Python实现区域生长算法（regionGrow）

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。

二维平面图像

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)

三维体素数据：

import numpy as np

def grow(img, seed, t):
    """
    img: ndarray, ndim=3
        An image volume.
    
    seed: tuple, len=3
        Region growing starts from this point.

    t: int
        The image neighborhood radius for the inclusion criteria.
    """
    seg = np.zeros(img.shape, dtype=np.bool)
    checked = np.zeros_like(seg)

    seg[seed] = True
    checked[seed] = True
    needs_check = get_nbhd(seed, checked, img.shape)

    while len(needs_check) > 0:
        pt = needs_check.pop()

        # Its possible that the point was already checked and was
        # put in the needs_check stack multiple times.
        if checked[pt]: continue

        checked[pt] = True

        # Handle borders.
        imin = max(pt[0]-t, 0)
        imax = min(pt[0]+t, img.shape[0]-1)
        jmin = max(pt[1]-t, 0)
        jmax = min(pt[1]+t, img.shape[1]-1)
        kmin = max(pt[2]-t, 0)
        kmax = min(pt[2]+t, img.shape[2]-1)

        if img[pt] >= img[imin:imax+1, jmin:jmax+1, kmin:kmax+1].mean():
            # Include the voxel in the segmentation and
            # add its neighbors to be checked.
            seg[pt] = True
            needs_check += get_nbhd(pt, checked, img.shape)

    return seg

区域生长涉及种子选取，提供一个获取图像zuo'坐标的函数：

def on_mouse(event, x,y, flags , params):
    if event == cv2.EVENT_LBUTTONDOWN:
        print('Seed: ' + 'Point' + '('+str(x) + ', ' + str(y)+')', imger[y, x])
        clicks.append((y, x))
cv2.setMouseCallback('input', on_mouse, 0, )

‘input’是你显示图像的命名。

Python实现区域生长算法（regionGrow）

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