CN103955610B - Medical image computer-aided analysis method - Google Patents
Medical image computer-aided analysis method Download PDFInfo
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Abstract
The invention provides a medical image computer-aided analysis method. The medical image computer-aided analysis method comprises the following steps: carrying out primary scanning on a medical image through an infrared light source and an infrared imager, and carrying out image division operation on volume data of the image through the primary scanning so as to obtain primary image data; carrying out secondary scanning on the medical image through a visible light source and a photosensitive element; carrying out image division operation on volume data of the image through the secondary scanning so as to obtain secondary image data; binding the primary image data with the secondary image data, and contrasting with an existing pathological sample in a pathological database, wherein the contrast process specifically comprises mutually contrasting the primary image data and mutually contrasting the secondary image data.
Description
Technical field
The present invention relates to medical instruments field, more particularly to a kind of medical image computer-aided analysis method.
Background technology
Computer aided detection is using advanced computer software and hardware analysis and process digital radiation image, to find simultaneously
Detection characteristics of lesion, its result is referred to as " second suggestion " for diagnostician, helps radiation technician to improve Lesion Detection rate,
It is referred to as " second eyes " of radiologist, it can improve diagnostic accuracy and improve the repeatability of diagnosis, shortens and read
The piece time, improve operating efficiency.
Conventional computer aided detection method is method for feature analysis and temporal subtraction.
1st, method for feature analysis：Certain variation characteristic is extracted by post processing of image, is exported again with after corresponding pattern match
The method of diagnostic result.Method for feature analysis is divided into selection region of interest, extracts the steps such as feature, pattern match and medical diagnosis on disease.Choosing
After taking region of interest, using various post processing of image means to selected extracting section feature, according to characteristics of lesion to it is related
Disease performance carries out pattern match and medical diagnosis on disease.
2nd, temporal subtraction：Two width images before and after to morbidity are mutually reduced computing, in removing two width images
Identical part, retains different piece, the substantially prominent abnormal change under homogeneous background.
Method for feature analysis is to pre-build pathological model, the pathological regions of target image is extracted, by characteristics of lesion and pathology
Model is matched.Because pathological model is fixed model, or the model library being made up of multiple fixed models, and pathology is special
Levy and vary, diagnosed according to matching degree merely, situation about judging by accident can occur unavoidably.
And temporal subtraction is mutually to be reduced computing for two width images before and after morbidity, rather than to single width figure
As being analyzed comparison, if patient only has single image, the method failure.
Above two method, is required for carrying out image procossing and data processing, specifically includes three below step：
(1) intensive treatment of focus shade.At present, mainly being filtered using spatial filtering and spatial frequency makes focus contrast
Increase, the part contrast beyond focus is reduced, so that focus is easily detected.
(2) extraction of focus candidate shade.Focus shade is detected according to threshold values process and the characteristic quantity of focus is measured, is judged
Whether it is selected focus.The characteristic quantity yardstick of shade, such as effective diameter, the circle about its characteristic of switch amount are determined first
Degree, degree of irregularity etc., using circularity is big, degree of irregularity is little shade as the scale for extracting inactivity of yang-qi shadow, then through multiple valve
Value process, obtains black and white contrast image.
(3) focus candidate's shaded region (reduction false positive) is reduced.Select to reflect that focus is cloudy using image recognition technology
The characteristic quantity of shadow, according to characteristic quantity focus and false positive shade are recognized, characteristic quantity is more, false positive number occurs fewer.As tied
10 features above amounts such as effective diameter, circularity, density, then carry out diagnostic classification to characteristic quantity used in section shadow, commonly use
Method artificial neural network method, fuzzy clustering algorithm, linear classification etc..
In above-mentioned steps, the intensive treatment step of focus shade is most important, is following two diagnostic process steps
Basis, if deviation occurs in the step, deviation can cause chain deviation in follow-up two steps, and this deviation is even put
Greatly, mistaken diagnosis is caused.
Therefore, how to set up a complete pathology storehouse, store pathological model as much as possible, improve the strong of focus shade
Change treatment effect, be current problem demanding prompt solution.
The content of the invention
The present invention proposes a kind of medical image computer-aided analysis method, by setting up and constantly updates pathological data
Storehouse, medical image to be diagnosed mutually is compared with existing pathology sample, improves the correctness of auxiliary diagnosis, solves existing meter
Calculation machine aided diagnosis method pathological model is single, focus shade has error.
