Salient region growing based on Gaussian pyramid

IET Image ProcessingVolume 15, Issue 13 p. 3142-3152 ORIGINAL RESEARCH PAPEROpen Access Salient region growing based on Gaussian pyramid Jianjun Jiao, Jianjun Jiao orcid.org/0000-0002-5165-1861 School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, ChinaSearch for more papers by this authorXiaopeng Wang, Corresponding Author Xiaopeng Wang wangxiaopeng@lzjtu.edu.cn School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China Correspondence Xiaopeng Wang, School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China. Email: wangxiaopeng@lzjtu.edu.cnSearch for more papers by this authorJungping Zhang, Jungping Zhang School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, ChinaSearch for more papers by this authorQingsheng Wang, Qingsheng Wang School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, ChinaSearch for more papers by this author Jianjun Jiao, Jianjun Jiao orcid.org/0000-0002-5165-1861 School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, ChinaSearch for more papers by this authorXiaopeng Wang, Corresponding Author Xiaopeng Wang wangxiaopeng@lzjtu.edu.cn School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China Correspondence Xiaopeng Wang, School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China. Email: wangxiaopeng@lzjtu.edu.cnSearch for more papers by this authorJungping Zhang, Jungping Zhang School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, ChinaSearch for more papers by this authorQingsheng Wang, Qingsheng Wang School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, ChinaSearch for more papers by this author First published: 14 July 2021 https://doi.org/10.1049/ipr2.12307Citations: 1AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract For region growing image segmentation, seed selection and image noise are two major concerns causing negative segmentation performance. This paper proposes a regional growth algorithm based on the Gaussian pyramid (GPRG), which automatically selects seed points and optimizes the growth path. To achieve automatic seed selection, Gaussian difference pyramid generated by the Gaussian pyramid is required. The growing seed of the salient target in an image is acquired by searching for extremums in each image of the Gaussian difference pyramid. For growing path optimization, edge changing direction is updated according to different Gaussian difference pyramid images with sizes from small to large. The edge of region growing is determined by the edge of the salient target in an image. Segmentation experiments show that this algorithm is able to automatically select seeds and accurately segment the salient target in an image with and without image noise. Performances in terms of segmentation accuracy (SA), mean intersection-over-union (mIoU) and E-measure are superior compared with other methods. 1 INTRODUCTION Image segmentation is a process of pixel classification based on intensity, colour, texture and other rules between pixels. Image segmentation is a key problem in computer vision and image processing and there are many approaches, for example, algorithms [1-4] based on threshold, edge, region, clustering, deep learning, etc. The concept of region growing was introduced by Adams and Bischof in seeded region growing (SRG) algorithm [5] assuming that pixels belong to the same region are supposed to share the same property such as intensity, colour or texture [6-8]. However, on the one hand, conventional region growing algorithms are sensitive to the initial seed pixels and image noise; on the other hand, it is difficult to sort the labelled pixels accordingly [9, 10]. To handle the problems above, improvements of region growing algorithm are introduced in the literature. To the segmentation of lung tumours, Soltani-Nabipour et al. proposed the IRG algorithm in 2020 [11], which restricted the range of regional growth to a circle with the radius of the seed point (the expected area of the tumour), and took the mean value of the regional growth pixel points obtained at each stage as the new comparison threshold. After obtaining the geometric centre of the initial tumour, it then grows from multiple points in different regions of the tumour, and then modifies the growth results. In order to detect internal defects in optical pulse thermal imaging images, Feng et al. proposed the ASRG algorithm in 2020 [12], which first initializes the seed value adaptive through the mean maximum raw threshold (MMRT) algorithm. Finally, the threshold is automatically selected based on the average intensity and characteristics of the infrared thermal image, rather than a fixed value. Considering that the MRI