CN109118499B - Layer segmentation method of coherent light tomography image based on backtracking shortest path algorithm - Google Patents
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Abstract
The invention discloses a layer segmentation method of a coherent light tomography image based on a backtracking shortest path algorithm, which comprises the following steps: s1, denoising the original image; s2, calculating the weights of all nodes in the graph to form a weight graph for the image after the noise is removed; s3, defining a starting point and an end point of the shortest path algorithm on the weight graph by adopting a method of automatically initializing the end points; s4, calculating and finding the minimum accumulated cost path between the starting point and the end point by using a shortest path algorithm with backtracking on the weight graph; s5, removing the auxiliary columns added when the end points are defined in the obtained minimum accumulated cost path to obtain a layer segmentation result of one layer in the coherent light tomography image; limiting the image search area, and repeating the steps S3-S5 to finally obtain the segmentation results of all layers in the coherent light tomography image. The segmentation method avoids the short circuit problem in the traditional method, is suitable for segmenting normal and lesion-affected coherent tomography images, and has high accuracy and flexibility.
Description
Technical Field
The invention relates to the technical field of coherent light tomographic image processing, in particular to a layer segmentation method of a coherent light tomographic image based on a backtracking shortest path algorithm.
Background
Oct (optical Coherence tomography), also known as optical Coherence tomography, is a new tomographic technique with high resolution and tomographic features on biological internal structures. OCT is able to obtain sub-surface images at near microscopic resolution because it uses light waves as a detection means rather than acoustic waves. The wavelength of light wave is shorter than that of sound wave, and the propagation speed is faster than that of sound wave. OCT was developed based on low coherence interference techniques and has made tremendous progress in recent decades. One generation of OCT, also known as time-domain OCT (TD-OCT), acquires 400 a scans per second with an axial resolution of 8 to 10 microns. Spectral domain OCT (SD-OCT) appeared in 2011. The imaging speed of the spectral domain OCT equipment which is commercially used at present is 10 to 100 times faster than that of the time domain OCT equipment, and the axial resolution range is 3 to 7 micrometers. Recently, swept source OCT (SS-OCT) has been developed, which can acquire over a hundred thousand a-scans per second, with axial resolution up to 3 microns.
OCT can image biological tissue in real time without the need to prepare a sample. It does not involve ion radiation and has no harm to human health. OCT has rapidly developed in many fields and can provide high-resolution cross-sectional images from backscatter profiles of biological samples by using low coherence interferometry. This allows pathological and morphological changes in the retina to be observed during the progression of the disease or before the patient recognizes visual changes (visual acuity). Over the past two decades, OCT has become a well established imaging method widely used by ophthalmologists in the diagnosis of retinal and optic nerve diseases. Distortion of the retinal layer is an important signal for experts in the diagnosis of age-related macular degeneration, macular edema, and macular hole. However, manual layer segmentation is typically a time consuming and poorly repeatable process. Therefore, an automatic or semi-automatic segmentation method of the OCT image is necessary.
Some automatic or semi-automatic OCT slice segmentation methods are currently available, which can be roughly classified into three types, a scan-based method, a scan-B based method, and a three-dimensional volume data-based method. The a-scan based method can detect the peak or valley of intensity on the boundary on each a-scan section, and then connect the detected points by using model fitting technique to form a smooth and continuous boundary. However, the a-scan based method mostly does not consider information around the detection point and is easily affected by noise, and thus, the accuracy and robustness of the a-scan based method are limited. Common methods based on B-scan include active contour methods, graph search methods based on shortest paths, and statistical model methods. B-scan methods are generally superior to a-scan methods, however, they still may fail to detect diseased retinal structures. The existing segmentation method based on volume data mainly adopts a 3D map-based method and a pattern recognition method, but the methods are generally high in calculation cost, and the pattern recognition method generally needs training data which is manually segmented by experts to learn a feasible classification model. These pattern recognition methods are also affected in terms of accuracy and efficiency. Although image algorithms applicable to normal images or limited pathologies have made great progress, segmentation of retinal image layers with significant pathological changes remains a challenge. For example, in the macular hole, when the central region is severely deformed, the above method cannot accurately perform segmentation.
