CN108447046B - The detection method and device of lesion, computer readable storage medium - Google Patents
The detection method and device of lesion, computer readable storage medium Download PDFInfo
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- CN108447046B CN108447046B CN201810115277.7A CN201810115277A CN108447046B CN 108447046 B CN108447046 B CN 108447046B CN 201810115277 A CN201810115277 A CN 201810115277A CN 108447046 B CN108447046 B CN 108447046B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H30/00—ICT specially adapted for the handling or processing of medical images
- G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
- G06T2207/10012—Stereo images
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30096—Tumor; Lesion
Abstract
The invention discloses a kind of detection methods of lesion and device, computer readable storage medium.The detection method of the lesion includes: to obtain at least one medical threedimensional images of tissue to be detected in response to lesion detection instruction；Tissue signature's information of the tissue to be detected is obtained, and each medical threedimensional images are cut according to tissue signature's information, obtains at least one area three-dimensional image；Each area three-dimensional image is detected, the corresponding suspected abnormality location information of each area three-dimensional image is obtained；Classify to each area three-dimensional image, obtains the corresponding area classification information of each area three-dimensional image；It according to each area classification information, is counted to same category of all area three-dimensional images are belonged to, obtains the corresponding 3-D image sum of each area classification information；According to each area classification information and each suspected abnormality location information, all suspected abnormalities belonged in same category of all area three-dimensional images in same position are counted, corresponding at least one suspected abnormality sum of each area classification information is obtained；According to the ratio between each suspected abnormality sum and corresponding 3-D image sum, the lesions position information of the tissue to be detected is generated.Using the present invention, the efficiency and accuracy detected to lesion can be improved.
Description
Technical field
The present invention relates to the detection methods and device of field of computer technology more particularly to a kind of lesion, computer-readable
Storage medium.
Background technique
In medical procedure, the position of lesion is determined be medical diagnosis on disease important link.Lesion refers to tissue
Or organ by virulence factor effect and cause the position of lesion, be the part that lesion occurs on body.For example, human lung
Certain a part destroyed by tulase, then this part is exactly pulmonary tuberculosis lesion.
Lesion can be divided into measurable lesion and immeasurability lesion.Wherein, measurable lesion refers at least at one
Can be with the lesion (being recorded as maximum gauge) of precise measurement on radial line, tumour maximum diameter answers >=20mm, spiral shell under the conditions of conventional detection
Tumour maximum diameter answers >=10mm when rotation CT detection.Immeasurability lesion refers to the lesion in addition to measurable lesion, conventional to examine
Tumour maximum diameter answers < 20mm under the conditions of survey, and tumour maximum diameter answers < 10mm when spiral CT detects, such as osteopathy stove spinal meninges lesion, inflammation
The abdominal mass and cystic lesion that property breast cancer lesion, skin or the angioleucitis of lung, imageological examination cannot be confirmed and be evaluated
Deng.
It in the prior art, is usually realized by artificial eye identification to the judgement of immeasurability lesion, effect
Rate is low.In addition, since immeasurability lesion volume is small, it is difficult to identify, and the energy of people is limited, recognition capability is largely
The problem of upper to depend on physical condition and clinical experience, therefore manual identified is easy to appear mistakes and omissions, to subsequent diagnosing and treating
It has adverse effect on, is also possible to will affect the health of patient when serious.
Summary of the invention
The embodiment of the present invention proposes the detection method and device, computer readable storage medium of a kind of lesion, can be improved
The efficiency and accuracy that lesion is detected.
