CN110349171A - A kind of scoliosis back contour curve extracting method based on gray scale intermediate value - Google Patents
A kind of scoliosis back contour curve extracting method based on gray scale intermediate value Download PDFInfo
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Abstract
The scoliosis back contour curve extracting method based on gray scale intermediate value that the invention discloses a kind of, blue background cloth and photography light are arranged first, upright human body back image is shot using ordinary digital camera, RGB image red channel information is extracted later, binary conversion treatment obtains human body back profile, back image is subjected to gray processing processing and the gray level image for the background interference that is removed with binaryzation human body back profile diagram dot product again, gray level image is subtracted into pixel gray level mean value again and obtains new gray level image, seek gray scale median pixel characteristic point line by line later, least square method fitting of a polynomial is carried out to characteristic point again and obtains backbone contour fitting curve.Method simple practical of the invention, algorithm are easily achieved, and can effectively complete the purpose of human body back backbone contour curve extraction.
Description
Technical field
The invention belongs to technical field of image processing, more particularly to a kind of scoliosis back profile based on gray scale intermediate value
Curve extracting method.
Background technique
Backbone is the axis of human body, physical appearance exception, dyskinesia is not only resulted in when scoliosis is serious, also
Cardio-pulmonary function obstacle can be caused because of chest deformity, reduced quality of life, seriously affected the development of teenager's physical and mental health.The disease
If not finding simultaneously active treatment early, the figure and appearance of infant are not only influenced, but also may cause cardio-pulmonary function exception, is made
, there is pain in the too early regression of backbone, and trunk is uneven.There is the failure of cardio-pulmonary function in the serious sick child of deformity, or even early stage, lead
It is lethal to die.
The method for checking scoliosis has very much, is broadly divided into two class of physical measurement and image measurement.Physical measurement is
Refer to the direct contact measurement scoliosis of human body back, mainly have Adams bend forward test, using scoliosis ruler measure body
The methods of dry rotation angle, measurement rib cage protuberantia；Image measurement refers to the inspection method not contacted directly with human body back,
Mainly there are moir é pattern mensuration, X-ray mensuration, structural light measurement method, laser scanner measurement method etc..
Although existing method can check scoliosis, since existing census method is to be mostly based on people
The physical detection of work, when being generally investigated especially teenager's physical examination to a large amount of crowd, artificial detection is cumbersome, low efficiency, by
It will also result in misjudgement and erroneous judgement in inspection personnel's fatigue.And generally investigated with X-ray, teenager especially children can be caused
Many unnecessary radiation injuries, and it is costly.
Summary of the invention
Goal of the invention: a kind of scoliosis back contour curve extracting method based on gray scale intermediate value, lossless no spoke are provided
It penetrates, simple and practical, quickly and effectively, algorithm is easy to accomplish, can efficiently extract backbone back contour curve, completes scoliosis
The task of inspection.
Technical solution: for achieving the above object, the invention adopts the following technical scheme:
A kind of scoliosis back contour curve extracting method based on gray scale intermediate value, comprising the following steps:
(1) arrange that blue background cloth and photography light, headlamp need uniform irradiation in human body back；
(2) human body back image is obtained using digital camera shooting, acquires upright human body back color image information, including
RGB triple channel luminance information and image resolution ratio size n × m；
(3) gray processing processing is carried out to the human body back color image obtained in step (2), obtains gray level image；
(4) channel separation is carried out to the human body back color image obtained in step (2), obtains red channel gray level image
Information；
(5) the red channel gray level image in step (4) is subjected to binary conversion treatment, obtains human body back bianry image；
(6) the human body back gray level image in step (3) and the human body back bianry image in step (5) are carried out a little
Multiply, the back gray level image for the background interference that is removed；
(7) by the back gray level image subtracted image gray scale base value in step (6), new gray level image is obtained；
(8) the new gray level image obtained to step (7) seeks gray scale median pixel characteristic point line by line；
(9) characteristic point that step (8) obtains is fitted to backbone back contour feature curve.