The technical scheme is that what is be achieved in that：
A kind of medical image computer-aided analysis method, comprises the following steps：
Step (1), carries out single pass, to single pass figure by infrared light supply and infrared thermography to medical image
The volume data of picture carries out image splitting operation, obtains a view data；
Described image splitting operation specifically includes following steps：
Step (a), creates a division region child node set setNodes, and one with the number of volume data formed objects
According to region NodeArray, the data area NodeArray is used to depositing the address of each voxel place child node, and by number
Sky is set to according to the data content of region NodeArray；
Step (b), travels through successively each tissue points Voxel (xi, yi, zi) in volume data, after traversal is completed, jumps
Go to step (g)；
Step (c), judges whether Voxel (xi, yi, zi) had been expanded；Determination methods are corresponding to see its
Whether the value in NodeArray (xi, yi, zi) is empty；If propagating through, then return to step (b)；
Step (d), creates a sub-regions node Nodej, and the original position of the node is Voxel (xi, yi, zi)；
Step (e), is extended respectively to the positive direction of tri- reference axis of X, Y, Z, and judge the voxel of new extension with
Whether Voxel (xi, yi, zi) has feature consistency；When inconsistent voxel occurs in a direction, then stop the direction
Extension；
Step (f), can obtain end position Voxel (xi+m, yi+n, the zi+ of node Nodej after the completion of the step (e)
k)；The value of NodeArray (xs, ys, zs) is set to corresponding to the voxel Voxel (xs, ys, zs) that first Nodej is included
The address of Nodej, then be added in child node set setNodes；
Step (g), in volume data has been traveled through after all voxels, by the data in the NodeArray of ergodic data region,
Generate the neighbouring relations of sub-district domain node；
Step (2), rescan is carried out by visible light source and photo-sensitive cell to the medical image；To rescan
The volume data of image carries out described image splitting operation, obtains secondary image data；
Step (3), a view data and secondary image data is bound, with existing pathology in pathological data storehouse
Sample is mutually compared, and comparison process is specially a view data and compares mutually, and secondary image data is compared mutually；
Step (4), a view data comparison result r1 is multiplied by coefficient of first order k1, and secondary image data comparison result r2 takes advantage of
With quadratic coefficients k2, k1+k2=1, and 0.2≤k1≤0.3,0.7≤k1≤0.8；If r1*k1+r2*k2 >=0.5, judge new
Pathology sample is pathology sample, and pathological data storehouse described in typing；If 0.05≤r1*k1+r2*k2 ＜ 0.5, new pathology is judged
Sample is suspected lesion sample；If r1*k1+r2*k2 ＜ 0.05, judge pathology sample for normal sample.
Alternatively, it is another positioned at the medical image by the inswept medical image of column infrared light supply in the step (1)
The infrared thermography of side carries out induction image forming to the infrared signal after the filtration of medical image film, is once swept
Tracing picture.
Alternatively, in the step (2), by the inswept medical image of column visible light source, positioned at the medical image
The photo-sensitive cell of opposite side carries out induction image forming to the visible light signal after the filtration of medical image film, obtains secondary sweeping
Tracing picture.
Alternatively, in the step (3), in the step of existing pathology sample is mutually compared in pathological data storehouse, also wrap
The step of decompressing to existing pathology sample is included, is decompressed as a view data and secondary image data.
The invention has the beneficial effects as follows：
(1) accurately, focal size measure error is little for lesion localization；
(2) and a set of complete pathological data storehouse is set up, and constantly data storehouse is updated, improve the standard of diagnosis
True property.
Description of the drawings
In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
The accompanying drawing to be used needed for having technology description is briefly described, it should be apparent that, drawings in the following description are only this
Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, can be with
Other accompanying drawings are obtained according to these accompanying drawings.
Fig. 1 is the flow chart of medical image computer aided diagnosing method of the present invention；
Fig. 2 is the flow chart of image splitting operation process in medical image computer aided diagnosing method of the present invention.
Specific embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than the embodiment of whole.It is based on
Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment, belongs to the scope of protection of the invention.
Computer-assisted analysis has become reference frame important during diagnosis, but existing area of computer aided
Diagnosis is all that image procossing and data processing, meeting after medical image film printing are carried out while medical image shooting process
Have and reported with piece, doctor makes final judgement according to report and the observation of oneself.If patient is to other hospital admissions, doctor
Can only diagnose by rule of thumb, or patient needs to carry out a filming image again, is all one to the body or economy of patient
Plant loss.