A kind of detection method of lesion provided in an embodiment of the present invention, specifically includes:
In response to lesion detection instruction, at least one medical threedimensional images of tissue to be detected are obtained；
Tissue signature's information of the tissue to be detected is obtained, and according to tissue signature's information to each medicine
3-D image is cut, at least one area three-dimensional image is obtained；
Each area three-dimensional image is detected, the corresponding suspected abnormality of each area three-dimensional image is obtained
Location information；
Classify to each area three-dimensional image, obtains the corresponding area classification of each area three-dimensional image
Information；
According to each area classification information, unite to same category of all area three-dimensional images are belonged to
Meter obtains the corresponding 3-D image sum of each area classification information；
According to each area classification information and each suspected abnormality location information, to belonging to same category of institute
There are all suspected abnormalities in the area three-dimensional image in same position to be counted, obtains each area classification information
Corresponding at least one suspected abnormality sum；
According to the ratio between each suspected abnormality sum and corresponding 3-D image sum, generate described to be detected
The lesions position information of tissue.
Further, at least one medical 3 D figure of tissue to be detected is obtained in response to lesion detection instruction described
As after, the tissue signature's information for obtaining the tissue to be detected, and according to tissue signature's information to each described
Medical threedimensional images are cut, before obtaining at least one area three-dimensional image, further includes:
Denoising is carried out to each medical threedimensional images.
Further, in the tissue signature's information for obtaining the tissue to be detected, and believed according to the tissue signature
Breath cuts each medical threedimensional images, described to each described after obtaining at least one area three-dimensional image
Area three-dimensional image is detected, and before obtaining the corresponding suspected abnormality location information of each area three-dimensional image, is also wrapped
It includes:
Processing is amplified to each area three-dimensional image.
Further, in the tissue signature's information for obtaining the tissue to be detected, and believed according to the tissue signature
Breath cuts each medical threedimensional images, described to each described after obtaining at least one area three-dimensional image
Area three-dimensional image is detected, and before obtaining the corresponding suspected abnormality location information of each area three-dimensional image, is also wrapped
It includes:
Each area three-dimensional image is identified, the corresponding characteristics of image of each area three-dimensional image is obtained
Information；
According to each described image characteristic information, each area three-dimensional image is carried out at corresponding characteristic strengthening
Reason.
Further, each medical threedimensional images are made of at least one medicine two dimensional image.
Further, the tissue to be detected is human body lung tissue；Then at least one described area three-dimensional image includes
Upper lobe of left lung area three-dimensional image, lobe of left lung area three-dimensional image, superior lobe of right lung area three-dimensional image and inferior lobe of right lung region
3-D image.
Correspondingly, it the embodiment of the invention also provides a kind of detection device of lesion, specifically includes:
Medical threedimensional images obtain module, for obtaining tissue to be detected at least one in response to lesion detection instruction
Medical threedimensional images；
Area three-dimensional image obtains module, for obtaining tissue signature's information of the tissue to be detected, and according to described
Tissue signature's information cuts each medical threedimensional images, obtains at least one area three-dimensional image；
Suspected abnormality position detecting module obtains each described for detecting to each area three-dimensional image
The corresponding suspected abnormality location information of area three-dimensional image；
Area three-dimensional image classification module obtains each described for classifying to each area three-dimensional image
The corresponding area classification information of area three-dimensional image；And
True lesion identification obtains module, for according to each area classification information, to belonging to same category of institute
There is the area three-dimensional image to be counted, obtains the corresponding 3-D image sum of each area classification information；According to every
A area classification information and each suspected abnormality location information, to belonging to same category of all area three-dimensionals
All suspected abnormalities in image in same position are counted, obtain each area classification information it is corresponding at least one
Suspected abnormality sum；According to the ratio between each suspected abnormality sum and corresponding 3-D image sum, described in generation
The lesions position information of tissue to be detected.
The embodiment of the invention also provides a kind of computer readable storage mediums, specifically include the computer program of storage；
Wherein, equipment where controlling the computer readable storage medium when the computer program is run executes disease as described above
The detection method of stove.
The implementation of the embodiments of the present invention has the following beneficial effects:
The detection method and device of lesion provided in an embodiment of the present invention, computer readable storage medium, by to be checked
The medical threedimensional images for surveying tissue are detected and are identified automatically, obtain this it is to be detected organize corresponding lesions position information, from
And make the process for being detected and being identified to lesion without artificial interference and participation, it can be improved and lesion is detected
Efficiency and accuracy.