Further, it when being acquired in step (2) to human body back color image, is collected people and needs to stand just, image
Comprising from cervical vertebra topmost to lumbar vertebrae bottom.
Further, human body back color image step (2) obtained in step (3) according to formula Gray1 (i, j)=
0.30R (i, j)+0.59G (i, j)+0.11B (i, j) carries out RGB three-component weighted average and handles to obtain gray level image Gray1, R
(i, j), G (i, j), B (i, j) are RGB triple channel component pixel brightness value, the gray scale that Gray1 (i, j) is respectively
Image slices vegetarian refreshments gray value.
Further, RGB three-component channel point is carried out to the human body back color image that step (2) obtains in step (4)
From according to formula Gray2 (i, j)=R (i, j), individually extraction red channel image information obtains red channel gray level image
Gray2, R (i, j) are red channel component pixel point brightness values, and Gray2 (i, j) is red channel gray level image pixel gray level
Value.
Further, bianry image is converted by red channel gray level image Gray2 in step (5) specifically include following point
Step:
(51) gray threshold T is set；
(52) two are carried out according to the gray threshold T that (51) are set to the red channel gray level image Gray2 that step (4) obtains
Value processing, if Gray2 (i, j) > T, Binary (i, j)=1；If Gray2 (i, j)≤T, Binary (i, j)=0, obtain
To human body back profile bianry image Binary1.
Further, step (3) are obtained according to formula Gray3 (i, j)=Gray1 (i, j) * Binary (i, j) in step (6)
To gray level image Gray1 and step (5) obtained human body back bianry image Binary carry out dot product, delete background information,
Obtain back gray level image Gray3.
Further, the back gray level image Gray3 subtracted image gray scale base value obtained step (6) in step (7) has
Body the following steps are included:
(71) according to formulaSeek the every row gray value mean value of image
Average (i, 1) sets every row gray scale base value as 0.5Average (i, 1)；
(72) Gray3 for obtaining step (6) according to formula Gray4 (i, j)=Gray3 (i, j) -0.5Average (i, 1)
The every row pixel gray value of image subtracts the gray scale base value 0.5Average (i, 1) that step (71) obtains, and obtains new grayscale image
As Gray4.
Further, the new gray level image that step (8) obtains step (7) seeks gray scale median pixel characteristic point line by line
Specifically includes the following steps:
(81) blank the bianry image Binary2, Binary2 (i, j) that the new resolution sizes of one width of creation are n × m=
0；
(82) the new gray level image Gray4 obtained to step (7) is according to formulaIt carries out
Row summation obtains Sum (i, 1)；
(83) assignment is carried out to bianry image Binary2, if the new gray level image Gray4 that step (7) obtains is according to formulaPixel sum and step in rows from left to right
(82) half of the sum of the every row obtained 0.5Sum (i, 1) differs within 0.5Average (i, 1), then Binary2 (i, k)=
1, finally obtain backbone back contour feature point image.
Further, the backbone back contour feature point that step (8) obtains is fitted to backbone back profile spy by step (9)
Levy curve specifically includes the following steps:
(91) characteristic point that step (8) obtains is transformed into practical Common Coordinate from image coordinate system, point by point according to formula
Characteristic point is transformed into practical normal by Point (k) .x=Point (k) .i, Point (k) .y=Point (k) .j from image coordinate system
Use coordinate system；
(92) it is bent to be fitted to human body back backbone contour feature for the practical Common Coordinate characteristic point for obtaining step (91)
Line, fit approach are least square method fitting of a polynomial.
The utility model has the advantages that compared with prior art, the present invention is acquired using White-light image, and it is lossless radiationless, it is quick and convenient, it is real
When detect, algorithm is easy to accomplish, can efficiently extract backbone back contour curve, completes the task of scoliosis inspection.
