医学图像预处理 1）搭建运行环境，运行Brain Tumor Detection代码中裁剪功能 预先安装好相关模块，安装Jupyter Notebook交互式计算环境
conda install jupyter 首先导入相关包
import tensorflow as tf from tensorflow.keras.layers import Conv2D, Input, ZeroPadding2D, BatchNormalization, Activation, MaxPooling2D, Flatten, Dense from tensorflow.keras.models import Model, load_model from tensorflow.keras.callbacks import TensorBoard, ModelCheckpoint from sklearn.model_selection import train_test_split from sklearn.metrics import f1_score from sklearn.utils import shuffle import cv2 import imutils import numpy as np import matplotlib.pyplot as plt import time from os import listdir %matplotlib inline 因为这里只是进行图形预处理，对Brain Tumor图片进行裁剪，主要用到的包是cv2, imutils, numpy, matplotlib.pyplot
然后是运行定义裁剪脑轮廓(查找大脑的顶部、底部、左端、右端的极点)的函数（crop_brain_contour）
def crop_brain_contour(image, plot=False): #import imutils #import cv2 #from matplotlib import pyplot as plt # Convert the image to grayscale, and blur it slightly gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) gray = cv2.GaussianBlur(gray, (5, 5), 0) # Threshold the image, then perform a series of erosions + # dilations to remove any small regions of noise thresh = cv2.threshold(gray, 45, 255, cv2.THRESH_BINARY)[1] thresh = cv2.erode(thresh, None, iterations=2) thresh = cv2.dilate(thresh, None, iterations=2) # Find contours in thresholded image, then grab the largest one cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnts = imutils.grab_contours(cnts) c = max(cnts, key=cv2.contourArea) # Find the extreme points extLeft = tuple(c[c[:, :, 0].argmin()][0]) extRight = tuple(c[c[:, :, 0].argmax()][0]) extTop = tuple(c[c[:, :, 1].argmin()][0]) extBot = tuple(c[c[:, :, 1].argmax()][0]) # crop new image out of the original image using the four extreme points (left, right, top, bottom) new_image = image[extTop[1]:extBot[1], extLeft[0]:extRight[0]] if plot: plt.figure() plt.subplot(1, 2, 1) plt.imshow(image) plt.tick_params(axis='both', which='both', top=False, bottom=False, left=False, right=False, labelbottom=False, labeltop=False, labelleft=False, labelright=False) plt.title('Original Image') plt.subplot(1, 2, 2) plt.imshow(new_image) plt.tick_params(axis='both', which='both', top=False, bottom=False, left=False, right=False, labelbottom=False, labeltop=False, labelleft=False, labelright=False) plt.title('Cropped Image') plt.show() return new_image 下面我简单说一下我对这个函数的理解（当然函数中已有原作者的解释）：
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