Detailed description of the invention
Fig. 1 is the flow diagram of a preferred embodiment of the detection method of lesion provided by the invention；
Fig. 2 is the structural schematic diagram of a preferred embodiment of the detection device of lesion provided by the invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
As shown in Figure 1, the flow diagram of a preferred embodiment for the detection method of lesion provided by the invention,
It is specific as follows including step S11 to S17:
S11: in response to lesion detection instruction, at least one medical threedimensional images of tissue to be detected are obtained.
It should be noted that the embodiment of the present invention is executed by the system.Wherein, the system can be installed on mobile phone, plate,
System in the terminal devices such as computer, or the system being installed in server.
Above-mentioned lesion detection instruction can be issued by user by clicking some button or executing a certain trigger action,
Sending when can detect specific trigger event at regular intervals or often by system.
System obtains m initial medical threedimensional images after receiving above-mentioned lesion detection instruction, and by using dynamic
State threshold segmentation method carries out image segmentation to every initial medical threedimensional images respectively, to obtain m of tissue to be detected
Medical threedimensional images.Wherein, m >=1.Above-mentioned initial medical threedimensional images can be the CT three-dimensional figure that acquisition is scanned to human body
Picture.
It is highly preferred that each medical threedimensional images are made of at least one medicine two dimensional image.
It should be noted that every above-mentioned medical threedimensional images can be made of n medicine two dimensional images.Wherein, n >=1.
Therefore, in the present embodiment, system actually obtains m*n medicine X-Y schemes when obtaining m medical threedimensional images
Picture.
It is understood that every medicine two dimensional image is the cross sectional image on above-mentioned tissue different location to be detected.
It in another preferred embodiment, further include step before following step S12 after above-mentioned steps S11
S012, specific as follows:
S012: denoising is carried out to each medical threedimensional images.
It should be noted that when in each medical threedimensional images including one or more medicine two dimensional image, it is practical
On be that denoising is carried out respectively to each medicine two dimensional image.
The present embodiment can be further increased and be detected to lesion by carrying out denoising to medical threedimensional images
Accuracy.
S12: tissue signature's information of the tissue to be detected is obtained, and according to tissue signature's information to each described
Medical threedimensional images are cut, at least one area three-dimensional image is obtained.
It should be noted that tissue signature's information as based on cutting is identical, system is to each medical 3 D
The cutting mode of image is identical.When in each medical threedimensional images including one or more medicine two dimensional image, actually
It is to be cut respectively to each medicine two dimensional image.
It is highly preferred that the tissue to be detected is human body lung tissue；Then at least one described area three-dimensional image includes
Upper lobe of left lung area three-dimensional image, lobe of left lung area three-dimensional image, superior lobe of right lung area three-dimensional image and inferior lobe of right lung region
3-D image.
It in yet another preferred embodiment, further include step before following step S13 after above-mentioned steps S12
S023_1, specific as follows:
S023_1: processing is amplified to each area three-dimensional image.
It should be noted that the amplification factor amplified when processing to area three-dimensional image can be by system according to reality
Situation (e.g., screen size, screen resolution, identification code of operator etc.) is adaptively adjusted, can also be by operator
Member presets.When in each medical threedimensional images including one or more medicine two dimensional image, actually to each
Medicine two dimensional image amplifies processing respectively.
It is highly preferred that after above-mentioned steps S12, it, can also be including step S023_2 extremely before following step S13
S023_3, specific as follows:
S023_2: identifying each area three-dimensional image, and it is corresponding to obtain each area three-dimensional image
Image feature information.
S023_3: according to each described image characteristic information, corresponding feature is carried out to each area three-dimensional image
Intensive treatment.