images do not have intensity contrast, allocation of a constant threshold to segment the cochlear nerve by the region growing method leads to inaccurate segmentation results. Therefore, a new approach for the selection of seed point and the threshold is proposed in 2017 [13], which adaptively adjusts the seed point and threshold based on the size of the cochlear nerve to avoid over- or undersegmentation. The result of colour image segmentation is produced by merging small growing regions. Fan et al. [14] proposed an edge-oriented, parallel pixel labelling method with automatic seed selection. Also, they presented a seed tracking algorithm to extract moving image targets. Deng and Manjunath [15] proposed a region growing method based on the multi-scale J-images (JSEG). The JSEG algorithm quantifies image colours into several representations to construct a class mapping, which is used to distinguish different image regions. Fan et al. [16] designed an improved isotropic colour image edge detector employing a fast entropy threshold method. The edge detector is able to extract closed edges of colour images where each seed pixel is the geometric centre of each region surrounded by the closed edge. Bama et al. [17] presented a superpixel melanoma segmentation algorithm based on Gaussian mixture model by combining superpixel and region growing. Stewart et al. [18] incorporated a pulse coupled neural network to automatically identify a single connected region. Each seed point is initialized and grown in each region. The above region growing algorithms can be divided into three main categories accordingly: region growing based on automatic seed selection, edge marking and superpixel. First of all, automatic seed selection eliminates the manual load and is able to segment image without manual interaction. However, the selection method is remained to be verified. A problem with automatic seed selection is that sometimes pixels are randomly selected and it can be affected by noise. Second, although the stopping condition of region growing based on edge marking is clear, it is easy to generate multiple unexpected sub-regions Finally, superpixel methods merge pixels into larger pixel groups to improve image edge SA. However, it is harder to quantize colour than grey levels. The characteristics of GPRG algorithm in this study are automatic seed selection and optimized growing path planning. First, a Gaussian difference pyramid with different scales of the salient objects in an image is generated by Gaussian pyramid. Second, extremes or geometric centres of image in a Gaussian difference pyramid are considered as seed pixels. Third, from small size to large size images of all the Gaussian pyramid, region growing path is directed by considering the changing trend of all images. Finally, the gradient of the salient object in an image is used to the constrain the region growing. 2 GAUSSIAN PYRAMID AND GAUSSIAN DIFFERENCE PYRAMID A Gaussian pyramid is a multi-scale representation of an image in different scale spaces, simulating the retinal imaging in different distances between human and objects. It was proved earlier [19] that the Gaussian function is the most appropriate scale space kernel function with all possible assumptions. As a result, image scale space is defined as the convolution between multi-scale Gaussian function and input image [20-23]: L x , y , σ = G x , y , σ ∗ I x , y , σ (1)where L ( x , y , σ ) is scale function, I ( x , y , σ ) is an input image, σ is scale factor, x and y are coordinates of input image, G ( x , y , σ ) is Gaussian function, ∗ is convolution operator and G ( x , y , σ ) is as follows: G x , y , σ = 1 2 π σ 2 exp − x 2 + y 2 2 σ 2 (2)where σ is defined as σ = 2 o + s S σ 0 (3)where σ 0 is the initial value of σ, o is the number of octaves in Gaussian pyramid, s is the number of images in each octave, and S is a constant number. Here, according to reference [24], σ 0 = 1.6 and S= 3. In order to effectively detect positions of stable feature points in the scale space, Lowe [21] proposed to use convolution between Gaussian difference function and image to find the extremums of the scale space. D x , y , σ = G x , y , k σ − G x , y , σ ∗ I x , y = L x , y , k σ − L x , y , σ (4)where k = 2 1 S . 3 PROPOSED METHODOLOGY There are two main problems that remain to be solved: How to initialize proper seed pixels for region growing? How to define the growing regularization? In this section, we present a solution for the above two problems to improve the segmentation performance of salient region growing. 