用于 X 射线图像和检测骨骼的图像阈值算法

问题描述

我有一个关于 OpenCV (python) 的小项目，其中一个步骤是从人体获取 X 射线图像并将其转换为二进制图像，其中白色像素表示存在某些骨骼的位置，黑色表示那里没有骨头。

由于有时“骨骼部分”可能比来自另一个区域的“非骨骼部分”更暗，因此简单的阈值设置不起作用。我也试过 adaptive threshold，但我看不出有多大区别。

我想出了一个简单的算法，为每一行应用一个简单的阈值。代码如下：

def threshhold(image,val):
    image = image.copy()

    for row_idx in range(image.shape[0]):
        max_row = image[row_idx].max()
        min_row = image[row_idx].min()
        tresh = np.median(image[row_idx]) + (val * (max_row - min_row))
        # Or use np.mean instead of np.median

        _,tresh = cv2.threshold(image[row_idx],tresh,255,cv2.THRESH_BINARY)
        image[row_idx] = tresh.ravel()
    return image

这是执行相同工作但逐列而不是逐行的代码：

def threshhold2(image,val):
    image = image.copy()

    for row_idx in range(image.shape[1]):
        max_row = image[:,row_idx].max()
        min_row = image[:,row_idx].min()
        tresh = np.median(image[:,row_idx]) + (val * (max_row - min_row))
        # Or use np.mean instead of np.median

        _,tresh = cv2.threshold(image[:,row_idx],cv2.THRESH_BINARY)
        image[:,row_idx] = tresh.ravel()
    return image

这种方法非常适用于这样的图像：

这个不太好，但也不错：

非常可怕：

只有左半边好看

...

如你所见；该算法仅适用于某些图像。我会很高兴看到更多有经验的人的想法。

顺便说一下，图片不适合我。

整个源代码：

import os
import cv2
import numpy as np

files_to_see = os.listdir("data_set")
files_to_see.sort()
current_file = 0

print(files_to_see)

def slice(image,size):
    out = []
    x_count = image.shape[1] // size
    y_count = image.shape[0] // size
    for y_idx in range(y_count):
        for x_idx in range(x_count):
            out.append(
                (
                    (y_idx,x_idx),image[y_idx * size: (y_idx + 1) * size,x_idx * size: (x_idx + 1) * size]
                )
            )
    return y_count,x_count,out

def normalize(image):
    image = image.copy()
    min_pix = image.min()
    max_pix = image.max()
    for y in range(image.shape[0]):
        for x in range(image.shape[1]):
            val = image[y,x]
            val -= min_pix
            val *= 255 / (max_pix - min_pix)
            image[y,x] = round(val)
    # image -= min_pix
    # image *= round(255 / (max_pix - min_pix))
    return image


def threshhold(image,val,method):
    image = image.copy()

    for row_idx in range(image.shape[0]):
        max_row = image[row_idx].max()
        min_row = image[row_idx].min()
        # tresh = np.median(image[row_idx]) + (val * (max_row - min_row))
        tresh = method(image[row_idx]) + (val * (max_row - min_row))

        _,cv2.THRESH_BINARY)
        image[row_idx] = tresh.ravel()
    return image

def threshhold2(image,method):
    image = image.copy()

    for row_idx in range(image.shape[1]):
        max_row = image[:,row_idx].min()
        tresh = method(image[:,row_idx]) + (val * (max_row - min_row))

        _,row_idx] = tresh.ravel()
    return image

def recalculate_threshhold(v):
    global original_current_image,thresh_current_image,y_c,x_c,slices

    method = np.mean
    if cv2.getTrackbarPos("method","xb labeler") == 0:
        method = np.median
    thresh_current_image = threshhold2(original_current_image,cv2.getTrackbarPos("threshhold_value","xb labeler") / 1000,method)
    y_c,slices = slice(thresh_current_image,128)

def thresh_current_image_mouse_event(event,x,y,flags,param):
    if event == 1:
        print(x // 128,y // 128)
        cv2.imshow("slice",slices[(x // 128) + (y // 128) * x_c][1])

cv2.namedWindow("xb labeler")
cv2.createTrackbar("threshhold_value","xb labeler",1000,recalculate_threshhold)
cv2.createTrackbar("method",1,recalculate_threshhold)

cv2.namedWindow("thresh_current_image")
cv2.setMouseCallback("thresh_current_image",thresh_current_image_mouse_event)

def init():
    global original_current_image,slices,files_to_see,current_file
    original_current_image = cv2.imread("data_set/" + files_to_see[current_file],cv2.CV_8UC1)
    original_current_image = cv2.resize(original_current_image,(512,512))
    original_current_image = normalize(original_current_image)
    original_current_image = cv2.GaussianBlur(original_current_image,(5,5),10)
    recalculate_threshhold(1)
    y_c,128)

init()

while True:

    cv2.imshow("thresh_current_image",thresh_current_image)
    cv2.imshow("xb labeler",original_current_image)
    k = cv2.waitKey(1)
    if k == ord('p'):
        cv2.imwrite("ssq.png",thresh_current_image)
        current_file += 1
        init()

cv2.destroyAllWindows()

编辑：添加原始图像：

解决方法

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image-processing medical-imaging opencv opencv opencv-python python