CN113658160A - Optimization and segmentation method and device for blood vessel-attached pulmonary nodule - Google Patents
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Abstract
The invention relates to a method and a device for optimizing and segmenting a vessel-attached pulmonary nodule, wherein the method comprises the following steps: acquiring a lung image; overlapping lung nodules and blood vessels of the lung image to perform enhancement processing on the lung nodules; extracting a lung parenchyma contour including a lung nodule from the lung image; automatically segmenting the obtained lung parenchyma contour by a graph cutting method to obtain a final contour of a lung nodule; the method enhances the pulmonary nodules, avoids interference of blood vessels during segmentation, and improves the segmentation accuracy of the pulmonary nodules.
Description
Technical Field
The invention relates to the technical field of medical auxiliary diagnosis, in particular to a method and a device for optimizing and segmenting a blood vessel-attached lung nodule.
Background
The lung cancer becomes a malignant tumor with the highest morbidity and mortality worldwide, seriously threatens the life and health of human beings, early discovery is an effective method for improving the treatment effect of lung cancer patients, and meanwhile, the detection and identification of lung nodules are increasingly important in lung cancer treatment because the lung nodules are early forms of the lung cancer. The accurate segmentation of the lung nodule is the key content of the lung nodule detection and identification research, and directly influences the reliability of the lung nodule auxiliary diagnosis technology.
In a CT image, lung nodules adhered to lung tissues are common, while the traditional segmentation method has a certain segmentation effect on isolated lung nodules, and the adjacency relation between the lung nodules and other tissues is less considered, so that the segmentation error is large, and the segmentation accuracy is low.
Disclosure of Invention
The invention aims to provide a method and a device for optimizing and segmenting a blood vessel-attached pulmonary nodule, and aims to solve the problems that in the prior art, a pulmonary nodule segmentation error of a central lung image is large, and the segmentation accuracy is low.
The technical purpose of the invention is realized by the following technical scheme:
in a first aspect, an embodiment of the present application provides a method for vessel-dependent lung nodule optimization and segmentation, the method including the steps of:
acquiring a lung image;
overlapping lung nodules and blood vessels of the lung image to perform enhancement processing on the lung nodules;
extracting a lung parenchyma contour including a lung nodule from the lung image;
and automatically segmenting the obtained lung parenchyma contour by a graph segmentation method to obtain a final contour of the lung nodule.
In some embodiments, overlapping the lung nodule and the blood vessel of the lung image to perform enhancement processing on the lung nodule includes:
directly overlapping an original lung nodule of the lung image with an original blood vessel;
blurring and overlapping original pulmonary nodules and blood vessels according to a first coefficient;
and blurring the lung nodule according to a second coefficient and overlapping the lung nodule and the blood vessel according to the second coefficient.
In some embodiments, the extracting a lung parenchymal contour including a lung nodule from the lung image comprises:
extracting a lung parenchyma contour including lung nodules from the lung image to obtain a lung parenchyma edge curve;
and correcting the lung parenchyma edge curve according to the curvature and the inertia rule of curve extension to obtain the lung parenchyma contour.
In some embodiments, the automatically segmenting the obtained lung parenchymal contour by a graph segmentation method to obtain a final contour of a lung nodule includes:
automatically segmenting the obtained lung parenchymal contour to obtain an initial contour of a lung nodule therein, and then initially segmenting the obtained lung nodule by adopting a kernel graph segmentation method to obtain an inner contour of the initial lung nodule;
extracting the central point of the inner contour of the primary pulmonary nodule and the boundary point of the periphery of the inner contour;
and further segmenting the lung nodule by using the extracted central point and boundary point and adopting a graph segmentation method based on a maximum flow algorithm to obtain the final contour of the lung nodule.
