镜像自地址
https://github.com/xming521/CTAI.git
已同步 2025-12-06 06:36:49 +00:00
201 行
7.5 KiB
Python
201 行
7.5 KiB
Python
import inspect
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import SimpleITK as sitk
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import cv2
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import numpy as np
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import pandas as pd
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from numba import jit
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np.set_printoptions(suppress=True) # 输出时禁止科学表示法，直接输出小数值
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column_all_c = ['面积', '周长', '重心x', '重心y', '似圆度', '灰度均值', '灰度方差', '灰度偏度',
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'灰度峰态', '小梯度优势', '大梯度优势', '灰度分布不均匀性', '梯度分布不均匀性', '能量', '灰度平均', '梯度平均',
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'灰度均方差', '梯度均方差', '相关', '灰度熵', '梯度熵', '混合熵', '惯性', '逆差矩']
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features_list = ['area', 'perimeter', 'focus_x', 'focus_y', 'ellipse', 'mean', 'std', 'piandu', 'fengdu',
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'small_grads_dominance',
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'big_grads_dominance', 'gray_asymmetry', 'grads_asymmetry', 'energy', 'gray_mean', 'grads_mean',
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'gray_variance', 'grads_variance', 'corelation', 'gray_entropy', 'grads_entropy', 'entropy', 'inertia',
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'differ_moment']
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# 最后俩偏度 峰度
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# 获取变量的名
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def get_variable_name(variable):
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callers_local_vars = inspect.currentframe().f_back.f_locals.items()
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return [var_name for var_name, var_val in callers_local_vars if var_val is variable]
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def glcm(img_gray, ngrad=16, ngray=16):
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'''Gray Level-Gradient Co-occurrence Matrix,取归一化后的灰度值、梯度值分别为16、16'''
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# 利用sobel算子分别计算x-y方向上的梯度值
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gsx = cv2.Sobel(img_gray, cv2.CV_64F, 1, 0, ksize=3)
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gsy = cv2.Sobel(img_gray, cv2.CV_64F, 0, 1, ksize=3)
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height, width = img_gray.shape
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grad = (gsx ** 2 + gsy ** 2) ** 0.5 # 计算梯度值
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grad = np.asarray(1.0 * grad * (ngrad - 1) / grad.max(), dtype=np.int16)
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gray = np.asarray(1.0 * img_gray * (ngray - 1) / img_gray.max(), dtype=np.int16) # 0-255变换为0-15
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gray_grad = np.zeros([ngray, ngrad]) # 灰度梯度共生矩阵
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for i in range(height):
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for j in range(width):
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gray_value = gray[i][j]
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grad_value = grad[i][j]
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gray_grad[gray_value][grad_value] += 1
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gray_grad = 1.0 * gray_grad / (height * width) # 归一化灰度梯度矩阵，减少计算量
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get_glcm_features(gray_grad)
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@jit
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def get_gray_feature():
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# 灰度特征提取算法
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hist = cv2.calcHist([image_ROI_uint8[index]], [0], None, [256], [0, 256])
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# 假的 还没用灰度直方图
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c_features['mean'].append(np.mean(image_ROI[index]))
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c_features['std'].append(np.std(image_ROI[index]))
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s = pd.Series(image_ROI[index])
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c_features['piandu'].append(s.skew())
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c_features['fengdu'].append(s.kurt())
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def get_glcm_features(mat):
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'''根据灰度梯度共生矩阵计算纹理特征量，包括小梯度优势，大梯度优势，灰度分布不均匀性，梯度分布不均匀性，能量，灰度平均，梯度平均，
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灰度方差，梯度方差，相关，灰度熵，梯度熵，混合熵，惯性，逆差矩'''
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sum_mat = mat.sum()
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small_grads_dominance = big_grads_dominance = gray_asymmetry = grads_asymmetry = energy = gray_mean = grads_mean = 0
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gray_variance = grads_variance = corelation = gray_entropy = grads_entropy = entropy = inertia = differ_moment = 0
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for i in range(mat.shape[0]):
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gray_variance_temp = 0
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for j in range(mat.shape[1]):
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small_grads_dominance += mat[i][j] / ((j + 1) ** 2)
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big_grads_dominance += mat[i][j] * j ** 2
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energy += mat[i][j] ** 2
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if mat[i].sum() != 0:
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gray_entropy -= mat[i][j] * np.log(mat[i].sum())
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if mat[:, j].sum() != 0:
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grads_entropy -= mat[i][j] * np.log(mat[:, j].sum())
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if mat[i][j] != 0:
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entropy -= mat[i][j] * np.log(mat[i][j])
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inertia += (i - j) ** 2 * np.log(mat[i][j])
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differ_moment += mat[i][j] / (1 + (i - j) ** 2)
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gray_variance_temp += mat[i][j] ** 0.5
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gray_asymmetry += mat[i].sum() ** 2
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gray_mean += i * mat[i].sum() ** 2
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gray_variance += (i - gray_mean) ** 2 * gray_variance_temp
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for j in range(mat.shape[1]):
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grads_variance_temp = 0
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for i in range(mat.shape[0]):
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grads_variance_temp += mat[i][j] ** 0.5