The algorithm for solving the shortest path problem in the graph is called as a shortest path algorithm, and the most commonly used shortest path algorithms are Dijkstra algorithm, Bellman-Ford algorithm and Floyd-Warshall algorithm. In the shortest path method, it extracts a curve-like structure in an image with the smallest cumulative cost through a communication path between two given points (a start point and an end point). However, the traditional shortest path algorithm has a short circuit problem, the improved shortest path algorithm with backtracking can effectively avoid the short path problem, and the method is suitable for segmentation images of the pathological change retina layer structure.
Disclosure of Invention
In order to overcome the defects of the technology, the invention provides a layer segmentation method of a coherent light tomography image based on a backtracking shortest path algorithm.
The technical scheme adopted by the invention for overcoming the technical problems is as follows:
a layer segmentation method of a coherent light tomography image based on a backtracking shortest path algorithm comprises the following steps:
s1, denoising the original image;
s2, calculating the weights of all nodes in the graph to form a weight graph for the image after the noise is removed;
s3, defining a starting point and an end point of the shortest path algorithm on the weight graph by adopting a method of automatically initializing the end points;
s4, calculating and finding the minimum accumulated cost path between the starting point and the end point by using a shortest path algorithm with backtracking on the weight graph;
s5, removing the auxiliary columns added when the end points are defined in the obtained minimum accumulated cost path to obtain a layer segmentation result of one layer in the coherent light tomography image;
limiting the image search area, and repeating the steps S3-S5 to finally obtain the segmentation results of all layers in the coherent light tomography image.
Preferably, in the step S1, the BM3D algorithm is adopted for denoising, and the method is fast and convenient.
Preferably, in step S2, the node weight calculation includes a gray scale weight, a gradient weight, and a maximum response value weight after Gabor transformation of the image.
Preferably, in step S4, the following formula is adopted to calculate and find the minimum cumulative cost path between the starting point and the ending point on the weight map by using the shortest path algorithm with backtracking:
where N represents the current image node,
representing the accumulated cost of the backtracking length l of the current image node to the previous point N-l +1 towards the starting node; (ii) a
Node WiThe cost calculation formula is as follows:
Wi＝r*Wi gray+s*Wi grad+t*Wi gabor*Dif
wherein, Wi grayIs the image gray value of the node, Wi gradIs a gradient weight, Wi gaborThe maximum Gabor response value of a node, r, s, t, is three constantsThe number parameter, r + s + t, is 1.
The invention has the beneficial effects that:
the method of the invention firstly removes noise from an OCT image of a lesion, calculates all node weight maps in the map based on the noise-reduced image, then defines a starting point and an end point by an automatic endpoint initialization method, and finally calculates the minimum accumulated cost path between the starting point and the end point on the weight map by using a shortest path algorithm with backtracking as a retina layer segmentation result. The segmentation method of the invention has the following advantages:
(1) high accuracy. The method uses a shortest path algorithm with backtracking, is used in the technical field for the first time, carries out layer structure segmentation on the lesion coherence tomography image, avoids the problem of short circuit in the traditional method, and obtains better segmentation effect and performance compared with the existing method.
(2) Flexibility. The method is also suitable for the layer structure segmentation of the normal coherence tomography image, can obtain a better segmentation result image on the normal image, and has higher flexibility and adaptability.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of the present invention.
Fig. 2 is a schematic view of the retinal layer structure.
Fig. 3(a) is the original before Gabor conversion, fig. 3(b), fig. 3(c), and fig. 3(d) are response graphs in the 30-degree direction, the horizontal direction, and the vertical direction, respectively, fig. 3(e) is the maximum response graph after Gabor conversion, and fig. 3(f) is the direction information graph after Gabor conversion.
