How to filter white bits in a monochrome image that exceed a specified area?

Question

I have this fancy image of "Brainbow image showing map of neuronal circuits of the mouse cerebral cortex"

and I want to count the number of green flashes so ran k-means on it with 15 clusters and I isolated the two colors that together do the job, but i am left with many green streaks/ tails and edges of yellow flashes.

I was hoping to find some algo that thresholds by area and select only the actual green flashes or maybe another aproach where I would not face this problem. I have used python k-means from sklearn.cluster

Answer 1

You could use color thresholding to isolate the green flashes. The idea is to convert the image to HSV format and define a lower/upper color threshold range. This will give you a binary mask. From here we can do additional processing by performing morphological opening with an elliptical shaped kernel to remove noise and tails. Finally we can find contours and filter using contour area with a defined threshold area to only keep the larger blobs. Here's the result:

Count: 116

Code

import numpy as np
import cv2

# Color threshold
image = cv2.imread('1.jpg')
original = image.copy()
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
lower = np.array([42, 67, 0])
upper = np.array([69, 255, 255])
mask = cv2.inRange(hsv, lower, upper)

# Perform morphological operations
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3,3))
opening = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=1)

# Find contours and filter using contour area
cnts = cv2.findContours(opening, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
count = 0
for c in cnts:
    area = cv2.contourArea(c)
    if area < 5: 
        cv2.drawContours(opening,[c], -1, (0,0,0), -1)
    else:
        count += 1

result = cv2.bitwise_and(original,original,mask=opening)
print('Count: {}'.format(count))

cv2.imshow('mask', mask)
cv2.imshow('opening', opening)
cv2.imshow('result', result)
cv2.waitKey()