Color Image Segmentation to the RGB and HSI Model Based on Region Growing Algorithm

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1 Color Image Segmentation to the RGB and HSI Model Based on Region Growing Algorithm YAS A. ALSULTANNY College of Graduate Studies Arabian Gulf University Manama, Kingdom of Bahrain Abstract: - Image segmentation is to divide the image into disjoint homogenous or classes, where all the pixels in the same class must have some common characteristics. In this paper, a new algorithm proposed to segment in an image, which is based on determining seed in an image. The algorithm applied on images of different complexity. The segmentation applied on each of the RGB images, and also tested on the HSI images, the results showed that, we can obtain good segmentation by processing the intensity of the HSI model, which gave similar results as that obtained in the RGB model. The results also showed the relationship between the number of dilation processing to close the and the number of that can be segmented, good result obtained with the number of dilation equal 2, this obtained experimentally, also the results showed the best value to expand pixel neighbor is from 1 to 4, after that the image will be degraded. Key-Words: - Image segmentation, seed region, dilation, RGB model, HSI model. 1 Introduction Segmentation is a process in which an image is divided into different in order to isolate the areas of interest on it. Segmentation enables us, to obtain a high level of knowledge [1]. Color image segmentation is a process of extracting from the image domain one or more connected satisfying uniformity criterion which is based on features derived from spectral component. These components are defined in a chosen color space model. The segmentation process could be augmented by some additional knowledge about object in the scene [2]. An RGB color image is an M*N*3 array of color pixels, where each color pixel is a triplet corresponding to the red, green and blue components of an RGB image at specific spatial location. An RGB image may be viewed as a "stack" of three gray-scale images that, when fed into the red, green and blue inputs of color monitor, produced a color image on the screen [3, 4]. In image segmentation the problem of developing automatic segmentation procedures has always been and still of great interest. The existing automatic segmentation methods are generally associated with some particular applications where 'a priori' information about the result of the segmentation is known [5, 6]. Region-based segmentation algorithms postulate that neighboring pixels within the same region have similar intensity values, of which the split-and-merge technique is probably the most well known. The general procedure is to compare a pixel with its immediate surrounding neighbors [7, 8]. Some region growing methods use edge as a growth-stopping condition, and the growing seeds are selected manually. The growth occurs in the homogenous intensity and stops at the edges. If there are broken edges then the segmentation is incorrect [9, 10]. ed Region Growing algorithm (SRG) is an approach which is based on conventional postulate of region growing algorithms, where the criteria of similarity of pixels is applied, but the mechanism of growing is closer to the watershed algorithm. Instead of controlling region growing by tuning homogeneity parameters, SRG is controlled by choosing a usually small number of pixels, known as seeds. These seed pixels are chosen in order to extract the, or these seed pixels are chosen automatically [11, 12, 13, 14 and 15]. 2 Structures of Image Bands Every image is represented by color models which is a specification of a coordinate system and subspace within that system, where each color is represented by a single point. There are common colors models used today, which are: RGB (Red, Green, Blue), CMY (Cyan, Magenta, Yellow), CMYK (Cyan, ISSN: ISBN:

2 Magenta, Yellow, black) and HSI (Hue, Saturation, Intensity) [16]. The RGB and HSI models will be used in this paper. 3 Color Conversions from RGB to HSI Model To convert image from RGB model to HSI model, the image should be normalized to the range [0, 1]. By applying the following equations [13]: H=θ if B G and H=360-θ if B>G (1) with θ=cos -1 (0.5[(R-G)+(R-B)]/[(R-G) 2 +(R-B)(G-B)] 1/2 ) (2) S=1-3*([min(R,G,B)]/(R+G+B)) (3) I=(R+G+B)/3 (4) Where: R:Red, G:Green, B:Blue. H: Hue, S: Saturation, I: Intensity Band. 4 Region Growing Algorithm by using The region growing algorithm usually used seed point to grow the. The seed point determined either manually (by user) or automatically. Fig.1 shows the block diagram of the proposed algorithm, to determine the seed region. The following is the region growing steps by using the seed : Step1: Split the RGB s of the image as shown in Fig.2, each has different appearance from the other. a- original image b- Red c- Green d- Blue Fig.2: Splitting the three s of the RGB image Step2: Apply Canny mask to each to determine edges, as shown in Fig.3, experimentally the canny mask gave us the best results of edge detection compared with the other methods of edge detection. 1 Step Split the three s of the (RGB) image Step 2 Step 3 Step 4 Step 5 Step 6 Step 7 Canny mask applied on the selected to obtain edges Closing area by dilation Determine the closed areas in the image (seed ) Determine the maximum and the minimum intensity values for every seed region Re-arrange seed from the largest to the smallest seed Merge seed region with the neighbor pixels region. (a) (b) (c) a- edges of the Red to the in Fig.(2-b). b- edges of the Green to the in Fig.(2-c). c- edges of the Blue to the in Fig.(2-d). Fig.3: The three s gave different results by applying Canny mask. Step3: Apply dilation operation to connect areas as shown in Fig.4. The Canny mask is not always joining edges with others, so the dilatation operation joins edges of the areas and delete any disconnect pixels on the edges [11]. Step 8 Delete pixels which are found in more than one region from the smallest Step 9 Color the Step 10 Repeat the previous steps to the other s Fig.1: Region growing steps by seed (a) (b) (c) a- applying dilation to the image in Fig.(3 -a). b- applying dilation to the image in Fig.(3 -b). c- applying dilation to the image in Fig.(3 -c). Fig.4: Edge detection Dilation to the three s ISSN: ISBN:

3 Step4: Split each closed area in a separate image, as shown in Fig.5, these are called seed, the number of seed are (15), which produced (15) seed region images, to the red image in Fig.4-a. R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 Fig.5: of red in Fig. (4-a), marked from R1 to R15. Step5: Find the maximum intensity value and the minimum intensity value for every seed region to the image in Fig.5. Step6: Re-arrange seed from the largest area seed region to the smallest area seed region as shown in Fig.6, this step is important for region growing processing. Because there are pixels may be set in two or more region by re-arrange, the algorithm sort them according to the largest region always. maximum intensity value of seed region, and minus from left bound of interval, which represent the minimum intensity value of the seed region. Fig.7-a represent an array of intensity values which has a seed region marked by black border line and this array represent a of image, merge seed region are done by the following steps: Mark every seed region pixels by 1 otherwise mark pixel by 0, as shown in Fig.7-b. Check the (8) pixels neighbor, which is marked by 0, if the corresponding intensity value is less than or equal the seed region and greater than or equal the (minimum intensity value - value of expand interval) of seed region then merge this pixel and mark it by 1 otherwise mark it by 0. Repeat step b until there is no pixels marked by 1. The value of the seed region is 221, the algorithm merge pixels 221±2 as shown in Fig.7-c. Fig.8 shows the result of merging process for region R6. Step8: Delete every pixel which sets in more than one region from all except the largest one. Because when the algorithm merges seed and pixels may be set pixel in more than one region. Step9: Color every region by specific color. (a) (b) Border of Region R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 Fig.6: Re-arrange the images in Fig.5 Step7: Merge seed region with every neighbor pixels which it s intensity between the (maximum intensity value+value of expand interval) and (minimum intensity value-value of expand interval). The value of expand interval is a constant value that is added to right bound of interval, which represent the a- Initial seed region with true values of the pixels. b- Initial seed region. c- The result of merging seed region after iteration. Fig.7: Merge seed region with neighboring pixels. (c) a- before merge b- after merge Fig.8: Difference between region before and after merge process, for region R6. ISSN: ISBN:

4 5 Implementation and Results Image segmentation applied to the two coordinates the RGB and HSI images. 5.1 RGB Image Segmentation The algorithm applied to the single enhanced images (enhancement used as a preprocessing before segmentation), the algorithm gave good result and it could find the noisy pixels which are colored by the black color, as shown in the Fig.9. image as shown in Fig.11; the algorithm shows that it also gave good result to the complex image. (a) (a) (b) (c) (d) (b) (c) (d) (e) (f) Noisy pixels a- Original image b- Red c- Green d- Blue e- Segmentation to R. f- Segmentation to G. g- Segmentation to B. Fig.9: Segmentation to the three s of the image Fig.10, shows processing the map of Africa by applying the same algorithm, which also shows good result. There are some colored with black color, because the size of these are very small, and the dilation process can not applied, because they are originally closed region area, and we can not determine a border to these. (g) (e) (f) (g) a- Original image. b- Red image. c- Green image. d- Blue image. e- Segmentation to R. f- Segmentation to G. g- Segmentation to B. Fig.11: Segmentation to the photo image. The medical images also tested; Fig.12 shows the brain image before and after segmentation. Experimentally most medical images s of the RGB model are similar, so we show only one of the brain image. Original image. Segmentation of Red. Fig.12: Brain image segmentation. (a) (b) (c) (d) (e) (f) (g) a- Original image. b- Red image. c- Green image. d- Blue image. e- Segmentation to R. f- Segmentation to G. g- Segmentation to B. Fig.10: Segmentation to the Africa map The algorithm was tested by using the photo 5.2 HSI Image Segmentation The photo image in Fig.11, is converted to the HSI model, Fig.13-b, 13-c, and 13-d, represent the three s of HSI model hue, saturation and intensity s respectively, images in Fig. 13-e, 13-f, and 13-g, represent the image segmentation to these three s of the HSI model. The intensity gave good segmentation, but the hue and saturation gave bad segmentation, this proves that almost all the information can be founded in the intensity. The HSI can be used instead of RGB to reduce the time of processing; we can only process the intensity to obtain good result. ISSN: ISBN:

5 (a) (b) (c) (d) some of the will be connected with the other, when the number of dilation equal 2. It gave the maximum value of the colored, which are the best results. Table 2 show the relationship between the value of the expand interval, the number of initial seed, and the number of the colored region to the segmented image. Table 2: Relationship between the expand interval, no. of seed region, no. of colored region Expand interval Value in R in G in B R G B (e) (f) (g) a- Original image in HSI model b- Hue. c- Saturation. d- Intensity. e-segmentation to H. f-segmentation to S g- Segmentation to I. Fig.13: Segmentation to the HIS model. 6 Comparison between RGB and HSI segmentation Table 1 shows the relationship between the number of dilation operation, the number of initial seed and the number of colored region to the segmented image. Table 1 shows the values obtained by applying the algorithm to the image in Fig.11, when the number of dilation operation changed from 1 to 5, experimentally when the dilation applied more than 5 times, then the number of the initial seed region will be decremented. Table 1: Relationship between no. of dilation, no. of seed region and no. of colored region based on RGB No. of dilation in R in G in B R G B The number of the colored in the three s of the RGB model depends on the number of dilation, these are increased until they arrive a suitable value, which produce the maximum number of colored, after that the number of are decremented, because when the number of dilation is small the border of the are still disconnected, and they have a gaps between them, where the number of dilation increased, the border of the are connected together and split the into the colored. But when the number of dilation operation increased, then the number of the colored is decreased, because The expanded interval (in pixel) in Table 2 from 0 to 5, where 0 means the interval is not expanded and 1 means the interval is expanded from the right by 1 and from left by 1, and so on. Tables 2 obtained when the dilation operation is fixed by 2 and applied to the photo image in Fig.11. Results showed that when the expand interval increased the colored are decreased, which