3.1 Automatic seed selection According to the Gaussian pyramid theory, the original image is smoothed via multi-scale Gaussian functions and thus a series of filtered images with sizes from large to small are produced to construct a Gaussian pyramid. These filtered images reflect approximate boundaries of the salient targets in an image. In order to highlight the salient target in an image and acquire the target boundaries in different scale-spaces, use (1) and (2) to produce different images of Gaussian pyramid. For an image with 481 × 321 pixels, Figure 1 shows five images chosen from a produced Gaussian pyramid with o = 7 and s = 6 , and the total number of images of all Gaussian pyramids are 42. FIGURE 1Open in figure viewerPowerPoint Images chosen from a Gaussian pyramid In Figure 1, from left to right, images with sizes from large to small are placed and the original image becomes smoother with a series of scale factors σ. In the last image on the right side, only approximate boundary of image target is apparent. Based on all octaves of Gaussian pyramids, we construct Gaussian difference pyramids by computing difference images between two adjacent images in each octave using (4). After a series of subtraction, pixels in smoothed areas become small or equal to 0 and the remained extremums construct salient targets in the images shown in Figure 2. FIGURE 2Open in figure viewerPowerPoint Images chosen from a Gaussian difference pyramid In Figure 2, from left to right, images are generated in the Gaussian difference pyramids with sizes from large to small. We search the extreme value from the smallest size to the largest size In the Gaussian difference pyramid and there is always an image that first appear salience target pixel, where the salient target in an image is represented by few white pixels in the second image to the last. We propose that the extremal pixel of the white region in the penultimate image is selected as the seed pixel. It can be seen that the last image on the right side contains black pixels only and it is the last image to be searched without an extreme value. Given six images, extremums or extremal regions generated by Gaussian difference pyramids are searched from the difference images with the smallest to the largest sizes. When the extremum of the salient target in an image is found, stop the searching process. Seed pixels for the salient target in an image in Figure 3 are acquired by computing geometric centres. In Figure 3, original images are placed in the first row and white extremal regions found in Gaussian difference pyramid are placed in the second row. Pixels marked in blue are geometric centres of extremal regions and they are considered as seed pixels of the salient targets in an image. FIGURE 3Open in figure viewerPowerPoint Seed pixels selection 3.2 Growing path planning This study proposes to resize every image of Gaussian difference pyramid to the size of the original image and place all greyscale images in the reverse image order (from top to bottom) of Gaussian difference pyramid to get Figure 4. From Figure 4a–k, the salient target in an image gradually changes from a pixel or a small region to a large region with clear edge details. Figures 4j and k show details of image target boundary. Based on this, it is proposed that Figure 4a is the initial growth region for seed point, Figure 4b shows the second growth region based on Figure 4a, Figure 4c shows the third growth region based on Figure 4b and so on. The detailed growth path is directed in Figure 5. FIGURE 4Open in figure viewerPowerPoint Resized images of Gaussian difference pyramid FIGURE 5Open in figure viewerPowerPoint Schematic diagram of region growing Suppose that set A is the pixel set containing foreground pixels in the previous grown binary image, set B is the pixel set containing foreground pixels required to be added in the next growing image, and set C is the pixel set containing foreground pixels required to be deleted in the next growing image. Here, we show Figure 5 as a schematic diagram indicating the growing procedure. Figure 5a is an image in Gaussian difference pyramid (see Figure 4h as an example) and suppose that green marked pixels in Figure 5a are similar to the seed pixel. Figure 5b is the next image in the same octave to which Figure 5a belongs directing the growing path (see Figure 4i as an example). Green marked pixels and yellow marked pixels are the extremums in Figures 5a and b respectively. Grey marked pixels in Figure 5c (pixel set A) are the grown region pixels from the previous growing step (the initial grown region is the seed pixel). Grey marked pixels (pixel set BW) in Figure 5d are the 8-connected boundary pixels of A. Figure 5e indicates the region growing path where yellow marked pixels (pixel set B) are required to be