Detailed description of the invention
Fig. 1 is the method for the present invention flow diagram；
Fig. 2 is the human body back image that digital camera obtains；
Fig. 3 is human body back gray level image；
Fig. 4 is red channel gray level image；
Fig. 5 is human body back two-value contour images；
Fig. 6 is the back gray level image for removing background interference；
Fig. 7 is the gray level image of subtracted image gray scale base value；
Fig. 8 is backbone back contour feature point image；
Fig. 9 is backbone back contour feature curve image.
Specific embodiment
Technical solution of the present invention is described in detail below in conjunction with the drawings and specific embodiments.
For the waste for reducing artificial wealth, scoliosis detection efficiency is improved, physical measurement subjective factor bring is avoided
Erroneous detection, it is bent that the scoliosis back contour curve extracting method based on gray scale intermediate value can sketch the contours of indirectly backbone profile from body surface
Line is a kind of lossless, radiationless and simple and effective scoliosis inspection method to judge scoliosis degree.
As shown in Figure 1, a kind of scoliosis back contour curve extracting method based on gray scale intermediate value, including walk as follows
It is rapid:
(1) blue background cloth and photography light are arranged, guarantees light uniform irradiation in human body back；
(2) human body back image is obtained using digital camera shooting, acquires upright human body back color image information, including
RGB triple channel luminance information and image resolution ratio size n × m；Obtain back color image as shown in Figure 2.To human body back
When color image is acquired, it is collected people and needs to stand just, image includes from cervical vertebra topmost to lumbar vertebrae bottom.
(3) gray processing processing is carried out to the human body back color image obtained in step (2), obtains gray level image；
The color image obtained to step (2) carries out RGB three-component weighted average and handles to obtain Fig. 3 according to following formula
Shown gray level image Gray1；
Gray1 (i, j)=0.30R (i, j)+0.59G (i, j)+0.11B (i, j)；
Wherein, R (i, j), G (i, j), B (i, j) are RGB triple channel component pixel brightness value respectively, Gray1 (i,
J) the gray level image pixel gray value for being；
(4) channel separation is carried out to the human body back color image obtained in step (2), obtains red channel gray level image
Information；
RGB three-component channel separation is carried out to the human body back color image obtained in step (2), according to formula Gray2
(i, j)=R (i, j) individually extracts red channel image information, obtains red channel gray level image Gray2, R as shown in Figure 4
(i, j) is red channel component pixel point brightness value, and Gray2 (i, j) is red channel gray level image pixel gray value.
(5) bianry image is converted by the red channel gray level image Gray2 in step (4), obtains human body back two-value
Image；Specifically include it is following step by step:
(51) gray threshold T is set；
(52) two are carried out according to the gray threshold T that (51) are set to the red channel gray level image Gray2 that step (4) obtains
Value processing, if Gray2 (i, j) > T, Binary (i, j)=1；If Gray2 (i, j)≤T, Binary1 (i, j)=0, obtain
To human body back profile bianry image Binary1 as shown in Figure 5.
(6) the human body back gray level image in step (3) and the human body back bianry image in step (5) are carried out a little
Multiply, the back gray level image for the background interference that is removed；
The gray level image Gray1 for obtaining step (3) according to formula Gray3 (i, j)=Gray1 (i, j) * Binary1 (i, j)
The human body back profile bianry image Binary1 obtained with step (5) carries out dot product, deletes background information, obtains as shown in Figure 6
Remove the back gray level image of background interference；
(7) by the back gray level image Gray3 subtracted image gray scale base value in step (6), new gray level image is obtained
Gray4, specifically includes the following steps:
(71) according to formulaSeek the every row gray average Average of image (i,
1) every row gray scale base value, is set as 0.5Average (i, 1)；
(72) Gray3 for obtaining step (6) according to formula Gray4 (i, j)=Gray3 (i, j) -0.5Average (i, 1)
The every row pixel gray value of image subtracts the gray scale base value 0.5Average (i, 1) that step (71) obtains, and obtains new as shown in Figure 7
Gray level image Gray4.