Fig. 4 is an original before division.
Fig. 5 is a graph of the layer division result of fig. 4.
Fig. 6 is a schematic diagram of automatically initializing the start and end points.
Detailed Description
In order to facilitate a better understanding of the invention for those skilled in the art, the invention will be described in further detail with reference to the accompanying drawings and specific examples, which are given by way of illustration only and do not limit the scope of the invention.
The invention discloses a layer segmentation method of a coherent light tomography image based on a backtracking shortest path algorithm, and the specific implementation process of the method is described below by taking a pathological change retina layer as an example and combining with accompanying drawings 1-6.
Step one, denoising an original image
The retina image data used in the invention has noise, which affects the final image segmentation result, and many image denoising methods exist, and the embodiment adopts BM3D algorithm to perform fast and convenient denoising processing to obtain the denoised image.
The BM3D algorithm: the method comprises the steps of firstly dividing an image into blocks with a certain size, combining two-dimensional image blocks with similar structures together to form three-dimensional arrays according to the similarity between the image blocks, then processing the three-dimensional arrays by a combined filtering method, and finally returning the processed result to the original image through inverse transformation, thereby obtaining the denoised image.
Step two, calculating the node weight to obtain a weight graph
After the denoised image is obtained in the first step, the second step needs to calculate all node weights on the denoised image, in this embodiment, the node weights are calculated by using image intensity, gradient information and Gabor direction information, which is specifically as follows:
first, an intensity weight of the image is calculated. The image intensity represents the intensity of a single-channel image pixel, in our dataset, the image is a gray scale image, so the intensity is the gray scale of the pixel, in the retina OCT image, the calculation formula of the intensity weight of this embodiment is as follows:
where I (I, j) represents the image gray scale value of the node in the original image, I, j is the position of the pixel in the image, and k is a constant value set to 1.
Second, the weight of the gradient information is calculated. The gradient image is obtained by convolving the original image with a filter, the image gradient can be used to extract the edge information of the image, in this embodiment, the layer in the retina OCT image can be used as the edge information, so the gradient information is used, and the calculation formula of the image gradient weight is as follows:
dx(i,j)＝I(i+1,j)-I(i,j) (3)
where I is the image, I, j are the coordinates of the pixels, and there are two types of edge considerations: dark to light edges and light to dark edges, we calculate dy (i, j) as shown above.
And finally, calculating the weight of the Gabor direction information. The Gabor transform is a special case of a short-time fourier transform, which is commonly used to determine the sinusoidal frequency and phase content of a signal as it varies over time, and can be used to extract features in different directions. In the method, a Gabor transform is applied to each pixel to calculate its response value in different directions, and then the calculation is performed using a Gabor maximum response value as a weight.
When calculating the weight of a node, the three weight calculation methods described above are combined as follows:
Wi＝r*Wi gray+s*Wi grad+t*Wi gabor*Dif (5)
wherein, Wi grayImage grey scale weight of node, Wi gradIs a gradient weight, Wi gaborFor the maximum response value of a node, r, s, t are three constant parameters, and the sum of r + s + t is 1.
Step three, automatically initializing algorithm propagation starting point and end point
FIG. 6 shows an example image with an auto-initialization technique. Firstly, adding a row of weight values to the left side and the right side of the weight graph obtained in the step two, wherein the weight values of the two rows are set as the minimum value of the whole weight graph; then, the starting point and the end point are automatically initialized to the upper left corner node and the lower right corner node, as shown in fig. 6, and the leftmost column and the rightmost column are the added auxiliary weight minimum value columns.
Step four, calculating the minimum accumulated cost path by using the shortest path algorithm with backtracking on the weight graph
Because of the short circuit problem, the distorted retina layer structure cannot be accurately segmented by the original shortest path algorithm, and therefore, the problem is solved by adopting a backtracking method in the embodiment. When the accumulated cost is calculated between the starting point and the end point of the weight graph, backtracking a certain length from the current node to the starting point direction, and replacing the node cost with the backtracked short edge accumulated cost. In doing so, the problem of short circuits can be avoided, as follows:
where N is the current image node,
and representing the accumulated cost of the backtracking length l of the current image node to the previous point.
