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How to separate images using watershed algorithm in Python


By : John Glenn
Date : September 15 2020, 12:00 PM
fixed the issue. Will look into that further How to separate indiviual images among multiple images after image segmentaion using watershed algorithm in Python The attached image is consists of 4 images , from which we need to apply image segmentation and separate individual image from those 4 images , We will flood fill it first
code :
import cv2;
import numpy as np;

# Read image
im_in = cv2.imread("2SNAT.jpg", cv2.IMREAD_GRAYSCALE);

# Threshold.
# Set values equal to or above 220 to 0.
# Set values below 220 to 255.

th, im_th = cv2.threshold(im_in, 220, 255, cv2.THRESH_BINARY_INV);

# Copy the thresholded image.
im_floodfill = im_th.copy()

# Mask used to flood filling.
# Notice the size needs to be 2 pixels than the image.
h, w = im_th.shape[:2]
mask = np.zeros((h+2, w+2), np.uint8)

# Floodfill from point (0, 0)
cv2.floodFill(im_floodfill, mask, (0,0), 255);

# Invert floodfilled image
im_floodfill_inv = cv2.bitwise_not(im_floodfill)

# Combine the two images to get the foreground.
im_out = im_th | im_floodfill_inv
im, contours, hierarchy = cv2.findContours(im_out.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

final_contours = []
for contour in contours:
    area = cv2.contourArea(contour)
    if area > 1000:
        final_contours.append(contour)
counter = 0        
for c in final_contours:
    counter = counter + 1
# for c in [final_contours[0]]:
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.01 * peri, True)
    x,y,w,h = cv2.boundingRect(approx)
    print(x, y, w, h)
    aspect_ratio = w / float(h)

    if (aspect_ratio >= 0.8 and aspect_ratio <= 4):
        cv2.rectangle(im_in,(x,y),(x+w,y+h),(0,255,0),2)
        cv2.imwrite('splitted_{}.jpg'.format(counter), im_in[y:y+h, x:x+w])
cv2.imwrite('rectangled_split.jpg', im_in)


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watershed algorithm in matlab


By : user3298874
Date : March 29 2020, 07:55 AM
this one helps. Here's one way to segment the image using watershed. There's plenty more you could do (e.g. fuse cells with two nuclei if they haven't completed cytokinesis yet), but the steps below should give you a first idea.
(1) Determine cell-background threshold, cell-nucleus threshold
code :
%# read image
img = imread('http://i.stack.imgur.com/nFDkX.png');
%# normalize to 0...1
imgN = double(img-min(img(:)))/(max(img(:)-min(img(:))));
th1=graythresh(imgN);
th2 = graythresh(imgN(imgN>th1));

cellMsk = imgN>th1;
nucMsk = imgN>th2;

figure,imshow(cellMsk+nucMsk,[])
[xx,yy]=ndgrid(-5:5,-5:5);
gf = exp((-xx.^2-yy.^2)/20);
filtImg = conv2(imgN,gf,'same');

figure,imshow(filtImg,[])

filtImgM = imimposemin(-filtImg,nucMsk);
ws = watershed(filtImgM);
ws(~cellMsk) = 0;

lblImg = bwlabel(ws);

figure,imshow(label2rgb(lblImg,'jet','k','shuffle'));

Image Segmentation, watershed, waterfall, p algorithm


By : user3429132
Date : March 29 2020, 07:55 AM
Hope that helps For the sake of completeness, an answer regarding the Hierarchical segmentation was posted on the ImageJ mailing list by Ignacio Arganda-Carreras. He implemented it on an experimental branch of MorphoLibJ.
All credits go to Ignacio!

Extracting contours after watershed algorithm


By : Ken Krutsch
Date : March 29 2020, 07:55 AM
help you fix your problem Found a way to do it! Contours after watershed algorithm This solves my problem.

Watershed algorithm – CT Lung segmentation


By : Golnar Ashkboos
Date : March 29 2020, 07:55 AM
Does that help I am searching for source code to segment Lung in CT images using watershed algorithm. Please let me know if you find any website / source code. , Relevant algorithms:

Over-segmentation of Watershed algorithm


By : Richard Hayden
Date : March 29 2020, 07:55 AM
it helps some times Use max(abs(x1-x2),abs(y1-y2)) as the distance metric (chessboard), and use eight-connected neighborhood in watershed function:
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