-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathseg_pneum.py
executable file
·158 lines (136 loc) · 6.24 KB
/
seg_pneum.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: ZRHonor
"""
import cv2
import numpy as np
import cmath
import math
import copy
MAIN_AREA_LIMIT = 10000 # 用来判断是不是喉部时用到的常数，超过这个值则为CT图中的主体部分
UPPER_LIMIT = -220
LOWER_LIMIT = -1000
# @function seg_pneum: 主函数：气胸分割算法的入口
# @param src_img: 传入的dcm文件（单张CT图像）
# @return flag: 此张CT图像是否具有气胸现象（True/False）
# @return pneum_area: 此张CT图像中气胸面积
# @return lung_area: 此张CT图像中胸腔面积
def seg_pneum(src_img):
# cv2.imshow('origin image:', src_img)
# Step 1: 分割得到 胸腔区域lung 和 气胸区域pneum
lung, pneum = segmenting(src_img)
# Step 2: 计算胸腔面积和气胸面积
pneum_area = 0
img2, contours, _ = cv2.findContours(
pneum, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
pneum_area += cv2.contourArea(contour)
lung_area = 0
img2, contours, _ = cv2.findContours(
lung, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
lung_area += cv2.contourArea(contour)
flag = False
if pneum_area != 0:
flag = True
return flag, pneum_area, lung_area
# @function segmenting: 分割得到 胸腔区域lung 和 气胸区域pneum
# @param src_img: 传入的dcm文件（单张CT图像）
# @return lung: 胸腔区域
# @return pneum: 气胸区域
def segmenting(src_img):
# Step 1: 预处理
src_img, main_part_img = preprocess_src_img(src_img)
# Step 2: 拿到内部腔体（气道和肺部)
# 2.1 对图像进行阈值分割，得到胸腔轮廓
lung_hu = -600 # lung HU is -700 to −600
ret, soft_tissue = cv2.threshold(src_img, lung_hu, 3071, cv2.THRESH_BINARY)
soft_tissue = np.uint8(soft_tissue)
# cv2.imshow('soft_tissue', soft_tissue)
# 2.2 由胸腔轮廓与整个胸腔区域对比得到内部的腔体（包含气道和肺部）
cavity = np.zeros(src_img.shape, np.uint8)
cavity[main_part_img > 127] = 255
cavity[soft_tissue > 127] = 0
# cv2.imshow('cavity', cavity)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))
cavity = cv2.morphologyEx(cavity, cv2.MORPH_OPEN, kernel)
# 2.3 把肺叶中的肺泡等空隙填充起来
img2, contours, _ = cv2.findContours(
cavity, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
cv2.fillPoly(cavity, [contour], 255)
cv2.imshow('cavity fill', cavity)
# Step 3: 去除气管支气管、食道等区域的干扰
lung = _rule_out_tracheal_area(cavity)
cv2.imshow("lung", lung)
src_img[lung < 127] = src_img.min()
# air HU: -1000
ret, black = cv2.threshold(src_img, -920, 3071, cv2.THRESH_BINARY)
pneum = np.zeros(src_img.shape, np.uint8)
pneum[black < 127] = 255
pneum[lung < 127] = 0
cv2.imshow('black', black)
cv2.imshow('pneum', pneum)
return lung, pneum
# @function preprocess_src_img: 预处理图像，取主体部分，去掉衣服部分，并截断Hu值
# @param src_img: 原始图像
# @return src_img: 预处理过后的图像：Hu值被截断，保留主体部分并去除外围衣服
# @return main_part_img: uint8格式的主体部分图像，range(0, 255)
def preprocess_src_img(src_img):
# Step 1: 取主体部分
# 对图像进行阈值分割，二值化为两部分：（气管支气管气胸，其他组织）
# 阈值-450：只需大致划分，便于后续拿到主要部分
thres_get_main_part = -450
ret, img = cv2.threshold(src_img, thres_get_main_part, 3071, cv2.THRESH_BINARY)
img = np.uint8(img)
img2, contours, _ = cv2.findContours(
img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
main_part_contour = []
for contour in contours:
area = cv2.contourArea(contour)
if area > MAIN_AREA_LIMIT:
main_part_contour.append(contour)
main_part_img = np.zeros(src_img.shape, np.uint8)
for contour in main_part_contour:
cv2.fillPoly(main_part_img, [contour], 255)
# Step 2: 去掉衣服部分 thickness = 20
cv2.drawContours(src_img, main_part_contour, -1, (0,0,255), 20)
# Step 3: 截断
src_img[main_part_img < 127] = LOWER_LIMIT
src_img[src_img > UPPER_LIMIT] = UPPER_LIMIT
src_img[src_img < LOWER_LIMIT] = LOWER_LIMIT
return src_img, main_part_img
def _rule_out_tracheal_area(src_img):
# 气胸判断.用于去除气管支气管区域
# 气管支气管区域判断依据：面积大于20且圆形度大于0.8
img2, contours, _ = cv2.findContours(
src_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
# 计算圆形度
area = cv2.contourArea(contour)
if area == 0:
continue
radius = (area / math.pi) ** 0.5
equivDiameter = 2 * radius
perimeter = cv2.arcLength(contour, True)
circularity = math.pi * equivDiameter / perimeter
# print("面积： ", area, " 半径： ", radius, " 周长： ", perimeter, " 圆形度： ", circularity)
if (area > 20) and (area < 500) and (circularity > 0.8):
# print("qiguan...")
cv2.fillPoly(src_img, [contour], 0)
return src_img
def refinePneum(pneum):
# 精细化处理气胸，先筛除气管支气管区域，再通过闭操作平滑图像，得到真实气胸区域
# cv2.imshow('[pneum] before partition', pneum)
# 先按圆形度去除气管支气管区域
pneum_area = 0
pneum_contours = []
pneum = _rule_out_tracheal_area(pneum)
cv2.imshow('_rule_out_tracheal_area', pneum)
# 膨胀腐蚀操作(闭操作)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (8, 8))
dilation = cv2.dilate(pneum, kernel, iterations = 1)
pneum = cv2.erode(dilation, kernel, iterations = 1)
cv2.imshow('close operation', pneum)
return pneum