added to the grey marked pixels in Figure 5c and the pixelset C of green marked pixels covered by a purple circle (pixel set D2) and a red polygon (pixel set D1) are required to be deleted from grey marked pixels in Figure 5c. All green pixels in Figure 5e construct pixel set D. Grey marked pixels in Figure 5f construct the final grown region growing from grey marked pixels in Figure 5c. Then the question arises: how to generate pixel sets B and C in Figure 5f? Based on pixel set A, we have: B = I x , y I b x , y − I a x , y > ε (5) D = I x , y I a x , y − I b x , y > ε (6) D 1 = D ∩ B W (7) D 2 = D ∩ 4 - connected neighbours of D 1 (8) C = D 1 ∪ D 2 (9)where Ia(x, y) are the green marked pixels in Figure 5a, Ib(x, y) are the yellow marked pixels in Figure 5b, and set ε = 175 according to experience. Equations (12) and (13) indicate that: D1 and D2 are two sub-sets of set D; D1 is also the sub-set of BW; D2 contains the 4-connected pixels of D1. Therefore, B is the difference larger than ε between Ia(x, y) and Ib(x, y), C is the union of D1 and D2. 3.3 Algorithm description The GPRG algorithm includes four steps, which are Generating the Gaussian difference pyramid, automatic seed selection, region growing planning, and stopping condition. Step 1: Double the size of original image to construct Gaussian pyramid and Gaussian difference pyramid. Step 2: Find the extremums in the Gaussian difference pyramid image with the smallest scale and consider the geometric centres as seed pixels. When searching for the geometric centres of the salient target in an image, binarize the scale image containing extremums and calculate geometric centres of the binary image using zero-order and first-order moments of the image: i c = M 10 M 00 (10) j c = M 01 M 00 (11)where i c and j c are coordinates of pixel ( i c , j c ) and ( i c , j c ) is the geometric centre of target region. M 10 and M 01 are the first-order moment, M 00 is the zero-order moment: M 00 = ∑ i = 1 m ∑ j = 1 n v i , j (12) M 01 = ∑ i = 1 m ∑ j = 1 n i v i , j (13) M 10 = ∑ i = 1 m ∑ j = 1 n j v i , j (14)where v ( i , j ) is the intensity of pixel located at ( i , j ) . Here v ( i , j ) = 1 . Step 3: Resize each different image to the size of the original image. The change of the extremums in current and upper layer of images (e.g. in Figure 4, b is the upper layer of a, a is the current image) of Gaussian difference pyramid is considered as the growing path to add or remove pixels. Similarity, between pixel and seed is the growing rule. Step 4: Repeat Step 3 until all the images in Gaussian difference pyramid are traversed. When the size of image in Gaussian difference pyramid is twice the size of original image, stop the growing using the image gradient as the boundary of region growing and delete unrelated regions. 4 EXPERIMENTAL STUDIES In this section, experiments are performed on 64-bit MATLAB R2018a with AMD A8-7100 CPU, AMD Radeon R5 GPU and 4GB RAM. Test images are collected from the Berkeley segmentation data set (UC Berkeley Computer Vision Group–Contour Detection and Image Segmentation–Resources). Each image contains 481 × 321 pixels. After the initial seed pixels selected in part A of Section 3, we construct Gaussian difference pyramid with 42 images for every image. For the proposed algorithm, Gaussian difference pyramid images are used to direct region growing. In order to obtain a more obvious feature map for region growing, Figure 6 shows some images obtained during region growing. It differs from Figure 4 in that the target is a binary image with no internal holes, using the path-guided growth method as shown in Figure 5, which is described in formula (5) to (9). FIGURE 6Open in figure viewerPowerPoint Region growing directed by Gaussian difference pyramid In Figure 6, from the top left corner to the lower right corner, every image indicates the growing region in each step of growing. It can be noticed that the final region is grown from a single seed pixel marked in blue (seed point) to a complete region representing the salient targets in an image. The image located at the lower right corner presents the final region constrained by the boundary. 