(8) gray scale median pixel characteristic point is sought line by line to the new gray level image Gray4 that step (7) obtains, it is specific to wrap
Include following steps:
(81) blank the bianry image Binary2, Binary2 (i, j) that the new resolution sizes of one width of creation are n × m=
0；
(82) the new gray level image Gray4 obtained to step (7) is according to formulaIt carries out
Row summation obtains Sum (i, 1)；
(83) assignment is carried out to bianry image Binary2, if the new gray level image Gray4 that step (7) obtains is according to formulaPixel sum and step in rows from left to right
(82) half of the sum of the every row obtained differs within 0.5Average (i, 1), then Binary2 (i, k)=1, finally obtains
Backbone back as shown in Figure 8 contour feature point image.
(9) the backbone back contour feature point that step (8) obtains is fitted to backbone back contour feature curve, it is specific to wrap
Include following steps:
(91) characteristic point that step (83) obtains is transformed into practical Common Coordinate from image coordinate system, point by point according to formula
Characteristic point is transformed into practical normal by Point (k) .x=Point (k) .i, Point (k) .y=Point (k) .j from image coordinate system
Use coordinate system；
(92) it is bent to be fitted to human body back backbone contour feature for the practical Common Coordinate characteristic point for obtaining step (91)
Line, as shown in figure 9, fit approach is least square method fitting of a polynomial.
A kind of scoliosis back contour curve extracting method based on gray scale intermediate value of the invention, first arrangement blue back
Scape cloth and photography light shoot upright human body back image using ordinary digital camera, extract RGB image red channel later
Information, binary conversion treatment obtain human body back profile, then by back image carry out gray processing processing and with binaryzation human body back
Profile diagram dot product is removed the gray level image of background interference, then gray level image is subtracted pixel gray level mean value and obtains new ash
Image is spent, seeks gray scale median pixel characteristic point line by line later, then least square method fitting of a polynomial is carried out to characteristic point and is obtained
Backbone contour fitting curve.Method simple practical of the invention, algorithm are easily achieved, and can effectively complete human body back backbone
The purpose that contour curve extracts.
Claims (9)
1. a kind of scoliosis back contour curve extracting method based on gray scale intermediate value, which comprises the following steps:
(1) arrange that blue background cloth and photography light, headlamp need uniform irradiation in human body back；
(2) human body back image is obtained using digital camera shooting, acquires upright human body back color image information, including RGB
Triple channel luminance information and image resolution ratio size n × m；
(3) gray processing processing is carried out to the human body back color image obtained in step (2), obtains gray level image；
(4) channel separation is carried out to the human body back color image obtained in step (2), obtains red channel gray level image letter
Breath；
(5) the red channel gray level image in step (4) is subjected to binary conversion treatment, obtains human body back bianry image；
(6) the human body back gray level image in step (3) and the human body back bianry image in step (5) are subjected to dot product, obtained
To the back gray level image for removing background interference；
(7) by the back gray level image subtracted image gray scale base value in step (6), new gray level image is obtained；
(8) the new gray level image obtained to step (7) seeks gray scale median pixel characteristic point line by line；
(9) characteristic point that step (8) obtains is fitted to backbone back contour feature curve.
2. a kind of scoliosis back contour curve extracting method based on gray scale intermediate value according to claim 1, special
Sign is, when being acquired in step (2) to human body back color image, is collected people and needs to stand just, image includes from cervical vertebra
Topmost arrive lumbar vertebrae bottom.
3. a kind of scoliosis back contour curve extracting method based on gray scale intermediate value according to claim 1, special
Sign is, the human body back color image obtained in step (3) to step (2) is according to formula Gray1 (i, j)=0.30R (i, j)
+ 0.59G (i, j)+0.11B (i, j) carries out RGB three-component weighted average and handles to obtain gray level image Gray1, R (i, j), G (i,
J), B (i, j) is RGB triple channel component pixel brightness value, the gray level image pixel that Gray1 (i, j) is respectively
Gray value.