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grads_asymmetry += mat[:, j].sum() ** 2
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grads_mean += j * mat[:, j].sum() ** 2
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grads_variance += (j - grads_mean) ** 2 * grads_variance_temp
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small_grads_dominance /= sum_mat
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big_grads_dominance /= sum_mat
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gray_asymmetry /= sum_mat
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grads_asymmetry /= sum_mat
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gray_variance = gray_variance ** 0.5
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grads_variance = grads_variance ** 0.5
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for i in range(mat.shape[0]):
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for j in range(mat.shape[1]):
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corelation += (i - gray_mean) * (j - grads_mean) * mat[i][j]
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glgcm_features = [small_grads_dominance, big_grads_dominance, gray_asymmetry, grads_asymmetry, energy, gray_mean,
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grads_mean,
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gray_variance, grads_variance, corelation, gray_entropy, grads_entropy, entropy, inertia,
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differ_moment]
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for i in range(len(glgcm_features)):
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t = get_variable_name(glgcm_features[i])[0]
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c_features[t].append(np.round(glgcm_features[i], 4))
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def get_geometry_feature():
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# 形态特征 分割mask获得一些特征
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im2, contours, x = cv2.findContours(mask_array.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
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tarea = []
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tperimeter = []
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for c in contours:
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# 生成矩
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try:
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M = cv2.moments(c)
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cx = int(M['m10'] / M['m00'])
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cy = int(M['m01'] / M['m00'])
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c_features['focus_x'].append(cx)
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c_features['focus_y'].append(cy)
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except:
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print('error')
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# 椭圆拟合
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try:
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(x, y), (MA, ma), angle = cv2.fitEllipse(c)
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c_features['ellipse'].append((ma - MA))
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except:
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continue
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# 面积周长
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tarea.append(cv2.contourArea(c))
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tperimeter.append(cv2.arcLength(c, True))
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# 将mask里的最大值追加 有黑洞
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try:
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c_features['area'].append(max(tarea))
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c_features['perimeter'].append(round(max(tperimeter), 4))
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except:
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print('area error')
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# 提取肿瘤特征
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def get_feature(image, mask):
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global w
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global image_ROI_uint8, index, image_ROI_mini, image_ROI, mask_array
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mask_array = cv2.imread(mask, 0)
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image = sitk.ReadImage(image)
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image_arrary = sitk.GetArrayFromImage(image)[0, :, :]
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# 映射到CT获得特征
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image_ROI = np.zeros(shape=image_arrary.shape)
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index = np.nonzero(mask_array)
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if not index[0].any():
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# c_features['no'] = True
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return None
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image_ROI[index] = image_arrary[index]
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image_ROI_uint8 = np.uint8(image_ROI)
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# 获得只有肿瘤的图片
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x, y, w, h = cv2.boundingRect(mask_array)
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image_ROI_mini = np.uint8(image_arrary[y:y + h, x:x + w])
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w = image_ROI_mini
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# 灰度梯度共生矩阵提取纹理特征
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get_geometry_feature()
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get_gray_feature()
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glcm(image_ROI_mini, 15, 15)
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return c_features
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def main(pid):
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global w
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person_id = pid
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global c_features
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c_features = {}
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for i in range(len(features_list)):
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c_features[features_list[i]] = [column_all_c[i]]
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get_feature(f'tmp/ct/{pid}.dcm', f'tmp/mask/{pid}_mask.png')
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for j in c_features:
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if j == 'id':
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continue
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c_features[j][1] = np.round(np.mean(c_features[j][1]), 4)
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return c_features
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if __name__ == '__main__':
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main()
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