--- a
+++ b/scripts/DABanalysis.py
@@ -0,0 +1,95 @@
+import numpy as np
+import os
+from natsort import natsorted
+import pandas as pd
+from skimage.color import rgb2hsv, rgb2gray
+from skimage.filters import gaussian, threshold_triangle
+from skimage.exposure import rescale_intensity
+from PyQt5.QtCore import QThread, pyqtSignal
+from PIL import Image
+
+
+class DabAnalysis(QThread):
+
+    maxcuts = pyqtSignal(int)
+    info = pyqtSignal(str)
+    countChanged = pyqtSignal(int)
+    figures = pyqtSignal()
+    activate = pyqtSignal(bool)
+
+    def __init__(self, path, save=False):
+        super().__init__()
+        self.inputpath = path
+        self.save = save
+        self.threshold = None
+
+    def run(self, debug=False):
+        self.activate.emit(False)
+        analysis = []
+        files = [f for f in natsorted(os.listdir(self.inputpath)) if not f.endswith("Overlay.png")]
+        self.maxcuts.emit(len([f for f in files if f.endswith('.png')]))
+        i = 0
+        for file in files:
+            if file.endswith('.png'):
+                self.info.emit("Analysing "+file)
+                print(file)
+                info = []
+                image = np.array(Image.open(self.inputpath+os.sep+file))[:, :, :3]
+                self.current_image = image[::10, ::10]
+                self.figures.emit()
+                info.append(str(file))
+                info.extend(self.QuantStain(image, filename=file, save=self.save))
+                info.append(self.QuantCore(image, filename=file, save=self.save))
+                analysis.append(info)
+                print(info)
+                self.countChanged.emit(int(i)+1)  # EMIT the loading bar
+            self.info.emit(file + " analysed")
+            i += 1
+        # TODO can make this more lightweight by removing pandas and using csv
+        df = pd.DataFrame(analysis, columns=('CoreName', 'AMTsignal', 'Mean_intensity', 'Standard_Dev_intensity',
+                                             'AMTtissue'))
+        df['AFperAMTT'] = df['AMTsignal']/df['AMTtissue']*100
+        df['Mean_Intensity_perAMTT'] = df['Mean_intensity'] / df['AMTtissue'] * 100
+        df['SD_Intensity_perAMTT'] = df['Standard_Dev_intensity'] / df['AMTtissue'] * 100
+        df.to_excel(self.inputpath+os.sep+'CoreAnalysis_'+str(df['CoreName'][0])[:-6]+'.xlsx')
+        self.info.emit("All Files Analysed - ready")
+        self.activate.emit(True)
+
+    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)
+        else:
+            print("normal threshold")
+            stain = np.logical_and(hue, image_sat > 0.79)
+        # TODO : fix this - sent bug report to Github
+        self.current_image = stain[::10, ::10].astype(float)
+        self.figures.emit()
+        if save:
+            self.info.emit("Saving - " + filename+'_stain.tiff')
+            imagesave = Image.fromarray(stain)
+            imagesave.save(self.inputpath+os.sep+filename+'_stain.tiff')
+        stint = np.copy(image)
+        stint = rgb2gray(stint)
+        stint = rescale_intensity(stint, out_range=(0, 255))
+        stint[stain == 0] = 0  # array with only the stained pixels
+        stint = np.ravel(stint)
+        stint = stint[stint != 0]
+        stint_mean = stint.mean()
+        stint_std = stint.std()
+        stained = np.sum(stain)
+        return stained, stint_mean, stint_std
+
+    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