4. a kind of scoliosis back contour curve extracting method based on gray scale intermediate value according to claim 1, special
Sign is, RGB three-component channel separation is carried out to the human body back color image that step (2) obtains in step (4), according to formula
The independent extraction red channel image information of Gray2 (i, j)=R (i, j), which obtains red channel gray level image Gray2, R (i, j), is
Red channel component pixel point brightness value, Gray2 (i, j) are red channel gray level image pixel gray values.
5. a kind of scoliosis back contour curve extracting method based on gray scale intermediate value according to claim 1, special
Sign is, in step (5) by red channel gray level image Gray2 be converted into bianry image specifically include it is following step by step:
(51) gray threshold T is set；
(52) binaryzation is carried out according to the gray threshold T that (51) are set to the red channel gray level image Gray2 that step (4) obtains
Processing, if Gray2 (i, j) > T, Binary (i, j)=1；If Gray2 (i, j)≤T, Binary (i, j)=0, obtain people
Body back profile bianry image Binary1.
6. a kind of scoliosis back contour curve extracting method based on gray scale intermediate value according to claim 1, special
Sign is, the grayscale image for obtaining step (3) according to formula Gray3 (i, j)=Gray1 (i, j) * Binary (i, j) in step (6)
As the human body back bianry image Binary progress dot product that Gray1 and step (5) obtain, background information is deleted, back ash is obtained
Spend image Gray3.
7. a kind of scoliosis back contour curve extracting method based on gray scale intermediate value according to claim 1, special
Sign is, specifically includes the back gray level image Gray3 subtracted image gray scale base value that step (6) obtains in step (7) following
Step:
(71) according to formulaThe every row gray value mean value Average of image (i, 1) is sought,
Every row gray scale base value is set as 0.5Average (i, 1)；
(72) the Gray3 image for obtaining step (6) according to formula Gray4 (i, j)=Gray3 (i, j) -0.5Average (i, 1)
Every row pixel gray value subtracts the gray scale base value 0.5Average (i, 1) that step (71) obtains, and obtains new gray level image
Gray4。
8. a kind of scoliosis back contour curve extracting method based on gray scale intermediate value according to claim 1, special
Sign is, the new gray level image that step (8) obtains step (7) seek line by line gray scale median pixel characteristic point specifically include with
Lower step:
(81) blank bianry image Binary2, Binary2 (i, j)=0 that the new resolution sizes of one width of creation are n × m；
(82) the new gray level image Gray4 obtained to step (7) is according to formulaRow is carried out to ask
With obtain Sum (i, 1)；
(83) assignment is carried out to bianry image Binary2, if the new gray level image Gray4 that step (7) obtains is according to formulaPixel sum and step in rows from left to right
(82) half of the sum of the every row obtained 0.5Sum (i, 1) differs within 0.5Average (i, 1), then Binary2 (i, k)=
1, finally obtain backbone back contour feature point image.
9. a kind of scoliosis back contour curve extracting method based on gray scale intermediate value according to claim 1, special
Sign is, it is specific that the backbone back contour feature point that step (8) obtains is fitted to backbone back contour feature curve by step (9)
The following steps are included:
(91) characteristic point that step (8) obtains is transformed into practical Common Coordinate from image coordinate system, point by point according to formula Point
(k) characteristic point is transformed into practical common seat from image coordinate system by .x=Point (k) .i, Point (k) .y=Point (k) .j
Mark system；
(92) the practical Common Coordinate characteristic point for obtaining step (91) is fitted to human body back backbone contour feature curve, intends
Conjunction mode is least square method fitting of a polynomial.
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| CN112258516B (en) * | 2020-09-04 | 2023-04-07 | 温州医科大学附属第二医院、温州医科大学附属育英儿童医院 | Method for generating scoliosis image detection model |
| CN112033250A (en) * | 2020-10-26 | 2020-12-04 | 湖南大学 | Automatic calibrating device and calibrating method for steel ruler |
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