means that it eliminate some, which cause the degradation to the result of segmentation, because some of the neighbor are merged together and caused this degradation. Experimentally the best segmentation obtained when the expand interval between 1 and 4. Table 3 shows the relationship between the number of dilation operation, the number of the seed and the number of the colored to the image in Fig.13. Table 3: Relationship between no. of dilation, no. of seed region, and no. of colored region. Number of dilation in H in S in I H S Table 4 shows the relationship between the values of the expand interval, the number of seed, and the number of colored to the segmented image based on HSI model. The values in Table 4 are gathered from the image in Fig.13, but at this time the number of dilation was 2 and the value of expand interval are changed from 1 to 5. I ISSN: ISBN:

6 Table 4: Relationship between the expand interval, no. of seed region and no. of colored region. Expand interval Values in H in S in I H S I Conclusions Segmentation is the process of dividing images into different, in order to isolate the selected objects of the processed image. The proposed algorithm tested on images of different complexity, with different image coordinates. The RGB and HSI image coordinate are used. The results showed that as shown in Fig.15, the curve of Intensity is near to the curves of Red, Green and Blue of RGB model, but the curve of Hue and saturation of the HSI model are too far from the other curves. So if the values of the expand interval are changed from 0 to 5 then the results of the image segmentation of the HSI model will be similar to the results by processing the image by using RGB model, and the relationship between the quality and number of dilation by using HSI model is similar to the relationship between the number of dilation and the quality by using RGB model except the Hue and Saturation. Fig.15: relationship between the expand intervals, and the no. of the color region to the RGB and HSI. At last we can conclude that we can use the intensity of the HSI, in segmentation which reduced the time of processing instead of processing the three s of the RGB model, and the suitable values of the number of dilation and expand interval between (1 and 5) to the two types of the RGB and HSI models. References: [1] Fondon I., Serrano C. and Acha B., Color Image Segmentation based on Multitolerance Region Growing, Proceedings of 4 th IASTED International Conference Visualization, Imaging, and Imaging Processing, Spain, pp , [2] Gonzalez R., Richard E. Woods and Steven L. Eddins, Digital Image Processing Using Matlab: 2 nd edition, Prentice Hall, [3] Sharbek W. and Koschan A., Color Image Segmentation A Survay, Franklinstrasse 28/29, 10587, Berlin, Germany, [4] Jung, R., Unsupervised Multiscale Segmentation of Color Images. Pattern Recognition Letters, 28(4): pp , [5] Cramariuc B., Gabbouj M. and Astola J., Clustering based Region Growing Algorithm for Color Image Segmentation, IEEE, DSP, pp , [6] Lukáč P., Benčo M., Hudec R., and Dubcová Z., Color Image Segmentation for Retrieval in Large Image Databases, Conference Transcom, June [7] Lin Z., Jin J. and Talbot H., Unseeded Region Growing For #D Image Segmentation: Australian Computer Society Inc, [8] Kang D., Park S., Shin G., Yo W., Jang S., Image Segmentation using Statistical Approach via Perception-based Color Information, IJCSNS International Journal of Computer Science and Network Security, Vol.8, No.4, pp.41-47, [9] Fan X. and Samur M., Edge based Region Growing a new Image Segmentation Method, the Association for Computing Machinery Inc. ACM: , [10] Leibe B., Leonardis A., and Schiele B.: Robust, Object Detection with Interleaved Categorization and Segmentation. IJCV 77(3), [11] Gonzalez C. and Woods. E., Digital Image Processing. 2 nd edition: Prentice Hall, [12]Ikonomakis N., Zervakis M. and Venetsanopoul. N., Region Growing and Region Merging Image Segmentation, IEEE, DSP 97: pp , [13] Ikonomakis N., Plataniotis N., and Venetsanopoulos N., Unsupervised Determination for a Region-based Color Image Segmentation Scheme, IEEE: , pp [14] Wesolkowski S. and Fieguth P., Color Image Segmentation using Connected, IEEE: pp , [15] Cheng C., Region Growing Approach to Color segmentation using 3-D Clustering and Relaxation labeling, IEEE Proceedings of Visual Image Signal Processing, pp , [16] Jie X. and Pengzfei S., Natural color Image Segmentation, IEEE: , ISSN: ISBN: