区域生长是一种串行区域分割的图像分割方法。区域生长是指从某个像素出发，按照一定的准则，逐步加入邻近像素，当满足一定的条件时，区域生长终止。区域生长的好坏决定于1.初始点（种子点）的选取。2.生长准则。3.终止条件。区域生长是从某个或者某些像素点出发，最后得到整个区域，进而实现目标的提取。

区域生长的原理：   

区域生长的基本思想是将具有相似性质的像素集合起来构成区域。具体先对每个需要分割的区域找一个种子像素作为生长起点，然后将种子像素和周围邻域中与种子像素有相同或相似性质的像素（根据某种事先确定的生长或相似准则来判定）合并到种子像素所在的区域中。将这些新像素当作新的种子继续上面的过程，直到没有满足条件的像素可被包括进来。这样一个区域就生长成了。

区域生长实现的步骤如下：

1. 对图像顺序扫描!找到第1个还没有归属的像素, 设该像素为(x0, y0);

2. 以(x0, y0)为中心, 考虑(x0, y0)的4邻域像素(x, y)如果(x0, y0)满足生长准则, 将(x, y)与(x0, y0)合并(在同一区域内), 同时将(x, y)压入堆栈;

3. 从堆栈中取出一个像素, 把它当作(x0, y0)返回到步骤2;

4. 当堆栈为空时!返回到步骤1;

5. 重复步骤1 - 4直到图像中的每个点都有归属时。生长结束。

Python实现

import numpy as np
import cv2

class Point(object):
 def __init__(self,x,y):
  self.x = x
  self.y = y

 def getX(self):
  return self.x
 def getY(self):
  return self.y

def getGrayDiff(img,currentPoint,tmpPoint):
 return abs(int(img[currentPoint.x,currentPoint.y]) - int(img[tmpPoint.x,tmpPoint.y]))

def selectConnects(p):
 if p != 0:
  connects = [Point(-1, -1), Point(0, -1), Point(1, -1), Point(1, 0), Point(1, 1), \
     Point(0, 1), Point(-1, 1), Point(-1, 0)]
 else:
  connects = [ Point(0, -1), Point(1, 0),Point(0, 1), Point(-1, 0)]
 return connects

def regionGrow(img,seeds,thresh,p = 1):
 height, weight = img.shape
 seedMark = np.zeros(img.shape)
 seedList = []
 for seed in seeds:
  seedList.append(seed)
 label = 1
 connects = selectConnects(p)
 while(len(seedList)>0):
  currentPoint = seedList.pop(0)

  seedMark[currentPoint.x,currentPoint.y] = label
  for i in range(8):
   tmpX = currentPoint.x + connects[i].x
   tmpY = currentPoint.y + connects[i].y
   if tmpX < 0 or tmpY < 0 or tmpX >= height or tmpY >= weight:
    continue
   grayDiff = getGrayDiff(img,currentPoint,Point(tmpX,tmpY))
   if grayDiff < thresh and seedMark[tmpX,tmpY] == 0:
    seedMark[tmpX,tmpY] = label
    seedList.append(Point(tmpX,tmpY))
 return seedMark


img = cv2.imread('lean.png',0)
seeds = [Point(10,10),Point(82,150),Point(20,300)]
binaryImg = regionGrow(img,seeds,10)
cv2.imshow(' ',binaryImg)
cv2.waitKey(0)

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