The medical image computer-aided analysis method of the present invention, is entered by infrared light and visible ray to medical image film
Row twice sweep, then carries out image splitting operation to scan image, obtain twice sweep data respectively with pathology storehouse in disease
Reason sample is mutually compared, and comparison result is multiplied by weight and obtains last diagnostic result.Below in conjunction with the accompanying drawings, the method for the present invention is carried out
Elaborate.
As shown in figure 1, the medical image computer-aided analysis method of the present invention, comprises the following steps：
Step (1), carries out single pass, to single pass figure by infrared light supply and infrared thermography to medical image
The volume data of picture carries out image splitting operation, obtains a view data.Quilt during the infrared light image film of infrared light supply
Filter, infrared light is different with the transmitance of other parts in focus, therefore the imaging on infrared thermography is different.Once sweep
Retouching can strengthen the positioning to lesions position, and to focal size preliminary surveying is carried out.
As shown in Fig. 2 described image splitting operation specifically includes following steps：
Step (a), creates a division region child node set setNodes, and one with the number of volume data formed objects
According to region NodeArray, the NodeArray is used to deposit the address of each voxel place child node, and by NodeArray's
Data content is set to sky；
Step (b), travels through successively each tissue points Voxel (xi, yi, zi) in volume data, after traversal is completed, jumps
Go to step (g)；
Step (c), judges whether Voxel (xi, yi, zi) had been expanded；Determination methods are corresponding to see its
Whether the value in NodeArray (xi, yi, zi) is empty；If propagating through, then return to step (b)；
Step (d), creates a sub-regions node Nodej, and the original position of the node is Voxel (xi, yi, zi)；
Step (e), is extended respectively to the positive direction of tri- reference axis of X, Y, Z, and judge the voxel of new extension with
Whether Voxel (xi, yi, zi) has feature consistency；When inconsistent voxel occurs in a direction, then stop the direction
Extension；
Step (f), can obtain end position Voxel (xi+m, yi+n, the zi+ of node Nodej after the completion of the step (e)
k)；The value of NodeArray (xs, ys, zs) is set to corresponding to the voxel Voxel (xs, ys, zs) that first Nodej is included
The address of Nodej, then be added in child node set setNodes；
Step (g), in volume data has been traveled through after all voxels, by the data in traversal NodeArray, generates sub-district
The neighbouring relations of domain node.
The image splitting operation process of the present invention is only by traveling through twice volume data, it is possible to enter line splitting to volume data,
And the region after dividing is not in the situation of Hypersegmentation.
Flow chart shown in Fig. 1 is returned to, the method for the present invention also includes：
Step (2), rescan is carried out by visible light source and photo-sensitive cell to the medical image；To rescan
The volume data of image carries out described image splitting operation, obtains secondary image data.Shown in image splitting operation process and Fig. 2
Process is identical, repeats no more here.Filtered during visible light-transmissive image film, it is seen that sharpening of the light at focus edge is imitated
It is really good, it is possible to increase focus contrast, image flatness is improved, obtain accurate focal size.
Step (3), a view data and secondary image data is bound, with existing pathology in pathological data storehouse
Sample is mutually compared, and comparison process is specially a view data and compares mutually, and secondary image data is compared mutually.
Step (4), a view data comparison result r1 is multiplied by coefficient of first order k1, and secondary image data comparison result r2 takes advantage of
With quadratic coefficients k2, k1+k2=1, and k1≤0.3, k2 >=0.7；If r1*k1+r2*k2 >=0.5, judge that new pathology sample is
Pathology sample, and typing pathological data storehouse；If 0.05≤r1*k1+r2*k2 ＜ 0.5, judge new pathology sample for suspected lesion
Sample；If r1*k1+r2*k2 ＜ 0.05, judge pathology sample for normal sample.
Preferably, it is another positioned at the medical image by the inswept medical image of column infrared light supply in the step (1)
The infrared thermography of side carries out induction image forming to the infrared signal after the filtration of medical image film, is once swept
Tracing picture.
Preferably, in the step (2), by the inswept medical image of column visible light source, positioned at the medical image
The photo-sensitive cell of opposite side carries out induction image forming to the visible light signal after the filtration of medical image film, obtains secondary sweeping
Tracing picture.
Preferably, in the step (3), in the step of existing pathology sample is mutually compared in pathological data storehouse, also wrap
The step of decompressing to existing pathology sample is included, is decompressed as a view data and secondary image data.