It should be noted that features described above intensive treatment can be image enhancement processing, or at image binaryzation
Reason, can also be other feature intensive treatment.Wherein, image enhancement processing can pass through greyscale transformation algorithm, histogram equalization
Change algorithm etc. to realize；Image binaryzation processing can be by preset threshold value, and by each of above-mentioned zone 3-D image
The gray value that gray value is greater than the pixel of the threshold value is set as 255, and each gray value is less than or equal to the picture of the threshold value
The gray value of vegetarian refreshments is set as 0 to realize.When in each medical threedimensional images including one or more medicine two dimensional image,
Characteristic strengthening processing is actually carried out respectively to each medicine two dimensional image.
It should be further noted that it is above-mentioned to area three-dimensional image amplify processing and characteristic strengthening processing can tie
It closes and uses, can also be used separately, can be combined with the use of other image processing methods.Most preferably, above-mentioned steps S023_1
Execution sequence to S023_3 is S023_1 → S023_2 → S023_3.
The present embodiment can be further increased by amplifying processing, characteristic strengthening processing etc. to medical threedimensional images
The accuracy that lesion is detected.
S13: detecting each area three-dimensional image, and it is corresponding doubtful to obtain each area three-dimensional image
Lesions position information.
It should be noted that system is after obtaining each region 3-D image, to each region 3-D image respectively into
Row detection, to identify the suspected abnormality in each area three-dimensional image, and obtains the corresponding doubtful disease of each suspected abnormality
Stove location information.
S14: classifying to each area three-dimensional image, obtains the corresponding region of each area three-dimensional image
Classification information.
It should be noted that when above-mentioned tissue to be detected is human body lung tissue, it can be under upper lobe of left lung, left lung
Leaf, superior lobe of right lung and inferior lobe of right lung are that standard classifies to each region 3-D image.
S15: it according to each area classification information, is carried out to same category of all area three-dimensional images are belonged to
Statistics obtains the corresponding 3-D image sum of each area classification information.
It should be noted that statistics belongs to upper lobe of left lung, a left side respectively when above-mentioned tissue to be detected is human body lung tissue
The number of lobi inferior, superior lobe of right lung and the area three-dimensional of inferior lobe of right lung image, to obtain the corresponding three-dimensional of upper lobe of left lung respectively
The corresponding 3-D image sum of total number of images, lobe of left lung, the corresponding 3-D image sum of superior lobe of right lung and inferior lobe of right lung are corresponding
3-D image sum.
S16: according to each area classification information and each suspected abnormality location information, to belonging to same category
All area three-dimensional images in all suspected abnormalities in same position counted, obtain each area classification
Corresponding at least one suspected abnormality sum of information.
It should be noted that when tissue to be detected is human body lung tissue, to the area three-dimensional figure for belonging to upper lobe of left lung
All suspected abnormalities as in same position are counted respectively, are doubted corresponding to each lesions position information to obtain
Like lesion sum；And so on, it is total to belong to each suspected abnormality corresponding to the area three-dimensional image of lobe of left lung for acquisition respectively
Number belongs to each suspected abnormality sum corresponding to the area three-dimensional image of superior lobe of right lung and belongs to the area three-dimensional of inferior lobe of right lung
Each suspected abnormality sum corresponding to image.
S17: according to the ratio between each suspected abnormality sum and corresponding 3-D image sum, generate it is described to
Detect the lesions position information of tissue.
It should be noted that system after obtaining each suspected abnormality sum and each 3-D image sum, calculates each
Ratio between a suspected abnormality sum and corresponding 3-D image sum determines that the suspected abnormality is total if the ratio is greater than α
The corresponding suspected abnormality of number is true lesion, and generates corresponding lesions position information, if the ratio is less than or equal to α,
Then determine that suspected abnormality corresponding to suspected abnormality sum is not true lesion, therefore ignores the suspected abnormality.And so on,
Obtain the lesions position information of all true lesions in above-mentioned tissue to be detected.
