurement scale to acquire three-dimensional size data of the focal zone;
the diagnosis report generation module is used for filling the obtained preliminary diagnosis result and the corresponding lesion area three-dimensional size data into a prefabricated template to generate a diagnosis report;
the lesion area coordinate generating module is used for constructing a three-dimensional coordinate system on the three-dimensional model of the human organ by taking the central point of the human organ as an origin, acquiring the coordinates of the central point of six surfaces of the lesion area, and is used for realizing the accurate positioning of the lesion area so as to facilitate medical staff to formulate a corresponding operation scheme;
the system comprises a focus area surrounding environment mining module, a surgery analysis module and a surgery analysis module, wherein the focus area surrounding environment mining module is used for mining focus area surrounding environment parameters on a human organ three-dimensional model so as to facilitate medical staff to formulate a corresponding surgery scheme; firstly, acquiring a left side, right side, front side, rear side, upper side and lower side adjacent organ, blood vessel or nerve distribution map of a focus area, then realizing the identification and size measurement of organs, blood vessels or nerves in the distribution map, wherein the identification of the organs, blood vessels or nerves and the corresponding size data are the surrounding environment parameters of the focus area;
and the central processor module is used for coordinating the work of the modules.
In this embodiment, the image preprocessing module performs wavelet decomposition on the medical image, and then performs median filtering on the sub-images of each frequency band by using different thresholds (i.e., a random threshold and a hard threshold), so that the edge information included in the image can be better maintained while the image noise is removed.
In this embodiment, the Dssd _ inclusion _ V3 model is trained based on historical image sample data by using a Dssd _ target detection algorithm.
In this embodiment, the three-dimensional reconstruction module first reconstructs a three-dimensional model of a human organ according to the medical image data and the preliminary diagnosis result, and then defines a lesion region on the three-dimensional model according to a recognition result of the lesion region.
In this embodiment, the size measurement module first identifies a graph of the focal zone based on an aspect ratio of a connected component circumscribed rectangle, then divides the focal zone into a plurality of regions formed by regular graphs according to a graph identification result, then calls corresponding measurement scales according to the division result to finish acquisition of size parameters of the regular graphs one by one, and then finishes calculation of the size parameters according to a preset formula to obtain three-dimensional size data of the focal zone.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be construed as the protection scope of the present invention.
Claims (6)
1. An intelligent digital image processing system for urology surgery is characterized by comprising
The medical image acquisition module is used for acquiring medical image data through medical imaging equipment;
the image preprocessing module is used for completing denoising processing of the medical image and performing automatic threshold segmentation on the image subjected to denoising processing by adopting an Otsu algorithm to obtain a binary image;
the lesion region identification module is used for identifying a lesion region in the medical image binary image based on the Dssd-inclusion-V3 model and outputting a corresponding primary diagnosis result;
the three-dimensional reconstruction module is used for reconstructing a three-dimensional model of a human organ according to the medical image data and the preliminary diagnosis result;
the size measurement module is used for calling a corresponding measurement scale to acquire three-dimensional size data of the focal zone;
and the diagnosis report generation module is used for filling the obtained preliminary diagnosis result and the corresponding three-dimensional size data of the focus area into a prefabricated template to generate a diagnosis report.
2. The intelligent digital image processing system for urinary surgery as claimed in claim 1, wherein the image preprocessing module performs wavelet decomposition on the medical image and then performs median filtering on the sub-images of each frequency band using different thresholds.
3. The intelligent digital image processing system for urinary surgery of claim 1, wherein the Dssd _ inclusion _ V3 model is trained based on historical image sample data using a Dssd _ target detection algorithm.
4. The intelligent digital image processing system for urinary surgery as claimed in claim 1, wherein the three-dimensional reconstruction module first reconstructs a three-dimensional model of the body organ based on the medical image data and the preliminary diagnosis result, and then defines a lesion region on the three-dimensional model based on the identification result of the lesion region.
5. The urological intelligent digital image processing system of claim 1, further comprising:
the lesion area coordinate generating module is used for constructing a three-dimensional coordinate system on the three-dimensional model of the human organ by taking the central point of the human organ as an origin, acquiring the coordinates of the central point of six surfaces of the lesion area, and is used for realizing the accurate positioning of the lesion area so as to facilitate medical staff to formulate a corresponding operation scheme;
the urological intelligent digital image processing system of claim 1, further comprising:
and the lesion area surrounding environment mining module is used for mining surrounding environment parameters of the lesion area on the human organ three-dimensional model, so that medical personnel can conveniently make a corresponding operation scheme.
6. The intelligent digital image processing system for urinary surgery as claimed in claim 1, wherein the size measuring module first identifies the pattern of the focal region based on the aspect ratio of the connected component circumscribed rectangle, then divides the focal region into a plurality of regions formed by regular patterns according to the pattern identification result, then invokes corresponding measuring scales according to the division result to collect the size parameters of the regular patterns, and then completes the calculation of the size parameters according to the preset formula to obtain the three-dimensional size data of the focal region.
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Cited By (6)
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| CN112287863A (en) * | 2020-11-09 | 2021-01-29 | 九江职业技术学院 | Computer portrait recognition system |
| CN112488136A (en) * | 2021-01-22 | 2021-03-12 | 山东商业职业技术学院 | Image recognition system and image recognition device |
| CN112801167A (en) * | 2021-01-25 | 2021-05-14 | 河北北方学院 | Analysis method of medical image big data |
| CN113243933A (en) * | 2021-05-20 | 2021-08-13 | 张涛 | Remote ultrasonic diagnosis system and use method |
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Application publication date: 20200807 |