我正在使用硬编码的CNN进行一个预测MRI是否患有肿瘤的项目,现在下一步是在肿瘤周围绘制一个边界矩形。我能够从MRI中提取肿瘤,现在我想获得矩形的相对角以将肿瘤绑定到原始图中。
我通过代码获得的进展是:
import cv2
import matplotlib.pyplot as plt
def show_image(title, image):
cv2.imshow(title, image)
cv2.waitKey(0)
cv2.destroyAllWindows()
def show_image_plt(title, image, cmap = None):
plt.figure(title)
plt.imshow(image,cmap=cmap)
plt.axis('off')
plt.show()
def cvt_image_colorspace(image, colorspace = cv2.COLOR_BGR2GRAY):
return cv2.cvtColor(image, colorspace)
def median_filtering(image, kernel_size=3):
'''
:param image: grayscale image
:param kernel_size: kernel size should be odd number
:return: blurred image
'''
return cv2.medianBlur(image, kernel_size)
def apply_threshold(image, **kwargs):
'''
:param image: image object
:param kwargs: threshold parameters - dictionary
:return:
'''
threshold_method = kwargs['threshold_method']
max_value = kwargs['pixel_value']
threshold_flag = kwargs.get('threshold_flag', None)
if threshold_flag is not None:
ret, thresh1 = cv2.adaptiveThreshold(image, max_value, threshold_method,cv2.THRESH_BINARY,
kwargs['block_size'], kwargs['const'])
else:
ret, thresh1 = cv2.threshold(image, kwargs['threshold'], max_value, threshold_method)
return thresh1
def sobel_filter(img,x,y,kernel_size = 3):
return cv2.Sobel(img, cv2.CV_8U, x,y, ksize=kernel_size)
path=r"Assignment-3\Y4.jpg"
image = cv2.imread(path, 1)
show_image('Original image', image)
#Step one - grayscale the image
grayscale_img = cvt_image_colorspace(image)
#show_image('Grayscaled image', grayscale_img)
#Step two - filter out image
median_filtered = median_filtering(grayscale_img,5)
#show_image('Median filtered', median_filtered)
#testing threshold function
bin_image = apply_threshold(median_filtered, **{"threshold" : 160,
"pixel_value" : 255,
"threshold_method" : cv2.THRESH_BINARY})
otsu_image = apply_threshold(median_filtered, **{"threshold" : 0,
"pixel_value" : 255,
"threshold_method" : cv2.THRESH_BINARY +
cv2.THRESH_OTSU})
#Step 3a - apply Sobel filter
img_sobelx = sobel_filter(median_filtered, 1, 0)
img_sobely = sobel_filter(median_filtered, 0, 1)
# Adding mask to the image
img_sobel = img_sobelx + img_sobely+grayscale_img
#show_image('Sobel filter applied', img_sobel)
#Step 4 - apply threshold
# Set threshold and maxValue
threshold = 160
maxValue = 255
# Threshold the pixel values
thresh = apply_threshold(img_sobel, **{"threshold" : 160,
"pixel_value" : 255,
"threshold_method" : cv2.THRESH_BINARY})
#show_image("Thresholded", thresh)
#Step 3b - apply erosion + dilation
#apply erosion and dilation to show only the part of the image having more intensity - tumor region
#that we want to extract
kernel=cv2.getStructuringElement(cv2.MORPH_RECT,(9,9))
erosion = cv2.morphologyEx(median_filtered, cv2.MORPH_ERODE, kernel)
#show_image('Eroded image', erosion)
dilation = cv2.morphologyEx(erosion, cv2.MORPH_DILATE, kernel)
#show_image('Dilatated image', dilation)
#Step 4 - apply thresholding
threshold = 160
maxValue = 255
# apply thresholding
new_thresholding = apply_threshold(dilation, **{"threshold" : 160,
"pixel_value" : 255,
"threshold_method" : cv2.THRESH_BINARY})
show_image('Threshold image after erosion + dilation', new_thresholding)
给定MRI的输出图像是:
我认为最好的方法是知道像素不是黑色的地方>
pts = np.argwhere(new_thresholding>0)
y1,x1 = pts.min(axis=0)
y2,x2 = pts.max(axis=0)
new_thresholding_rect= cv2.rectangle(new_thresholding,(x1,y1),(x2,y2),(255,0,0),2)
show_image('Threshold image after erosion + dilation + Rectangle',new_thresholding_rect)