The detection method of lesion provided by the embodiment of the present invention is carried out by the medical threedimensional images to tissue to be detected
Automatic detection and identification obtain the corresponding lesions position information of the tissue to be detected, so that being detected and being known to lesion
Other process is not necessarily to artificial interference and participation, can be improved the efficiency and accuracy detected to lesion.In addition, by adopting
The case where tissue to be detected is described, can really reflect tissue to be detected with medical threedimensional images and details, so as to
Enough further increase the accuracy detected to lesion；Classified and distinguished by each region to medical threedimensional images
Processing so that can have specific aim and fining to the detection of lesion, therefore can also further increase to lesion into
The accuracy of row detection.
Correspondingly, the present invention also provides a kind of detection device of lesion, it can be realized the inspection of the lesion in above-described embodiment
All processes of survey method.
As shown in Fig. 2, the structural schematic diagram of a preferred embodiment for the detection device of lesion provided by the invention,
It is specific as follows:
Medical threedimensional images obtain module 21, for obtaining at least the one of tissue to be detected in response to lesion detection instruction
A medical threedimensional images；
Area three-dimensional image obtains module 22, for obtaining tissue signature's information of the tissue to be detected, and according to institute
It states tissue signature's information to cut each medical threedimensional images, obtains at least one area three-dimensional image；
Suspected abnormality position detecting module 23 obtains each institute for detecting to each area three-dimensional image
State the corresponding suspected abnormality location information of area three-dimensional image；
Area three-dimensional image classification module 24 obtains each institute for classifying to each area three-dimensional image
State the corresponding area classification information of area three-dimensional image；And
True lesion identification obtains module 25, comprising: 3-D image sum statistic unit, for according to each region
Classification information is counted to same category of all area three-dimensional images are belonged to, and obtains each area classification letter
Cease corresponding 3-D image sum；Suspected abnormality sum statistic unit, for according to each area classification information and each
The suspected abnormality location information, it is all doubtful in same position in same category of all area three-dimensional images to belonging to
It is counted like lesion, obtains corresponding at least one suspected abnormality sum of each area classification information；Lesions position letter
Generation unit is ceased, for generating institute according to the ratio between each suspected abnormality sum and corresponding 3-D image sum
State the lesions position information of tissue to be detected.
Further, the detection device of the lesion, further includes:
Image denoising processing module, for carrying out denoising to each medical threedimensional images.
Further, the detection device of the lesion, further includes:
Image enhanced processing module, for amplifying processing to each area three-dimensional image.
Further, the detection device of the lesion, further includes:
Characteristics of image identification module obtains each region for identifying to each area three-dimensional image
The corresponding image feature information of 3-D image；And
Image intensification processing module is used for according to each described image characteristic information, to each area three-dimensional image
Carry out corresponding characteristic strengthening processing.
Further, each medical threedimensional images are made of at least one medicine two dimensional image.
Further, the tissue to be detected is human body lung tissue；Then at least one described area three-dimensional image includes
Upper lobe of left lung area three-dimensional image, lobe of left lung area three-dimensional image, superior lobe of right lung area three-dimensional image and inferior lobe of right lung region
3-D image.
The detection device of lesion provided in an embodiment of the present invention is carried out certainly by the medical threedimensional images to tissue to be detected
Dynamic detection and identification obtain the corresponding lesions position information of the tissue to be detected, so that being detected and being identified to lesion
Process be not necessarily to artificial interference and participation, can be improved the efficiency and accuracy detected to lesion.In addition, by using
The case where medical threedimensional images are described to tissue to be detected, can really reflect tissue to be detected and details, so as to
Further increase the accuracy detected to lesion；Classification is carried out by each region to medical threedimensional images and is located respectively
Reason so that can have specific aim and fining to the detection of lesion, therefore can also be further increased and be carried out to lesion
The accuracy of detection.
The present invention also provides a kind of computer readable storage mediums, specifically include the computer program of storage, wherein
Equipment executes described in any embodiment as above the computer program controls the computer readable storage medium when running where
Lesion detection method.
