--- a
+++ b/docs/Measurements.rst
@@ -0,0 +1,54 @@
+.. _Measurements_page:
+
+***********************************
+Measurements as performed in python
+***********************************
+
+Amount of Tissue
+================
+
+.. code-block:: python
+
+    def QuantCore(self, image, filename, save=False):
+        image = gaussian(rgb2gray(image), sigma=2)
+        thresh = threshold_triangle(image[image > 0])
+        binary = np.logical_and(image < thresh, image > 0)
+        wholeCore = np.sum(binary)
+        if save:
+            self.info.emit("Saving - " + filename + '_core.tiff')
+            imagesave = Image.fromarray(binary)
+            imagesave.save(self.inputpath+os.sep+filename + '_core.tiff')
+        return wholeCore
+
+
+Amount of Signal
+================
+
+.. code-block:: python
+
+    def QuantStain(self, image, filename, save=False):
+        img_hsv = rgb2hsv(image)
+        img_hue = img_hsv[:, :, 0]
+        image_sat = img_hsv[:, :, 1]
+        hue = np.logical_and(img_hue > 0.02, img_hue < 0.10)  # BROWN PIXELS BETWEEN 0.02 and 0.10
+        if self.threshold:
+            stain = np.logical_and(hue, image_sat > self.threshold)  # USER DEFINED MINIMUM SATURATION THRESHOLD
+        else:
+            print("Defult threshold")
+            stain = np.logical_and(hue, image_sat > 0.79)  #  DEFAULT SATURATION THRESHOLD APPLIED IF NO USER INPUT
+        self.current_image = stain[::10, ::10].astype(float)  # SHOW A LOW RESOLUTION MASK AS A FIGURE
+        self.figures.emit()
+        if save:
+            self.info.emit("Saving - " + filename+'_stain.tiff')  # SAVE A FIGURE IF REQUIRED
+            imagesave = Image.fromarray(stain)
+            imagesave.save(self.inputpath+os.sep+filename+'_stain.tiff')
+        stint = np.copy(image)
+        stint = exposure.rescale_intensity(stint, out_range=(0, 255))
+        stint[stain == 0] = 0  # array with only the stained pixels
+        stint = color.rgb2gray(stint) # convert to greyscale
+        stint = np.ravel(stint) # flatten image array
+        stint = stint[stint != 0] # remove all zeros
+        stint_mean = stint.mean()
+        stint_std = stint.std()
+        stained = np.sum(stain)
+        return stained, stint_mean, stint_std