Gaussian Mixture Models of an Image's Histogram

Question

I am attempting to do automatic image segmentation of the different regions of a 2D MR image based on pixel intensity values. The first step is implementing a Gaussian Mixtu

Answer 1

The issue was with passing the histogram rather than the array of pixel intensities to GaussianMixture.fit gmm = gmm.fit(hist). I also found that a minimum of n_components = 6 is needed to visually fit this particular histogram.

import numpy as np
import cv2
import matplotlib.pyplot as plt
from sklearn.mixture import GaussianMixture

# Read image
img = cv2.imread("test.jpg",0)

hist = cv2.calcHist([img],[0],None,[256],[0,256])
hist[0] = 0     # Removes background pixels

data = img.ravel()
data = data[data != 0]
data = data[data != 1]  #Removes background pixels (intensities 0 and 1)

# Fit GMM
gmm = GaussianMixture(n_components = 6)
gmm = gmm.fit(X=np.expand_dims(data,1))

# Evaluate GMM
gmm_x = np.linspace(0,253,256)
gmm_y = np.exp(gmm.score_samples(gmm_x.reshape(-1,1)))


# Plot histograms and gaussian curves
fig, ax = plt.subplots()
ax.hist(img.ravel(),255,[2,256], normed=True)
ax.plot(gmm_x, gmm_y, color="crimson", lw=4, label="GMM")

ax.set_ylabel("Frequency")
ax.set_xlabel("Pixel Intensity")

plt.legend()

plt.show()