It should be noted that the present invention realizes all or part of the process in above-described embodiment method, meter can also be passed through
Calculation machine program is completed to instruct relevant hardware, and the computer program can be stored in a computer readable storage medium
In, the computer program is when being executed by processor, it can be achieved that the step of above-mentioned each embodiment of the method.Wherein, the calculating
Machine program includes computer program code, and the computer program code can be source code form, object identification code form, can hold
Style of writing part or certain intermediate forms etc..The computer-readable medium may include: that can carry the computer program code
Any entity or device, recording medium, USB flash disk, mobile hard disk, magnetic disk, CD, computer storage, read-only memory (ROM,
Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunications letter
Number and software distribution medium etc..It should be further noted that the content that the computer-readable medium includes can basis
Legislation and the requirement of patent practice carry out increase and decrease appropriate in jurisdiction, such as in certain jurisdictions, according to legislation
And patent practice, computer-readable medium do not include electric carrier signal and telecommunication signal.
Computer readable storage medium provided in an embodiment of the present invention, by the medical threedimensional images to tissue to be detected into
The automatic detection of row and identification obtain the corresponding lesions position information of the tissue to be detected so as to lesion carry out detection and
The process of identification is not necessarily to artificial interference and participation, can be improved the efficiency and accuracy detected to lesion.In addition, passing through
The case where tissue to be detected is described, can really reflect tissue to be detected using medical threedimensional images and details, thus
The accuracy detected to lesion can be further increased；Classified and divided by each region to medical threedimensional images
Other places reason, so that can have specific aim and fining to the detection of lesion, therefore can also further increase to lesion
The accuracy detected.
The above is a preferred embodiment of the present invention, it is noted that for those skilled in the art
For, without departing from the principle of the present invention, it can also make several improvements and retouch, these improvements and modifications are also considered as
Protection scope of the present invention.
Claims (8)
1. a kind of detection method of lesion characterized by comprising
In response to lesion detection instruction, at least one medical threedimensional images of tissue to be detected are obtained；
Tissue signature's information of the tissue to be detected is obtained, and according to tissue signature's information to each medical 3 D
Image is cut, at least one area three-dimensional image is obtained；
Each area three-dimensional image is detected, the corresponding suspected abnormality position of each area three-dimensional image is obtained
Information；
Classify to each area three-dimensional image, obtains the corresponding area classification letter of each area three-dimensional image
Breath；
According to each area classification information, counts, obtain to same category of all area three-dimensional images are belonged to
Obtain the corresponding 3-D image sum of each area classification information；
According to each area classification information and each suspected abnormality location information, to belonging to same category of all institutes
It states all suspected abnormalities in area three-dimensional image in same position to be counted, it is corresponding to obtain each area classification information
At least one suspected abnormality sum；
According to the ratio between each suspected abnormality sum and corresponding 3-D image sum, the tissue to be detected is generated
Lesions position information.
2. the detection method of lesion as described in claim 1, which is characterized in that described in response to lesion detection instruction, obtain
After at least one medical threedimensional images for obtaining tissue to be detected, the tissue signature's information for obtaining the tissue to be detected,
And each medical threedimensional images are cut according to tissue signature's information, obtain at least one area three-dimensional image
Before, further includes:
Denoising is carried out to each medical threedimensional images.
3. the detection method of lesion as described in claim 1, which is characterized in that in the group for obtaining the tissue to be detected
Characteristic information is knitted, and each medical threedimensional images are cut according to tissue signature's information, obtains at least one
It is described that each area three-dimensional image is detected after area three-dimensional image, obtain each area three-dimensional image
Before corresponding suspected abnormality location information, further includes:
Processing is amplified to each area three-dimensional image.
4. the detection method of lesion as described in claim 1, which is characterized in that in the group for obtaining the tissue to be detected
Characteristic information is knitted, and each medical threedimensional images are cut according to tissue signature's information, obtains at least one
It is described that each area three-dimensional image is detected after area three-dimensional image, obtain each area three-dimensional image
Before corresponding suspected abnormality location information, further includes:
Each area three-dimensional image is identified, the corresponding characteristics of image letter of each area three-dimensional image is obtained
Breath；
According to each described image characteristic information, corresponding characteristic strengthening processing is carried out to each area three-dimensional image.