4.1 Experiments on greyscale image segmentation algorithms We show the segmentation results of GPRG with original images, ground-truth, results of K-means [25], results of Watershed [26], results of background subtraction [27] in Figure 7. It can be seen in Figure 7 that holes appear in the results of K-means and oversegmentation occurs in the watershed algorithm. Although we used a marker-controlled watershed approach to avoid this, it still produces oversegmentation for the larger target. The background subtraction algorithm for single image segmentation because it needs to estimate the background of the image, which makes the segmentation effect for a single image is not good. The GPRG algorithm results are placed along the last row and similar to the ground-truth. The reason is that the growing path defined in this proposed algorithm is different that of conventional region growing where the merging operation of regions is based on the original image intensity similarity between pixels and seeds. The advantages of GPRG algorithm are that growing is based on the intensities of pixels in Gaussian difference pyramid image and the growing is directed by Gaussian difference pyramid image by image. Therefore, there is no pixels outside the salient target in an image and thus holes are less possibly appeared in the final segmentation result. We use SSIM index [28] to evaluate the above segmentation algorithms, as shown in Table 1. It shows that the segmentation result of GPRG algorithm is closest to ground truth. FIGURE 7Open in figure viewerPowerPoint Results of greyscale images with k = 2 in the k-means, Minima = 5 in the watershed, R = 20, #min = 2 in the background subtraction. (a)–(g) are results of four methods. (a) Result on 'bird' image. (b) Result on 'elk' image. (c) Result on 'mushroom' image. (d) Result on 'skier' image. (e) Result on 'parterre' image. (f) Result on 'ape' image. (g) Result on 'man' image TABLE 1. SSIM Image K-means Watershed Background subtraction GPRG 1 0.64 0.37 0.42 0.98 2 0.43 0.46 0.14 0.79 3 0.47 0.53 0.57 0.92 4 0.51 0.32 0.47 0.89 5 0.61 0.49 0.32 0.87 6 0.38 0.31 0.49 0.89 7 0.46 0.35 0.53 0.91 4.2 Noise image segmentation In this section, image noise constructed by Gaussian white noise, salt and pepper noise, and uniformly distributed multiplicative noise is added to every test image in Figure 8. Image noise is described as '100 * d% mixed noise'. It can be seen that IRG and ASRG are sensitive to image noise, and pixels outside the salient targets in an image as well as holes are appeared in the results of IRG and ASRG. A large number of unexpected pixels appear in the segmentation results of the bear, aeroplane and bird images by the algorithm [13]. JSEG is less able to segment complete region of the bear and insect. The result of GPRG algorithm is free from noise because the noise in images of Gaussian pyramid are smoothed and the generated growing path does not have any noise. We use PSNR index to measure the segmentation effect of the above algorithms on noisy images. As shown in Table 2, GPRG algorithm has higher accuracy. FIGURE 8Open in figure viewerPowerPoint Results of mixed noise corrupted greyscale images. (a) Is six images 'elephant', 'horses', 'bear', 'aeroplane' 'insect' and 'bird', destroyed by 1%, 5%, 10%, 15%, 20% and 25% mixed noise respectively. (b) Is the ground-truth, (c)–(h) are results of IRG, ASRG, algorithm in [13], JSEG, algorithm in [17], and GPRG algorithm TABLE 2. PSNR Image IRG ASRG Ref [13] JSEG Ref [17] GPRG 1 34.48 36.61 35.24 26.45 33.98 46.25 2 32.12 35.61 34.24 24.45 28.98 45.68 3 27.23 29.48 27.35 22.76 20.71 37.85 4 25.86 26.29 24.16 18.68 19.45 34.67 5 15.41 24.89 16.25 17.48 18.66 33.21 6 14.62 22.90 12.43 17.21 15.75 32.58 4.3 Experiments on region growing based algorithms For colour image segmentation, we choose IRG, ASRG, algorithm in [13], JSEG, algorithm in [17], and the GPRG algorithm to produce segmentation results using seven images from BSDS500 benchmark, Reference [29] and network. In Figure 9, from the first line to the last line are original image, noise image, IRG algorithm segmentation results, ASRG algorithm segmentation results, segmentation results in [13], JSEG algorithm segmentation results, segmentation results in [17], and GPRG algorithm segmentation results. IRG, ASRG, and JSEG are sensitive to noise. The algorithm from IRG and reference [13] oversegments image of porcelain and woman. ASRG undersegments image of a woman and mistakenly segments the image target into several regions. JSEG segments a part of region of woman. The algorithm in [17] is less sensitive to noise because of the incorporation of superpixel. However, regions of bear and the tunnel crack are segmented incorrectly. Images along the last row are the results of the GPRG algorithm and they are free from noise. It can be seen from Figure 9 that GPRG algorithm is able to improve the segmentation performance of the salient target in an image. FIGURE 9Open in figure viewerPowerPoint Results of mixed noise corrupted colour images. (a)–(g) are results of six methods. (a) Result on 'eagles' image corrupted by 5% mixed noise. (b) Result on 'aeroplane' image corrupted by 15% mixed noise. (c) Result on 'porcelain' image