Image Segmentation. Figure 1: Input image. Step.2. Use Morphological Opening to Estimate the Background

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1 Image Segmentation Image segmentation is the process of dividing an image into multiple parts. This is typically used to identify objects or other relevant information in digital images. There are many different ways to perform image segmentation, including: 1- Thresholding methods such as Otsu s method 2- Color-based Segmentation such as K-means clustering 3- Transform methods such as watershed segmentation 4- Texture methods such as texture filters First method: Thresholding method: This example shows how to correct nonuniform illumination in an image to make it easy to identify individual grains of rice in the image. You can then learn about the characteristics of the grains and easily compute statistics for all the grains in the image. Step. 1. Load Images %load image I = imread('rice.png'); % show the image imshow(i) Figure 1: Input image Step.2. Use Morphological Opening to Estimate the Background Notice that the background illumination is brighter in the center of the image than at the bottom. Use imopen to estimate the background illumination. background = imopen(i,strel('disk',15)); % Display the Background Approximation as a Surface 1

2 surf(double(background(1:8:end,1:8:end))),zlim([0 255]); ax = gca; ax.ydir = 'reverse'; Step.3. Subtract the Background Image from the Original Image I2 = I - background; imshow(i2) Note that step 2 and step 3 together could be replaced by a single step using imtophat which first calculates the morphological opening and then subtracts it from the original image. I2 = imtophat(i,strel('disk',15)); Step.4. Increase the Image Contrast I3 = imadjust(i2); imshow(i3); 2

3 Step.5. Threshold the Image Create a new binary image by thresholding the adjusted image. Remove background noise with bwareaopen. bw = imbinarize(i3); bw = bwareaopen(bw, 50); imshow(bw) Step.6. Identify Objects in the Image The function bwconncomp finds all the connected components (objects) in the binary image. The accuracy of your results depend on the size of the objects, the connectivity parameter (4,8,or arbitrary), and whether or not any objects are touching (in which case they may be labeled as one object). Some of the rice grains in bw are touching. cc = bwconncomp(bw, 4) Step.7. Examine One Object Each distinct object is labeled with the same integer value. Show the grain that is the 50th connected component. 3

4 grain = false(size(bw)); grain(cc.pixelidxlist{50}) = true; imshow(grain); Step.8. View All Objects One way to visualize connected components is to create a label matrix and then display it as a pseudo-color indexed image. Use labelmatrix to create a label matrix from the output of bwconncomp. Note that labelmatrix stores the label matrix in the smallest numeric class necessary for the number of objects. labeled = labelmatrix(cc); whos labeled In the pseudo-color image, the label identifying each object in the label matrix maps to a different color in the associated colormap matrix. Use label2rgb to choose the colormap, the background color, and how objects in the label matrix map to colors in the colormap. RGB_label = 'c', 'shuffle'); imshow(rgb_label) 4

5 Step.9. Compute Area of Each Object Each rice grain is one connected component in the cc structure. Use regionprops on cc to get the area. graindata = regionprops(cc,'basic') To find the area of the 50th component, use dot notation to access the Area field in the 50th element of graindata structure array. graindata(50).area Step.10. Compute Area-based Statistics Create a new vector grain_areas, which holds the area measurement for each grain. grain_areas = [graindata.area]; Find the grain with the smallest area: [min_area, idx] = min(grain_areas) grain = false(size(bw)); grain(cc.pixelidxlist{idx}) = true; imshow(grain); Step.11. Create Histogram of the Area histogram(grain_areas) title('histogram of Rice Grain Area'); 5

6 Second method: Marker-Controlled Watershed Segmentation This example shows how to use watershed segmentation to separate touching objects in an image. The watershed transform is often applied to this problem. The watershed transform finds "catchment basins" and "watershed ridge lines" in an image by treating it as a surface where light pixels are high and dark pixels are low. Segmentation using the watershed transform works better if you can identify, or "mark," foreground objects and background locations. Marker-controlled watershed segmentation follows this basic procedure: 1. Compute a segmentation function. This is an image whose dark regions are the objects you are trying to segment. 2. Compute foreground markers. These are connected blobs of pixels within each of the objects. 3. Compute background markers. These are pixels that are not part of any object. 4. Modify the segmentation function so that it only has minima at the foreground and background marker locations. 5. Compute the watershed transform of the modified segmentation function. This example highlights many different Image Processing Toolbox functions, including fspecial, imfilter, watershed, label2rgb, imopen, imclose, imreconstruct, imcomplement, imregionalmax, bwareaopen, graythresh, and imimposemin. Step.1. Read in the Color Image and Convert it to Grayscale rgb = imread('pears.png'); I = rgb2gray(rgb); imshow(i) text(732,501,'image courtesy of Corel(R)',... 'FontSize',7,'HorizontalAlignment','right') 6

7 Step.2. Use the Gradient Magnitude as the Segmentation Function Use the Sobel edge masks, imfilter, and some simple arithmetic to compute the gradient magnitude. The gradient is high at the borders of the objects and low (mostly) inside the objects. hy = fspecial('sobel'); hx = hy'; Iy = imfilter(double(i), hy, 'replicate'); Ix = imfilter(double(i), hx, 'replicate'); gradmag = sqrt(ix.^2 + Iy.^2); imshow(gradmag,[]), title('gradient magnitude (gradmag)') L = watershed(gradmag); Lrgb = label2rgb(l);, imshow(lrgb), title('watershed transform of gradient magnitude (Lrgb)') 7

8 Without additional preprocessing such as the marker computations below, using the watershed transform directly often results in "oversegmentation." Step.3. Mark the Foreground Objects A variety of procedures could be applied here to find the foreground markers, which must be connected blobs of pixels inside each of the foreground objects. In this example you'll use morphological techniques called "opening-by-reconstruction" and "closing-by-reconstruction" to "clean" up the image. These operations will create flat maxima inside each object that can be located using imregionalmax. Opening is an erosion followed by a dilation, while opening-by-reconstruction is an erosion followed by a morphological reconstruction. Let's compare the two. First, compute the opening using imopen. se = strel('disk', 20); Io = imopen(i, se); imshow(io), title('opening (Io)') 8

9 Next compute the opening-by-reconstruction using imerode and imreconstruct. Ie = imerode(i, se); Iobr = imreconstruct(ie, I); imshow(iobr), title('opening-by-reconstruction (Iobr)') Following the opening with a closing can remove the dark spots and stem marks. Compare a regular morphological closing with a closing-by-reconstruction. First try imclose: Ioc = imclose(io, se); imshow(ioc), title('opening-closing (Ioc)') Now use imdilate followed by imreconstruct. Notice you must complement the image inputs and output of imreconstruct. Iobrd = imdilate(iobr, se); Iobrcbr = imreconstruct(imcomplement(iobrd), imcomplement(iobr)); Iobrcbr = imcomplement(iobrcbr); imshow(iobrcbr), title('opening-closing by reconstruction (Iobrcbr)') 9

10 As you can see by comparing Iobrcbr with Ioc, reconstruction-based opening and closing are more effective than standard opening and closing at removing small blemishes without affecting the overall shapes of the objects. Calculate the regional maxima of Iobrcbr to obtain good foreground markers. fgm = imregionalmax(iobrcbr); imshow(fgm), title('regional maxima of opening-closing by reconstruction (fgm)') To help interpret the result, superimpose the foreground marker image on the original image. I2 = I; I2(fgm) = 255; imshow(i2), title('regional maxima superimposed on original image (I2)') 10

11 Notice that some of the mostly-occluded and shadowed objects are not marked, which means that these objects will not be segmented properly in the end result. Also, the foreground markers in some objects go right up to the objects' edge. That means you should clean the edges of the marker blobs and then shrink them a bit. You can do this by a closing followed by an erosion. se2 = strel(ones(5,5)); fgm2 = imclose(fgm, se2); fgm3 = imerode(fgm2, se2); This procedure tends to leave some stray isolated pixels that must be removed. You can do this using bwareaopen, which removes all blobs that have fewer than a certain number of pixels. fgm4 = bwareaopen(fgm3, 20); I3 = I; I3(fgm4) = 255; imshow(i3) title('modified regional maxima superimposed on original image (fgm4)') 11

12 Step.4. Compute Background Markers Now you need to mark the background. In the cleaned-up image, Iobrcbr, the dark pixels belong to the background, so you could start with a thresholding operation. bw = imbinarize(iobrcbr); imshow(bw), title('thresholded opening-closing by reconstruction (bw)') The background pixels are in black, but ideally we don't want the background markers to be too close to the edges of the objects we are trying to segment. We'll "thin" the background by computing the "skeleton by influence zones", or SKIZ, of the foreground of bw. This can be done by computing the watershed transform of the distance transform of bw, and then looking for the watershed ridge lines (DL == 0) of the result. D = bwdist(bw); DL = watershed(d); bgm = DL == 0; imshow(bgm), title('watershed ridge lines (bgm)') 12

13 Step.5. Compute the Watershed Transform of the Segmentation Function The function imimposemin can be used to modify an image so that it has regional minima only in certain desired locations. Here you can use imimposemin to modify the gradient magnitude image so that its only regional minima occur at foreground and background marker pixels. gradmag2 = imimposemin(gradmag, bgm fgm4); Finally we are ready to compute the watershed-based segmentation. L = watershed(gradmag2); Step 6: Visualize the Result One visualization technique is to superimpose the foreground markers, background markers, and segmented object boundaries on the original image. You can use dilation as needed to make certain aspects, such as the object boundaries, more visible. Object boundaries are located where L == 0. I4 = I; I4(imdilate(L == 0, ones(3, 3)) bgm fgm4) = 255; imshow(i4) title('markers and object boundaries superimposed on original image (I4)') This visualization illustrates how the locations of the foreground and background markers affect the result. In a couple of locations, partially occluded darker objects were merged with their brighter neighbor objects because the occluded objects did not have foreground markers. 13

14 Another useful visualization technique is to display the label matrix as a color image. Label matrices, such as those produced by watershed and bwlabel, can be converted to truecolor images for visualization purposes by using label2rgb Lrgb = label2rgb(l, 'jet', 'w', 'shuffle'); imshow(lrgb) title('colored watershed label matrix (Lrgb)') You can use transparency to superimpose this pseudo-color label matrix on top of the original intensity image. imshow(i) hold on himage = imshow(lrgb); himage.alphadata = 0.3; title('lrgb superimposed transparently on original image') 14

15 Exercise: 1. Use your own image and implement both methods for image segmentation. Give title to each image as your student ID and save all the images. 2. Take an image of Thai baht coins using your mobile and apply these two methods. State if it is possible to count the money from the final results, and which method gives better results for this problem? Assignment: You are to implement the third and fourth method i.e.; Color-based Segmentation such as K-means clustering and Texture Segmentation Using Texture Filters by yourself. The report should contain: a). Include a comment for every function saying why that particular function was needed. b). An image showing the final segmentation result and its histogram. Give tile as of your student ID. c) save the images to your directory using imwirte. 15

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