CN110084769B

CN110084769B - OCT video image self-adaptive enhancement method based on histogram statistics

Info

Publication number: CN110084769B
Application number: CN201910418926.5A
Authority: CN
Inventors: 梁江荣; 赵晖; 叶剑锋; 任均宇; 吴春波
Original assignee: Foshan Light Micro Technology Co ltd
Current assignee: Foshan Light Micro Technology Co ltd
Priority date: 2019-05-20
Filing date: 2019-05-20
Publication date: 2023-05-23
Anticipated expiration: 2039-05-20
Also published as: CN110084769A

Abstract

The invention discloses a histogram statistics-based OCT video image self-adaptive enhancement method, which is characterized in that maximum points and slow inflection points in a histogram curve are calculated based on statistics of an OCT image histogram, then a calculation region is linearly stretched and integrated into a mapping table, and finally the mapping table is applied to an original image, so that the effects of enhancing effective signals of a foreground and inhibiting background noise can be achieved.

Description

OCT video image self-adaptive enhancement method based on histogram statistics

Technical Field

The invention relates to the field of computer software/image processing, in particular to an OCT video image self-adaptive enhancement method based on histogram statistics.

Background

OCT (optical coherence tomography) scanning is a novel optical imaging technology, which is based on the interference principle of light, adopts infrared light with longer wavelength to shoot, can penetrate a certain depth of a scanning medium, and thus obtains a high-resolution cross-sectional image of scanned tissue by reflection, and can form a three-dimensional image of the scanned tissue by transverse continuous scanning. In the medical field, OCT imaging technology has been widely popularized and applied, because OCT provides imaging with resolution of micron order for living tissue, and can be used as a judging means for in vivo pathology. However, the imaging light path of OCT is complex, where the presence of background noise affects the contrast of the OCT effective signal, thereby affecting the doctor's diagnosis from the image. Therefore, an OCT image enhancement technique is needed to improve OCT image quality and diagnostic accuracy.

In the image processing technology, in view of the real-time processing of video, an airspace method is generally adopted for image enhancement. The conventional histogram equalization HE algorithm (image histogram equalization), the restricted histogram equalization CLAHE algorithm (CLAHE, contrast Limited Adaptive Histogram Equalization), aims to enhance the contrast of images, but in OCT video image applications, the background noise is easily erroneously enhanced and amplified in a scene with insignificant foreground, so that the observation of the tissue state by a doctor is seriously affected, so that the condition of a patient is erroneously judged. The non-linear image enhancement method similar to gamma transformation can improve the OCT video image to a certain extent, but the relatively fixed transformation method is difficult to adapt to the OCT video image under various environmental conditions, and the enhancement effect is not obvious in the occasion with dark prospect.

Accordingly, the prior art is still in need of improvement and development.

Disclosure of Invention

The invention aims to provide an OCT video image self-adaptive enhancement method based on histogram statistics, which aims to solve the problems that the existing partial image enhancement method has no self-adaptability to the enhancement effect of an OCT video image, noise is easy to be brought or the enhancement effect is not obvious, or the partial complex image enhancement method is not suitable for processing scenes in real time.

The technical scheme of the invention is as follows:

an OCT video image self-adaptive enhancement method based on histogram statistics specifically comprises the following steps:

s1: inputting an original image;

s2: counting the histogram of the original image;

s3: calculating an original image gray value corresponding to a histogram maximum point and an original image gray value corresponding to a histogram slowing inflection point;

s4: calculating a mapping table of image enhancement according to the original image gray value corresponding to the maximum point of the histogram and the original image gray value corresponding to the slowing inflection point of the histogram;

s5: applying the mapping table to the original image to obtain an enhanced image;

s6: and outputting the enhanced image.

In the self-adaptive enhancement method of the OCT video image based on histogram statistics, in the S1, the input original image is a gray image with 8-bit gray values.

In the self-adaptive enhancement method of the OCT video image based on histogram statistics, in the S2, a total of 256 histograms of gray values of an original image are calculated.

In the self-adaptive enhancement method of the OCT video image based on histogram statistics, in the S3, 256-bit histogram statistics values of an original image are compared to obtain an original image gray value corresponding to the maximum value of the histogram, and the histogram value at the position, which is 95% of the total area, of the histogram coverage is taken as a slowing inflection point to obtain the original image gray value corresponding to the slowing inflection point of the histogram.

According to the OCT video image self-adaptive enhancement method based on histogram statistics, the histogram maximum value of an original image is calculated through a formula (1), and the gradual inflection point corresponding to the histogram maximum value of the original image is calculated through a formula (2):

wherein Hist [256] is a 256-bit histogram statistic Value, value_Max is an original image gray Value corresponding to a maximum Value point of Hist [256], value_Turn is an original image gray Value corresponding to a histogram gradual inflection point, width is a Width of an original image, and Height is a Height of the original image.

In the histogram statistics-based OCT video image adaptive enhancement method, in S4, when Vi < value_max, map [ Vi ] =0; when value_Max is less than or equal to Vi and less than value_Turn, linear stretching is carried out according to the Value Max and the value_Turn to obtain Map [ Vi ]; when Vi > value_turn, map [ Vi ] =255; where Vi e [0, 255] represents 256 gray values, and Map [ Vi ] is the corresponding gray mapping table.

In the self-adaptive enhancement method of the OCT video image based on the histogram statistics, in the S4, according to the calculated value_Max and value_Turn, a mapping table Map of image enhancement is obtained through a formula (3):

where Vi e [0, 255] represents 256 gray values, and Map [ Vi ] is the corresponding gray mapping table.

In the self-adaptive enhancement method of the OCT video image based on histogram statistics, in the step S5, a mapping table is applied to an original image through a formula (4), so that an enhanced image is obtained:

wherein Dst is the enhanced image, and Map [ Src ] is to apply the mapping table Map to each pixel of the original image Src.

The invention has the beneficial effects that: the invention provides a histogram statistics-based OCT video image self-adaptive enhancement method, which is characterized in that the maximum point and the slowing inflection point in a histogram curve are calculated based on the statistics of an OCT image histogram, then the calculated area is linearly stretched and integrated into a mapping table, and finally the mapping table is applied to an original image, so that the effects of enhancing the effective signal of the foreground and inhibiting the background noise can be achieved.

Drawings

Fig. 1 is a flow chart of steps of an OCT video image adaptive enhancement method based on histogram statistics in the present invention.

Fig. 2a to 2c are original images, enhanced effect images and histograms of one of the original images in the present invention.

Fig. 3a to 3c are original, enhanced effect, histogram of another original image according to the present invention.

Fig. 3d to 3e are enhancement effect diagrams of the histogram equalization HE and the limiting histogram equalization CLAHE of another original image according to the present invention.

Detailed Description

Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and are not to be construed as limiting the present invention.

In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.

In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically connected, electrically connected or can be communicated with each other; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.

In the present invention, unless expressly stated or limited otherwise, a first feature "above" or "below" a second feature may include both the first and second features being in direct contact, as well as the first and second features not being in direct contact but being in contact with each other through additional features therebetween. Moreover, a first feature being "above," "over" and "on" a second feature includes the first feature being directly above and obliquely above the second feature, or simply indicating that the first feature is higher in level than the second feature. The first feature being "under", "below" and "beneath" the second feature includes the first feature being directly under and obliquely below the second feature, or simply means that the first feature is less level than the second feature.

The following disclosure provides many different embodiments, or examples, for implementing different features of the invention. In order to simplify the present disclosure, components and arrangements of specific examples are described below. They are, of course, merely examples and are not intended to limit the invention. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples, which are for the purpose of brevity and clarity, and which do not themselves indicate the relationship between the various embodiments and/or arrangements discussed. In addition, the present invention provides examples of various specific processes and materials, but one of ordinary skill in the art will recognize the application of other processes and/or the use of other materials.

As shown in fig. 1, the self-adaptive enhancement method for OCT video image based on histogram statistics specifically includes the following steps:

s1: the original image is input and denoted Src.

The first step: the original image Src is input, and is a gray-scale image with 8-bit gray-scale values, the Width is denoted by Width, and the Height is denoted by Height. For example, fig. 2 (a) is an OCT video image with a slightly pronounced effective signal, and fig. 3 (a) is an OCT video image with a lacking a pronounced effective signal.

S2: the histogram of the original image Src is counted and is denoted as Hist [256].

And a second step of: a histogram of 256 gray values of the original image Src is calculated and is denoted as Hist [256].

S3: and calculating an original image gray Value corresponding to a Hist [256] maximum point and an original image gray Value corresponding to a Hist [256] buffering inflection point, wherein the original image gray Value corresponding to the Hist [256] maximum point is marked as value_Max, and the original image gray Value corresponding to the Hist [256] buffering inflection point is marked as value_Turn.

And a third step of: the 256-bit histogram statistical Value of the original image Src is traversed, the histogram maximum Value Max of the original image Src can be found through comparison, and at the moment, the corresponding gray Value value_Max is recorded, as shown in a formula (1). By observation, it can be found that the histogram curve of OCT video images is generally single-peak, and the curve rises first and then falls, and finally tends to be gentle to zero. In the technical scheme, the histogram Value at 95% of the total area covered by the histogram is marked as a feature point, the feature point is called a gradual inflection point, and the gray Value of the original image corresponding to the gradual inflection point is value_Turn, as shown in formula (2). For example, corresponding to fig. 2a, the value_max is 43, the value_turn is 70 (as shown in fig. 2c, the gray part is a histogram), and the two values are far apart, which indicates that the foreground valid signal is obvious; and corresponding to fig. 3a, the value_max is 41, the value_turn is 48 (as shown in fig. 3c, the gray part is a histogram), the two values are very similar, which indicates that the foreground effective signal is not obvious, similar to the background noise.

S4: and calculating an image enhancement mapping table Map according to the original image gray Value value_Max corresponding to the maximum Value point of Hist [256] and the original image gray Value value_Turn corresponding to the gradual inflection point of Hist [256].

Fourth step: and (3) obtaining the Map of the image enhancement through a formula (3) according to the value_Max and the value_Turn obtained through the calculation in the third step. Where Vi ε [0, 255] represents 256 gray values and Map [ Vi ] is the corresponding gray mapping table. When Vi < value_max, map [ Vi ] =0; when value_Max is less than or equal to Vi and less than value_Turn, linear stretching is carried out according to the Value Max and the value_Turn to obtain Map [ Vi ]; when Vi > value_turn, map [ Vi ] =255.

S5: and applying the Map to the original image Src to obtain an enhanced image, and recording the enhanced image as Dst.

Fifth step: applying the mapping table Map to each pixel of the original image Src to obtain an enhanced image Dst, as shown in formula (4):

s6: the enhanced image Dst is output.

Sixth step: the enhanced image Dst is output.

For example, the effect diagram after enhancement of fig. 2 (a) is fig. 2 (b), and the corresponding histogram-based enhancement is depicted as fig. 2 (c), and it can be seen that the effective signal is significantly enhanced while the background noise is suppressed. For example, the enhanced effect diagram of fig. 3 (a) is fig. 3 (b), and the corresponding histogram-based enhancement is described as fig. 3 (c), and since the foreground effective signal of the original image is not obvious and is similar to the background noise, the enhanced image has no clustered significant enhancement signal, and meets the adaptive requirement.

The effects of the conventional enhancement method (histogram equalization HE, restricted histogram equalization CLAHE) on fig. 3 (a) are fig. 3 (d), 3 (e), and as can be seen from fig. 3 (d) and 3 (e), the noise after enhancement is very obvious due to lack of self-adaptive description factors, and does not meet the use requirements.

According to the technical scheme, the maximum point and the slowing inflection point in the histogram curve are calculated based on statistics of the OCT image histogram, then the calculated area is linearly stretched and integrated into the mapping table, and finally the mapping table is applied to the original image, so that the effects of enhancing the effective signal of the foreground and inhibiting the background noise can be achieved.

The technical scheme provides an OCT video image description factor based on histogram feature points: maximum point and slow inflection point; the factor can characterize the characteristics of the current image, adaptively calculate the most applicable enhancement mapping table of the current image, and ensure that the image can be enhanced more reasonably under the condition that the effective signal is obvious or not obvious; moreover, the algorithm is low in complexity and suitable for real-time video processing scenes.

The obvious difference between the technical scheme and the prior art is that: the traditional technology does not consider the self-adaption problem of OCT video images in detail, and is easy to erroneously enhance and amplify background noise; the technical scheme introduces a characteristic factor of a histogram curve suitable for analyzing OCT images: the maximum point and the slowing inflection point can adaptively calculate characteristic factors for images in different scenes and convert the characteristic factors into an enhancement mapping table; the technical scheme can enhance the expression of effective signals, obviously inhibit background noise and weaken the influence of environmental noise; the technical scheme has low algorithm complexity and strong real-time performance.

In the description of the present specification, reference to the terms "one embodiment," "certain embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

It is to be understood that the invention is not limited in its application to the examples described above, but is capable of modification and variation in light of the above teachings by those skilled in the art, and that all such modifications and variations are intended to be included within the scope of the appended claims.

Claims

1. An OCT video image self-adaptive enhancement method based on histogram statistics is used in the medical field and is characterized by comprising the following steps:

s1: inputting an original image;

s2: counting the histogram of the original image;

s6: outputting the enhanced image;

in the step S2, calculating a histogram of 256 gray values in the original image;

in the step S3, 256-bit histogram statistical values of the original image are compared to obtain an original image gray value corresponding to the maximum value of the histogram, and the histogram value at the position, which is 95% of the total area covered by the histogram, is taken as a gradual inflection point to obtain the original image gray value corresponding to the gradual inflection point of the histogram;

calculating the maximum value of the histogram of the original image through a formula (1), and calculating a gradual inflection point corresponding to the maximum value of the histogram of the original image through a formula (2):

Hist[Value_Max]＝Max＝max(Hist[256]) (1)

Hist[Value_Turn]＝0.95·Width·Height (2)

wherein Hist [256] is a 256-bit histogram statistic Value, value_Max is an original image gray Value corresponding to a maximum Value point of Hist [256], value_Turn is an original image gray Value corresponding to a histogram gradual inflection point, width is the Width of an original image, and Height is the Height of the original image;

in the step S4, when Vi < value_max, map [ Vi ] =0; when value_Max is less than or equal to Vi and less than value_Turn, linear stretching is carried out according to the Value Max and the value_Turn to obtain Map [ Vi ]; when Vi > value_turn, map [ Vi ] =255; wherein Vi [0, 255] represents 256 gray values, and Map [ Vi ] is a corresponding gray mapping table;

in the step S4, according to the calculated value_max and value_turn, a Map of image enhancement is obtained by the formula (3):

2. The OCT video image adaptive enhancement method based on histogram statistics according to claim 1, wherein in S1, the input original image is a gray-scale image having 8-bit gray-scale values.

3. The method for adaptively enhancing an OCT video image based on histogram statistics according to claim 1, wherein in S5, the mapping table is applied to the original image by the formula (4), resulting in an enhanced image:

Dst＝Map[Src] (4)