diff --git a/content/otsu.md b/content/otsu.md
index 0222824e..c76d4226 100644
--- a/content/otsu.md
+++ b/content/otsu.md
@@ -34,13 +34,13 @@ Otsu's method works by maximizing the **between class variance** σB
with
-P~1~(θ) = $\sum_{i = 0}^{\theta} h(i)$ (≙ number of pixels below the threshold (background))
+P1(θ) = $\sum_{i = 0}^{\theta} h(i)$ (≙ number of pixels below the threshold (background))
-P~2~(θ) = 1 - P~1~(θ) = $\sum_{i = \theta +1}^{L-1} h(i)$ (≙ number of pixels above the threshold (foreground))
+P2(θ) = 1 - P1(θ) = $\sum_{i = \theta +1}^{L-1} h(i)$ (≙ number of pixels above the threshold (foreground))
-μ~1~(θ) = $\frac{1}{P1(\theta)}$ $\cdot$ $\sum_{i = 0}^{\theta} (i+1)h(i)$ (≙ mean intensity of the background)
+μ1(θ) = $\frac{1}{P1(\theta)}$ $\cdot$ $\sum_{i = 0}^{\theta} (i+1)h(i)$ (≙ mean intensity of the background)
-μ~2~(θ) = $\frac{1}{P2(\theta)}$ $\cdot$ $\sum_{i = \theta +1}^{L-1} (i+1)h(i)$ (≙ mean intensity of the foreground)
+μ2(θ) = $\frac{1}{P2(\theta)}$ $\cdot$ $\sum_{i = \theta +1}^{L-1} (i+1)h(i)$ (≙ mean intensity of the foreground)
with __h(i)__ being the normalized histogram of the image, __θ__ being the current threshold and __L__ being the length of the histogram-array.