In some embodiments, the extracting a lung parenchymal contour including a lung nodule on the lung image obtains a lung parenchymal edge curve; the method for correcting the pulmonary parenchyma edge curve according to the curvature and the inertia law of curve extension to obtain the pulmonary parenchyma comprises the following steps:
obtaining the curvature K of the pulmonary nodule boundary curve and the pulmonary parenchyma boundary curve intersected with the pulmonary nodule boundary curve according to the following formula, and extracting points with changed curvature directions;
judging the concavity and convexity of each part of the curve according to the curvature radius rho defined by the following formula;
wherein Ix is a first derivative of a coordinate x of the curve, Iy is a first derivative of a coordinate y of the curve, Ixx is a second derivative of a coordinate x of the curve, Iyy is a second derivative of a coordinate y of the curve, and M is an empirical coefficient;
obtaining the inflection point when the lung parenchymal boundary curve and the lung nodule boundary curve are intersected, and removing the curve segment with small curvature;
and correcting the edge of the curve by using the point of the change of the curvature direction and combining with the inertia rule of curve extension by a curve fitting method to obtain the lung parenchyma contour including the lung nodule.
In a second aspect, an embodiment of the present application provides a device for vessel-dependent lung nodule optimization and segmentation, including:
the image acquisition module is used for acquiring lung images;
the enhancement processing module is used for overlapping the pulmonary nodules and the blood vessels of the lung images so as to perform enhancement processing on the pulmonary nodules;
the contour extraction module is used for extracting a lung parenchyma contour containing a lung nodule from the lung image;
and the contour acquisition module is used for automatically segmenting the obtained lung parenchyma contour by a graph segmentation method to obtain a final contour of the lung nodule.
In some of these embodiments, the enhancement processing module comprises:
a first processing unit for directly overlaying an original lung nodule of the lung image with an original blood vessel;
the second processing unit is used for blurring and overlapping the original pulmonary nodule and the blood vessel according to a first coefficient;
and the third processing unit is used for blurring the lung nodule according to the second coefficient and then overlapping the lung nodule with the blood vessel after blurring according to the second coefficient.
In some of these embodiments, the contour acquisition module comprises:
the initial segmentation unit is used for automatically segmenting the obtained lung parenchymal contour to obtain an initial contour of a lung nodule, and then performing initial segmentation on the obtained lung nodule by adopting a kernel graph segmentation method to obtain an inner contour of the initial lung nodule;
the important point extraction unit is used for extracting the central point of the inner contour of the primary pulmonary nodule and the boundary points of the periphery of the inner contour;
and the final segmentation unit is used for further segmenting the lung nodule by using the extracted central point and the extracted boundary point and adopting a graph segmentation method based on a maximum flow algorithm to obtain a final contour of the lung nodule.
In a third aspect, embodiments of the present application provide a computer device, including a memory and one or more processors;
the memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a method for vessel-dependent lung nodule optimization and segmentation as described in the first aspect.
In a fourth aspect, embodiments of the present application provide a storage medium containing computer executable instructions for performing the method for vessel dependent lung nodule optimization and segmentation as described in the first aspect when executed by a computer processor.
The invention has the beneficial effects that: the invention obtains lung images; overlapping lung nodules and blood vessels of the lung image to perform enhancement processing on the lung nodules; extracting a lung parenchyma contour including a lung nodule from the lung image; automatically segmenting the obtained lung parenchyma contour by a graph cutting method to obtain a final contour of a lung nodule; the pulmonary nodules are enhanced, the interference of blood vessels during segmentation is avoided, and the segmentation accuracy of the pulmonary nodules is improved.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic representation of the steps of a method for vessel-dependent lung nodule optimization and segmentation;
FIG. 2 is a schematic structural diagram of an apparatus for vessel-dependent lung nodule optimization and segmentation;
fig. 3 is a schematic diagram of a computer device for a vessel-dependent lung nodule optimization and segmentation method.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and the detailed description, and it should be noted that any combination of the embodiments or technical features described below can be used to form a new embodiment without conflict.
Referring to fig. 1, a method for vessel-dependent lung nodule optimization and segmentation according to the present invention is shown, the method comprising the steps of:
100. a lung image is acquired.
200. And overlapping the pulmonary nodules and the blood vessels of the lung image to perform enhancement processing on the pulmonary nodules.
Specifically, the original pulmonary nodules of the lung image are directly overlapped with the original blood vessels; blurring and overlapping original pulmonary nodules and blood vessels according to a first coefficient; blurring the pulmonary nodule according to a second coefficient, and overlapping the pulmonary nodule and the blood vessel according to the second coefficient; it is understood that the first coefficient and the second coefficient may be set according to specific situations, and the embodiment of the present application is not limited thereto.
300. And extracting a lung parenchyma contour including a lung nodule from the lung image.
Specifically, a lung parenchyma contour including a lung nodule is extracted from the lung image, and a lung parenchyma edge curve is obtained; and correcting the lung parenchyma edge curve according to the curvature and the inertia rule of curve extension to obtain the lung parenchyma contour.
Optionally, the curvature K of the pulmonary nodule boundary curve and the pulmonary parenchyma boundary curve intersected with the pulmonary nodule boundary curve are obtained according to the following formula, and a point of curvature direction change is extracted;
judging the concavity and convexity of each part of the curve according to the curvature radius rho defined by the following formula;
wherein Ix is a first derivative of a coordinate x of the curve, Iy is a first derivative of a coordinate y of the curve, Ixx is a second derivative of a coordinate x of the curve, Iyy is a second derivative of a coordinate y of the curve, and M is an empirical coefficient;
obtaining the inflection point when the lung parenchymal boundary curve and the lung nodule boundary curve are intersected, and removing the curve segment with small curvature;
and correcting the edge of the curve by using the point of the change of the curvature direction and combining with the inertia rule of curve extension by a curve fitting method to obtain the lung parenchyma contour including the lung nodule.
400. And automatically segmenting the obtained lung parenchyma contour by a graph segmentation method to obtain a final contour of the lung nodule.
Specifically, the obtained lung parenchymal contour is automatically segmented to obtain an initial contour of a lung nodule therein, and then the obtained lung nodule is initially segmented by adopting a kernel graph segmentation method to obtain an inner contour of the initial lung nodule; extracting the central point of the inner contour of the primary pulmonary nodule and the boundary point of the periphery of the inner contour; and further segmenting the lung nodule by using the extracted central point and boundary point and adopting a graph segmentation method based on a maximum flow algorithm to obtain the final contour of the lung nodule.
Referring to fig. 2, there is shown an apparatus for vessel-dependent lung nodule optimization and segmentation according to the present invention, including: an image acquisition module 101, an enhancement processing module 102, a contour extraction module 103, and a contour acquisition module 104.
The image acquisition module is used for acquiring lung images; the enhancement processing module is used for overlapping the pulmonary nodules and the blood vessels of the lung images so as to perform enhancement processing on the pulmonary nodules; the contour extraction module is used for extracting a lung parenchyma contour containing a lung nodule from the lung image; the contour acquisition module is used for automatically segmenting the obtained lung parenchyma contour through a graph segmentation method to obtain a final contour of a lung nodule.
Optionally, the enhancement processing module includes: a first processing unit, a second processing unit and a third processing unit; a first processing unit for directly overlaying an original lung nodule of the lung image with an original blood vessel; the second processing unit is used for blurring and overlapping the original pulmonary nodule and the blood vessel according to a first coefficient; the third processing unit is used for blurring the lung nodule according to the second coefficient and then overlapping the lung nodule with the blood vessel after blurring according to the second coefficient.
Optionally, the contour acquiring module includes: the device comprises an initial segmentation unit, a key point extraction unit and a final segmentation unit; the initial segmentation unit is used for automatically segmenting the obtained lung parenchymal contour to obtain an initial contour of a lung nodule, and then performing initial segmentation on the obtained lung nodule by adopting a kernel graph segmentation method to obtain an inner contour of the initial lung nodule; the important point extraction unit is used for extracting the central point of the inner contour of the primary pulmonary nodule and the boundary points of the periphery of the inner contour; and the final segmentation unit is used for further segmenting the lung nodule by using the extracted central point and the extracted boundary point and adopting a graph segmentation method based on a maximum flow algorithm to obtain a final contour of the lung nodule.
As described above, the embodiment of the present application acquires a lung image; overlapping lung nodules and blood vessels of the lung image to perform enhancement processing on the lung nodules; extracting a lung parenchyma contour including a lung nodule from the lung image; automatically segmenting the obtained lung parenchyma contour by a graph cutting method to obtain a final contour of a lung nodule; the pulmonary nodules are enhanced, the interference of blood vessels during segmentation is avoided, and the segmentation accuracy of the pulmonary nodules is improved.
The embodiment of the present application further provides a computer device, which can integrate the device for optimizing and segmenting the vessel-dependent pulmonary nodule provided by the embodiment of the present application. Fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present application. Referring to fig. 3, the computer apparatus includes: an input device 43, an output device 44, a memory 42, and one or more processors 41; the memory 42 for storing one or more programs; when executed by the one or more processors 41, cause the one or more processors 41 to implement the method for vessel-dependent lung nodule optimization and segmentation as provided in the embodiments above. Wherein the input device 43, the output device 44, the memory 42 and the processor 41 may be connected by a bus or other means, for example, in fig. 3.
The processor 41 executes various functional applications of the device and data processing, i.e. implements the above-described optimization and segmentation method for vessel-dependent lung nodules, by running software programs, instructions and modules stored in the memory 42.
The computer device provided above can be used to execute the method for vessel-dependent lung nodule optimization and segmentation provided in the above embodiments, and has corresponding functions and advantages.
Embodiments of the present application further provide a storage medium containing computer-executable instructions which, when executed by a computer processor, are configured to perform a method for vessel-dependent lung nodule optimization and segmentation, the method comprising: acquiring a lung image; overlapping lung nodules and blood vessels of the lung image to perform enhancement processing on the lung nodules; extracting a lung parenchyma contour including a lung nodule from the lung image; and automatically segmenting the obtained lung parenchyma contour by a graph segmentation method to obtain a final contour of the lung nodule.
Storage medium-any of various types of memory devices or storage devices. The term "storage medium" is intended to include: mounting media such as CD-ROM, floppy disk, or tape devices; computer device memory or random access memory such as DRAM, DDRRAM, SRAM, EDORAM, Lanbus (Rambus) RAM, etc.; non-volatile memory such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in a first computer apparatus in which the program is executed, or may be located in a different second computer apparatus connected to the first computer apparatus through a network (such as the internet). The second computer device may provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations, such as in different computer devices that are connected by a network. The storage medium may store program instructions (e.g., embodied as a computer program) that are executable by one or more processors.
Of course, the storage medium provided by the embodiments of the present application contains computer executable instructions, and the computer executable instructions are not limited to the method for optimizing and segmenting the vessel-dependent lung nodule as described above, and may also perform related operations in the method for optimizing and segmenting the vessel-dependent lung nodule provided by any of the embodiments of the present application.
The vessel-dependent lung nodule optimizing and segmenting device, the storage medium and the computer device provided in the above embodiments may perform the vessel-dependent lung nodule optimizing and segmenting method provided in any embodiment of the present application, and the technical details not described in detail in the above embodiments may be referred to the vessel-dependent lung nodule optimizing and segmenting method provided in any embodiment of the present application.
The above embodiments are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited thereby, and any insubstantial changes and substitutions made by those skilled in the art based on the present invention are within the protection scope of the present invention.
Claims (10)
1. A method for optimizing and segmenting a vessel-dependent pulmonary nodule is characterized by comprising the following steps: the method comprises the following steps:
acquiring a lung image;
overlapping lung nodules and blood vessels of the lung image to perform enhancement processing on the lung nodules;
extracting a lung parenchyma contour including a lung nodule from the lung image;
and automatically segmenting the obtained lung parenchyma contour by a graph segmentation method to obtain a final contour of the lung nodule.
2. The vessel-dependent pulmonary nodule optimization and segmentation method of claim 1, wherein: the overlapping the pulmonary nodule and the blood vessel of the lung image to perform enhancement processing on the pulmonary nodule comprises:
directly overlapping an original lung nodule of the lung image with an original blood vessel;
blurring and overlapping original pulmonary nodules and blood vessels according to a first coefficient;
and blurring the lung nodule according to a second coefficient and overlapping the lung nodule and the blood vessel according to the second coefficient.
3. The vessel-dependent pulmonary nodule optimization and segmentation method of claim 1, wherein: the extracting of the lung parenchyma contour including the lung nodule on the lung image comprises:
extracting a lung parenchyma contour including lung nodules from the lung image to obtain a lung parenchyma edge curve;
and correcting the lung parenchyma edge curve according to the curvature and the inertia rule of curve extension to obtain the lung parenchyma contour.
4. The vessel-dependent pulmonary nodule optimization and segmentation method of claim 1, wherein: the automatic segmentation of the obtained lung parenchymal contour by the graph cut method to obtain a final contour of a lung nodule includes:
automatically segmenting the obtained lung parenchymal contour to obtain an initial contour of a lung nodule therein, and then initially segmenting the obtained lung nodule by adopting a kernel graph segmentation method to obtain an inner contour of the initial lung nodule;
extracting the central point of the inner contour of the primary pulmonary nodule and the boundary point of the periphery of the inner contour;
and further segmenting the lung nodule by using the extracted central point and boundary point and adopting a graph segmentation method based on a maximum flow algorithm to obtain the final contour of the lung nodule.
5. A method for vessel-dependent lung nodule optimization and segmentation as claimed in claim 3, wherein: extracting a lung parenchyma contour including lung nodules from the lung image to obtain a lung parenchyma edge curve; the method for correcting the pulmonary parenchyma edge curve according to the curvature and the inertia law of curve extension to obtain the pulmonary parenchyma comprises the following steps:
obtaining the curvature K of the pulmonary nodule boundary curve and the pulmonary parenchyma boundary curve intersected with the pulmonary nodule boundary curve according to the following formula, and extracting points with changed curvature directions;
judging the concavity and convexity of each part of the curve according to the curvature radius rho defined by the following formula;
wherein Ix is a first derivative of a coordinate x of the curve, Iy is a first derivative of a coordinate y of the curve, Ixx is a second derivative of a coordinate x of the curve, Iyy is a second derivative of a coordinate y of the curve, and M is an empirical coefficient;
obtaining the inflection point when the lung parenchymal boundary curve and the lung nodule boundary curve are intersected, and removing the curve segment with small curvature;
and correcting the edge of the curve by using the point of the change of the curvature direction and combining with the inertia rule of curve extension by a curve fitting method to obtain the lung parenchyma contour including the lung nodule.
6. The utility model provides a depend on pulmonary nodule to blood vessel and optimize and segmenting device which characterized in that: the method comprises the following steps:
the image acquisition module is used for acquiring lung images;
the enhancement processing module is used for overlapping the pulmonary nodules and the blood vessels of the lung images so as to perform enhancement processing on the pulmonary nodules;
the contour extraction module is used for extracting a lung parenchyma contour containing a lung nodule from the lung image;
and the contour acquisition module is used for automatically segmenting the obtained lung parenchyma contour by a graph segmentation method to obtain a final contour of the lung nodule.
7. The vessel-dependent pulmonary nodule optimizing and segmenting device of claim 6, wherein: the enhancement processing module comprises:
a first processing unit for directly overlaying an original lung nodule of the lung image with an original blood vessel;
the second processing unit is used for blurring and overlapping the original pulmonary nodule and the blood vessel according to a first coefficient;
and the third processing unit is used for blurring the lung nodule according to the second coefficient and then overlapping the lung nodule with the blood vessel after blurring according to the second coefficient.
8. The vessel-dependent pulmonary nodule optimizing and segmenting device of claim 6, wherein: the contour acquisition module includes:
the initial segmentation unit is used for automatically segmenting the obtained lung parenchymal contour to obtain an initial contour of a lung nodule, and then performing initial segmentation on the obtained lung nodule by adopting a kernel graph segmentation method to obtain an inner contour of the initial lung nodule;
the important point extraction unit is used for extracting the central point of the inner contour of the primary pulmonary nodule and the boundary points of the periphery of the inner contour;
and the final segmentation unit is used for further segmenting the lung nodule by using the extracted central point and the extracted boundary point and adopting a graph segmentation method based on a maximum flow algorithm to obtain a final contour of the lung nodule.
9. A computer device, comprising: a memory and one or more processors;
the memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a method of vessel dependent lung nodule optimization and segmentation as recited in any of claims 1-5.
10. A storage medium containing computer executable instructions for performing a method of vessel dependent lung nodule optimization and segmentation as claimed in any one of claims 1 to 5 when executed by a computer processor.
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| US20040184647A1 (en) * | 2002-10-18 | 2004-09-23 | Reeves Anthony P. | System, method and apparatus for small pulmonary nodule computer aided diagnosis from computed tomography scans |
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| US20040184647A1 (en) * | 2002-10-18 | 2004-09-23 | Reeves Anthony P. | System, method and apparatus for small pulmonary nodule computer aided diagnosis from computed tomography scans |
| CN103824295A (en) * | 2014-03-03 | 2014-05-28 | 天津医科大学 | Segmentation method of adhesion vascular pulmonary nodules in lung CT (computed tomography) image |
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