In addition, the implementation method also defines a new weight component by utilizing Gabor direction information to limit the propagation direction of the algorithm. In the layer segmentation task of OCT images, the direction of the layers is usually horizontal, as shown in fig. 2, and this a priori information can be exploited to improve the robustness of the segmentation method. When the gabor transform is applied to an image, each node has a principal direction value, which is used in the method to limit the propagation direction, as shown in fig. 3 (f). In the algorithm propagation process, the average direction value of the traced short side is calculated, the average direction is used as a propagation trend, the direction value of the current node is compared with the average direction value, and the propagation of the next node is determined.
Step five, removing the auxiliary column added when the end point is defined in the obtained minimum accumulated cost path to obtain the layer segmentation result of one layer in the coherent light tomography image
As shown in fig. 6, on the weight graph after the auxiliary columns are added, lines represent an example result after the method of the present embodiment is used, and then the left and right columns of auxiliary columns that are added are deleted, so as to obtain a segmentation result of the ILM layer in the lesion retina layer structure.
After the ILM layer is segmented, the image search area is limited, and the above steps three to five are repeated to obtain the segmentation results of the remaining layers as shown in fig. 2.
In the description of the present invention, the terms "horizontal", "vertical", "upper", "lower", "left", "right", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for the purpose of describing the present invention and simplifying the description, and do not indicate or imply that the structures referred to must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention.
The foregoing merely illustrates the principles and preferred embodiments of the invention and many variations and modifications may be made by those skilled in the art in light of the foregoing description, which are within the scope of the invention.
Claims (3)
1. A layer segmentation method of a coherent light tomography image based on a shortest path backtracking algorithm is characterized by comprising the following steps:
s1, denoising the original image;
s2, calculating the weights of all nodes in the graph to form a weight graph for the image after the noise is removed;
s3, defining a starting point and an end point of the shortest path algorithm on the weight graph by adopting a method of automatically initializing the end points;
s4, calculating and finding the minimum accumulated cost path between the starting point and the end point by using a shortest path algorithm with backtracking on the weight graph; calculating and finding the minimum accumulated cost path between the starting point and the end point by using a shortest path algorithm with backtracking on the weight graph by adopting the following formula:
where N represents the current image node,
representing the cumulative sum of node weight values from the start node start to the current image node N on the path,
representing the cumulative sum of node weight values from the start node start to the image node N-l on the path,
representing the accumulated cost of the backtracking length l of the current image node to the previous point N-l +1 towards the starting node;
cost W of node iiThe calculation formula of (a) is as follows:
Wi＝r*Wi gray+s*Wi grad+t*Wi gabor*Dif
wherein, Wi grayIs the image gray value of the node, Wi gradIs a gradient weight, Wi gaborThe maximum response value weight after Gabor transformation of the node is shown, r, s and t are three constant parameters, r + s + t is 1, and Dif represents the direction deviation of the path;
s5, removing the auxiliary columns added when the end points are defined in the obtained minimum accumulated cost path to obtain a layer segmentation result of one layer in the coherent light tomography image;
limiting the image search area, and repeating the steps S3-S5 to finally obtain the segmentation results of all layers in the coherent light tomography image.
2. The method according to claim 1, wherein in step S1, the BM3D algorithm is used for denoising.
3. The method according to claim 1, wherein in step S2, the node weight calculation includes a gray scale weight, a gradient weight, and a maximum response value weight after Gabor transformation of the image.
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| 基于超高分辨率OCT图像的视网膜层状结构研究;徐肃仲等;《国际眼科杂志》;20140831;第14卷(第8期);第1494-1497页 * |
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