The medical image computer-aided analysis method of the present invention, accurately, focal size measure error is little for lesion localization；And
And a set of complete pathological data storehouse is set up, and constantly data storehouse is updated, improve the accuracy of diagnosis.
Presently preferred embodiments of the present invention is the foregoing is only, not to limit the present invention, all essences in the present invention
Within god and principle, any modification, equivalent substitution and improvements made etc. should be included within the scope of the present invention.
Claims (3)
1. a kind of medical image computer-aided analysis method, it is characterised in that comprise the following steps：
Step (1), carries out single pass, to single pass image by infrared light supply and infrared thermography to medical image
Volume data carries out image splitting operation, obtains a view data；
Described image splitting operation specifically includes following steps：
Step (a), creates a division region child node set setNodes, and one with the data field of volume data formed objects
Domain NodeArray, the data area NodeArray are used to depositing the address of each voxel place child node, and by data field
The data content of domain NodeArray is set to sky；
Step (b), travels through successively each tissue points Voxel (xi, yi, zi) in volume data, after traversal is completed, jumps to
Step (g)；
Step (c), judges whether Voxel (xi, yi, zi) had been expanded；Determination methods are to see its correspondence NodeArray
Whether the value in (xi, yi, zi) is empty；If propagating through, then return to step (b)；
Step (d), creates a sub-regions node Nodej, and the original position of the node is Voxel (xi, yi, zi)；
Step (e), is extended respectively to the positive direction of tri- reference axis of X, Y, Z, and judges the voxel and Voxel of new extension
Whether (xi, yi, zi) has feature consistency；When inconsistent voxel occurs in a direction, then stop the extension of the direction；
Step (f), can obtain the end position Voxel (xi+m, yi+n, zi+k) of node Nodej after the completion of the step (e)；
The value of NodeArray (xs, ys, zs) is set to Nodej corresponding to the voxel Voxel (xs, ys, zs) that first Nodej is included
Address, then be added in child node set setNodes；
Step (g), in volume data has been traveled through after all voxels, by the data in the NodeArray of ergodic data region, generates
The neighbouring relations of sub-district domain node；
Step (2), rescan is carried out by visible light source and photo-sensitive cell to the medical image；To rescan image
Volume data carry out described image splitting operation, obtain secondary image data；
Step (3), a view data and secondary image data is bound, with existing pathology sample in pathological data storehouse
Mutually compare, comparison process is specially a view data and compares mutually, and secondary image data is compared mutually；In pathological data storehouse
In the step of existing pathology sample is mutually compared, also include the step of decompressing to existing pathology sample, decompress once to scheme
As data and secondary image data；
Step (4), a view data comparison result r1 is multiplied by coefficient of first order k1, and secondary image data comparison result r2 is multiplied by two
Ordered coefficients k2, k1+k2=1, and 0.2≤k1≤0.3,0.7≤k1≤0.8；If r1*k1+r2*k2 >=0.5, new pathology is judged
Sample is pathology sample, and pathological data storehouse described in typing；If 0.05≤r1*k1+r2*k2 ＜ 0.5, new pathology sample is judged
For suspected lesion sample；If r1*k1+r2*k2 ＜ 0.05, judge pathology sample for normal sample.
2. medical image computer-aided analysis method as claimed in claim 1, it is characterised in that in the step (1), leads to
The inswept medical image of column infrared light supply is crossed, the infrared thermography positioned at the medical image opposite side is to through medical image
Infrared signal after film is filtered carries out induction image forming, obtains single pass image.
3. medical image computer-aided analysis method as claimed in claim 1, it is characterised in that in the step (2), leads to
The inswept medical image of column visible light source is crossed, the photo-sensitive cell positioned at the medical image opposite side is to through medical image glue
Visible light signal after piece is filtered carries out induction image forming, obtains rescan image.
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| CN103608839A (en) * | 2011-03-28 | 2014-02-26 | 皇家飞利浦有限公司 | Contrast-dependent resolution image |
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| CN103282790A (en) * | 2010-12-21 | 2013-09-04 | 皇家飞利浦电子股份有限公司 | Fast dual contrast mr imaging |
| CN103608839A (en) * | 2011-03-28 | 2014-02-26 | 皇家飞利浦有限公司 | Contrast-dependent resolution image |
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| 基于图像特征检索和识别的医学影像辅助诊断系统;刘俊 等;《中国科技信息》;20080215(第4期);第166-167页 * |
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