corrupted by 5% mixed noise. (d) Result on 'woman' image corrupted by 5% mixed noise. (e) Result on 'bear' image corrupted by 10% mixed noise. (f) Result on 'yellow flower' image corrupted by 5% mixed noise. (g) Result on ' tunnel crack' image corrupted by 20% mixed noise Performance indexes including SA, mean intersection-over-union (mIoU) and E-measure [30-32] are used to give numerical results of SRG, ISRG, JSEG, algorithm in [13], algorithm in [17] and the proposed algorithm. SA is defined as the ratio of a number of correctly classified pixels to the total number of all pixels. which is: SA = ∑ i = 1 K A i ∩ C i ∑ j = 1 K C j (15)where Ai is the number of pixels of ith cluster in segmentation result, Ci is the number of pixels of ith cluster in ground-truth. mIoU is defined as: mIoU = 1 K ∑ i = 1 K A i ∩ C i A i ∪ C i (16)where all symbols are the same as that in (15). It indicates the similarity between the segmentation result and ground truth. The larger the mIoU, the better the segmentation result. E-measure E is defined as: E = 1 4 m n ∑ x = 1 m ∑ y = 1 n { 1 + 2 I G T x , y − μ G T ∗ I F M x , y − μ F M I G T x , y − μ G T 2 + I F M x , y − μ F M 2 2 (17)where m and n are the height and width of the image, I G T ( x , y ) and I F M ( x , y ) are intensities of pixels located at ( x , y ) in segmentation result and ground truth. The larger the E, the better the result. With 1%, 5%, 10%, 15%, 20% and 25% mixed noise, Table 3 to 5 show the results of segmentation indexes on test image in Figure 8. TABLE 3. Sa (%) of noise image segmentation Mixed noise IRG ASRG Ref [13] JSEG Ref [17] GPRG 1% 95.45 91.35 89.73 85.47 86.12 93.94 5% 83.36 90.06 87.29 81.36 83.47 93.53 10% 78.34 77.87 80.54 74.21 78.35 91.76 15% 69.87 74.51 75.94 63.57 71.48 91.02 20% 62.24 68.45 74.65 62.02 68.49 90.36 25% 40.34 61.75 32.45 60.11 65.28 70.65 TABLE 4. Miou (%) of noise image segmentation Mixed noise IRG ASRG Ref [13] JSEG Ref [17] GPRG 1% 91.38 90.18 79.46 81.46 80.46 90.37 5% 85.47 90.05 68.59 75.82 78.58 89.46 10% 84.16 75.80 44.76 53.01 72.91 85.45 15% 69.05 58.37 40.21 42.57 60.55 83.14 20% 54.48 49.26 39.65 40.95 51.54 82.68 25% 45.65 55.84 31.94 39.67 42.47 78.60 TABLE 5. E-measure (%) of noise image segmentation Mixed noise IRG ASRG Ref [13] JSEG Ref [17] GPRG 1% 93.13 91.57 98.47 95.25 89.89 97.66 5% 92.74 82.16 78.69 91.63 87.15 95.34 10% 85.88 80.05 57.18 63.54 83.26 93.26 15% 81.92 74.83 46.52 60.32 72.74 91.28 20% 78.43 69.98 43.42 58.33 58.48 90.45 25% 69.66 58.27 42.57 50.14 51.78 87.54 From Table 3 to 5, performances of all algorithms are good when 1% mixed noise exists. However, with severer mixed noise, performances of all algorithms are reduced where IRG and algorithm in [13] reduce fast. Noise pixels outside the regions of the salient target in an image in results of IRG and ASRG are clear. GPRG algorithm is the least sensitive to noise because of noise suppression by Gaussian difference pyramid. 5 TIME COMPLEXITY ANALYSIS The time complexity of GPRG algorithm involves two aspects: the time spent to generate Gaussian difference pyramid and the time spent to grow the region. The time complexity of the former is a ∗ W ∗ H ∗ K 2 , where a is the number of images generated by Gaussian difference pyramid, W is the length of the image, His the height of the image, and K is the size of the convolution window set in the case of Gaussian blur. According to the description in part B of Section 3, the time complexity of the latter is the time cost of adding and deleting pixel sets. Bis the number of pixels to be added in region growth, C is the number of pixels to be deleted, the size of Band Cis dynamic and cannot be predicted in advance, so the time complexity of the latter is set as f ( t ) , then the overall time complexity of the algorithm is W ∗ H ∗ K 2 + f ( t ) . For the above reasons, we measured the average program running time of the seven noise test images of the same size in Figure 9, and obtained the program running time data as shown in Table 6. As the noise increases by 5%, 10%, 15%, the program running time of each algorithm is gradually increasing, but the time consumed by the GPRG algorithm is relatively low. TABLE 6. The time consumption of noise image segmentation T IRG ASRG Ref [13] JSEG Ref [17] GPRG 1 3.542 4.547 6.164 6.744 5.806 4.657 2 4.858 4.826 6.581 6.926 6.147 4.813 3 5.024 5.034 6.725 7.012 6.305 5.012 6 CONCLUSION In this paper, we proposed GPRG algorithm which is achieved by automatic seed selection and region growing planning. Seed pixels of the salient target in an image are collected by finding extremums in the images of Gaussian difference pyramid. 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