5. the detection method of lesion according to any one of claims 1 to 4, which is characterized in that each medical 3 D
Image is made of at least one medicine two dimensional image.
6. the detection method of lesion according to any one of claims 1 to 4, which is characterized in that it is described it is to be detected tissue be
Human lung's tissue；Then at least one described area three-dimensional image includes upper lobe of left lung area three-dimensional image, lobe of left lung region
3-D image, superior lobe of right lung area three-dimensional image and inferior lobe of right lung area three-dimensional image.
7. a kind of detection device of lesion characterized by comprising
Medical threedimensional images obtain module, for obtaining at least one medicine of tissue to be detected in response to lesion detection instruction
3-D image；
Area three-dimensional image obtains module, for obtaining tissue signature's information of the tissue to be detected, and according to the tissue
Characteristic information cuts each medical threedimensional images, obtains at least one area three-dimensional image；
Suspected abnormality position detecting module obtains each region for detecting to each area three-dimensional image
The corresponding suspected abnormality location information of 3-D image；
Area three-dimensional image classification module obtains each region for classifying to each area three-dimensional image
The corresponding area classification information of 3-D image；And
True lesion identification obtains module, for according to each area classification information, to belonging to same category of all institutes
It states area three-dimensional image to be counted, obtains the corresponding 3-D image sum of each area classification information；According to each institute
Area classification information and each suspected abnormality location information are stated, to belonging to same category of all area three-dimensional images
All suspected abnormalities in middle same position are counted, and obtaining each area classification information, corresponding at least one is doubtful
Lesion sum；According to the ratio between each suspected abnormality sum and corresponding 3-D image sum, generate described to be checked
Survey the lesions position information of tissue.
8. a kind of computer readable storage medium, which is characterized in that the computer readable storage medium includes the calculating of storage
Machine program；Wherein, equipment is executed as weighed where controlling the computer readable storage medium when the computer program is run
Benefit require any one of 1 to 6 described in lesion detection method.
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|---|---|---|---|---|
| CA3067824A1 (en) | 2017-06-26 | 2019-01-03 | The Research Foundation For The State University Of New York | System, method, and computer-accessible medium for virtual pancreatography |
| CN109255782A (en) * | 2018-09-03 | 2019-01-22 | 图兮深维医疗科技（苏州）有限公司 | A kind of processing method, device, equipment and the storage medium of Lung neoplasm image |
| CN111292301A (en) * | 2018-12-07 | 2020-06-16 | 北京市商汤科技开发有限公司 | Focus detection method, device, equipment and storage medium |
| CN110648311B (en) * | 2019-09-03 | 2023-04-18 | 南开大学 | Acne image focus segmentation and counting network model based on multitask learning |
| WO2021081839A1 (en) * | 2019-10-30 | 2021-05-06 | 未艾医疗技术(深圳)有限公司 | Vrds 4d-based method for analysis of condition of patient, and related products |
| CN111339899B (en) * | 2020-02-21 | 2020-12-22 | 腾讯科技（深圳）有限公司 | Catheter feature acquisition method, device, equipment, medium and intelligent microscope |
| CN111539944B (en) * | 2020-04-28 | 2024-04-09 | 讯飞医疗科技股份有限公司 | Method, device, electronic equipment and storage medium for acquiring statistical attribute of lung focus |
