--- a
+++ b/tissue_detection.py
@@ -0,0 +1,40 @@
+import numpy as np
+import cv2
+
+
+def tissue_detection(img, remove_top_percentage=0.2):
+
+    assert 0 <= remove_top_percentage < 1, (f"remove_top_percentage needs to be in [0, 1). You passed "
+                                            f"{remove_top_percentage}.")
+
+    kernel_size = 3
+
+    # remove alpha channel
+    img = img[:, :, 0:3]
+
+    top_border = int(len(img)*remove_top_percentage)
+    # hack for removing border artifacts
+    img[0:top_border, :, :] = [0, 0, 0]
+
+    # remove black background pixel
+    black_px = np.where((img[:, :, 0] <= 5) & (img[:, :, 1] <= 5) & (img[:, :, 2] <= 5))
+    img[black_px] = [255, 255, 255]
+
+    # apply median filter to remove artifacts created by transitions to background pixels
+    median_filtered_img = cv2.medianBlur(img, 11)
+
+    # convert to HSV color space
+    hsv_image = cv2.cvtColor(median_filtered_img, cv2.COLOR_RGB2HSV)
+
+    # get saturation channel
+    saturation = hsv_image[:, :, 1]
+
+    # Otsu's thresholding
+    _, threshold_image = cv2.threshold(saturation, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+
+    # apply dilation to image to close spots inside mask regions
+    kernel = np.ones(shape=(kernel_size, kernel_size))
+    tissue_mask = cv2.dilate(threshold_image, kernel, iterations=1)
+    # tissue_mask = cv2.erode(tissue_mask, kernel)
+
+    return tissue_mask