diff --git a/__pycache__/lumen_profiler.cpython-313.pyc b/__pycache__/lumen_profiler.cpython-313.pyc
index b550c01..93eaeeb 100644
--- a/__pycache__/lumen_profiler.cpython-313.pyc
+++ b/__pycache__/lumen_profiler.cpython-313.pyc
Binary files differ
diff --git a/gui_app.py b/gui_app.py
index 140eb8b..e0073d6 100644
--- a/gui_app.py
+++ b/gui_app.py
@@ -44,6 +44,11 @@
         self.interval_text = tk.Entry(self.analize_frame, width=5, justify=tk.RIGHT)
         self.interval_text.insert(tk.END, "2")
         self.interval_text.pack(side=tk.LEFT)
+        self.sigma_label = tk.Label(self.analize_frame, text="シグマ（輪郭の円滑性）")
+        self.sigma_label.pack(side=tk.LEFT, padx=(20, 0))
+        self.sigma_text = tk.Entry(self.analize_frame, width=5, justify=tk.RIGHT)
+        self.sigma_text.insert(tk.END, "5.0")
+        self.sigma_text.pack(side=tk.LEFT)
 
         # 画像表示
         self.canvas = tk.Canvas(self.tkroot, bg="black")
@@ -59,13 +64,16 @@
 
         self.tkroot.mainloop()
 
+    # 分析
     def analyze(self):
         interval = int(self.interval_text.get())
-        self.lp.profiling(interval)
+        ratio = float(self.area_ratio_text.get()) / 100
+        sigma = float(self.sigma_text.get())
+        self.lp.profiling(ratio, sigma, interval)
         self.time_slider.config(to=len(self.lp.results) - 1)
         self.time_slider.set(0)
-        self.show_image()
         self.is_analyzed = True
+        self.show_image()
 
     # 映像ファイルを開く
     def open_movie(self):
diff --git a/lumen_profiler.py b/lumen_profiler.py
index 36f583a..c9e0ed6 100644
--- a/lumen_profiler.py
+++ b/lumen_profiler.py
@@ -21,11 +21,11 @@
         self.frame_count = len(self.frames)
         cap.release()
 
-    def profiling(self, area_ratio, step=1):
+    def profiling(self, area_ratio, sigma, step=1):
         self.results = []
         for idx in range(0, len(self.frames), step):
             frame = self.frames[idx]
-            mask = self.lumen_mask(frame, area_ratio)
+            mask = self.lumen_mask(frame, area_ratio, sigma)
             circle_level, contour = self.calc_circle_level(mask)
             result = {
                 "idx": idx,
@@ -34,6 +34,7 @@
                 "circle_level": circle_level,
                 "contour": contour,
                 "ratio": area_ratio,
+                "sigma": sigma,
             }
             self.results.append(result)
 
@@ -88,7 +89,7 @@
                 thres = i
                 break
         # 気道のマスク生成
-        val_img = cv2.GaussianBlur(val_img, (13, 13), sigma)
+        val_img = cv2.GaussianBlur(val_img, (25, 25), sigma)
         mask = cv2.threshold(val_img, thres, 255, cv2.THRESH_BINARY_INV)[1]
 
         # 連結部の解析