| CN111667468A (en) * | 2020-05-28 | 2020-09-15 | 平安科技（深圳）有限公司 | OCT image focus detection method, device and medium based on neural network |
| CN115578554B (en) * | 2021-06-21 | 2024-02-02 | 数坤(上海)医疗科技有限公司 | Vascular focus identification method, device, electronic equipment and readable storage medium |
| CN113344921B (en) * | 2021-08-04 | 2022-08-02 | 浙江太美医疗科技股份有限公司 | Image detection method and device, computer readable storage medium and electronic equipment |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106504232A (en) * | 2016-10-14 | 2017-03-15 | 北京网医智捷科技有限公司 | A kind of pulmonary nodule automatic testing method based on 3D convolutional neural networks |
| CN106530296A (en) * | 2016-11-07 | 2017-03-22 | 首都医科大学 | Lung detection method and device based on PET/CT image features |
| CN106780728A (en) * | 2017-02-28 | 2017-05-31 | 成都金盘电子科大多媒体技术有限公司 | A kind of single organ method for splitting and system based on medical image |
| CN106887039A (en) * | 2017-02-28 | 2017-06-23 | 成都金盘电子科大多媒体技术有限公司 | The three-D imaging method and system of a kind of organ and focus based on medical image |
| CN106940816A (en) * | 2017-03-22 | 2017-07-11 | 杭州健培科技有限公司 | Connect the CT image Lung neoplasm detecting systems of convolutional neural networks entirely based on 3D |
| CN107480673A (en) * | 2017-06-30 | 2017-12-15 | 上海联影医疗科技有限公司 | Determine the method, apparatus and image editing system of area-of-interest in medical image |
| CN107485446A (en) * | 2017-09-07 | 2017-12-19 | 安徽紫薇帝星数字科技有限公司 | It is a kind of can fast positioning belly operation guided positioning system |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101727537A (en) * | 2009-11-16 | 2010-06-09 | 杭州电子科技大学 | Computer determining method of mammary gland CR image based on double visual angle information fusion |
| CN103366183B (en) * | 2013-07-19 | 2017-05-10 | 南方医科大学 | Nonparametric automatic detection method of focal niduses |
| CN105488781B (en) * | 2015-06-01 | 2019-04-30 | 深圳市第二人民医院 | A kind of dividing method based on CT images liver neoplasm lesion |
| CN106780460B (en) * | 2016-12-13 | 2019-11-08 | 杭州健培科技有限公司 | A kind of Lung neoplasm automatic checkout system for chest CT images |
| CN107563983B (en) * | 2017-09-28 | 2020-09-01 | 上海联影医疗科技有限公司 | Image processing method and medical imaging device |
-
2018
- 2018-02-05 CN CN201810115277.7A patent/CN108447046B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106504232A (en) * | 2016-10-14 | 2017-03-15 | 北京网医智捷科技有限公司 | A kind of pulmonary nodule automatic testing method based on 3D convolutional neural networks |
| CN106530296A (en) * | 2016-11-07 | 2017-03-22 | 首都医科大学 | Lung detection method and device based on PET/CT image features |
| CN106780728A (en) * | 2017-02-28 | 2017-05-31 | 成都金盘电子科大多媒体技术有限公司 | A kind of single organ method for splitting and system based on medical image |
| CN106887039A (en) * | 2017-02-28 | 2017-06-23 | 成都金盘电子科大多媒体技术有限公司 | The three-D imaging method and system of a kind of organ and focus based on medical image |
| CN106940816A (en) * | 2017-03-22 | 2017-07-11 | 杭州健培科技有限公司 | Connect the CT image Lung neoplasm detecting systems of convolutional neural networks entirely based on 3D |
| CN107480673A (en) * | 2017-06-30 | 2017-12-15 | 上海联影医疗科技有限公司 | Determine the method, apparatus and image editing system of area-of-interest in medical image |
| CN107485446A (en) * | 2017-09-07 | 2017-12-19 | 安徽紫薇帝星数字科技有限公司 | It is a kind of can fast positioning belly operation guided positioning system |
Non-Patent Citations (2)
| Title |
|---|
| Diabetic Retinopathy Lesion Detection using Region-based Approach;Mohd Fazli Hashim 等;《2014 8th Malaysian Software Engineering Conference(MySEC)》;20140924;306-310 |
| Lesion Detection in Lung Regions that are Segmented Using Spatial Relations;Donia Ben Hassen 等;《2012 International Conference on Information Technology and e-Services》;20120326;1-4 |
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