NumPy鍙互涓嶥ICOM闆嗘垚浣跨敤鏉ュ鐞嗗尰瀛﹀浘鍍忔暟鎹€侱ICOM锛圖igital Imaging and Communications in Medicine锛夋槸涓€绉嶇敤浜庡瓨鍌ㄥ拰浼犺緭鍖诲褰卞儚鏁版嵁鐨勬爣鍑嗘牸寮忋€傞€氳繃浣跨敤NumPy搴擄紝鍙互鏂逛究鍦拌鍙栥€佸鐞嗗拰鍒嗘瀽DICOM鏍煎紡鐨勫尰瀛﹀浘鍍忔暟鎹€?/p>

浠ヤ笅鏄竴浜汵umPy涓嶥ICOM闆嗘垚浣跨敤鐨勭ず渚嬶細

1. 璇诲彇DICOM鏂囦欢锛氫娇鐢∟umPy璇诲彇DICOM鏂囦欢骞跺皢鍏惰浆鎹负NumPy鏁扮粍锛屽彲浠ユ柟渚垮湴瀵瑰尰瀛﹀浘鍍忔暟鎹繘琛屽鐞嗐€傚彲浠ヤ娇鐢?code>pydicom搴撴潵璇诲彇DICOM鏂囦欢骞跺皢鍏惰浆鎹负NumPy鏁扮粍銆?/li>

import numpy as np
import pydicom

# 璇诲彇DICOM鏂囦欢
ds = pydicom.dcmread('path/to/dicom/file.dcm')

# 灏咲ICOM鏁版嵁杞崲涓篘umPy鏁扮粍
image_data = ds.pixel_array

2. 澶勭悊DICOM鍥惧儚鏁版嵁锛歂umPy鎻愪緵浜嗕赴瀵岀殑鏁扮粍鎿嶄綔鍜屾暟瀛﹀嚱鏁帮紝鍙互鏂逛究鍦板DICOM鍥惧儚鏁版嵁杩涜澶勭悊銆備緥濡傦紝鍙互浣跨敤NumPy瀵瑰浘鍍忔暟鎹繘琛岃鍓€佺缉鏀俱€佹棆杞瓑鎿嶄綔銆?/li>

# 瀵瑰浘鍍忔暟鎹繘琛岃鍓?/span>
cropped_image = image_data[100:300, 200:400]

# 瀵瑰浘鍍忔暟鎹繘琛岀缉鏀?/span>
resized_image = np.resize(image_data, (200, 200))

# 瀵瑰浘鍍忔暟鎹繘琛屾棆杞?/span>
rotated_image = np.rot90(image_data)

3. 鍒嗘瀽DICOM鍥惧儚鏁版嵁锛歂umPy杩樺彲浠ョ敤浜庡DICOM鍥惧儚鏁版嵁杩涜鍒嗘瀽鍜屽鐞嗐€備緥濡傦紝鍙互浣跨敤NumPy璁＄畻鍥惧儚鏁版嵁鐨勭粺璁′俊鎭€佺粯鍒剁洿鏂瑰浘銆佸簲鐢ㄦ护娉㈠櫒绛夈€?/li>

# 璁＄畻鍥惧儚鏁版嵁鐨勭粺璁′俊鎭?/span>
min_value = np.min(image_data)
max_value = np.max(image_data)
mean_value = np.mean(image_data)
std_value = np.std(image_data)

# 缁樺埗鍥惧儚鏁版嵁鐨勭洿鏂瑰浘
import matplotlib.pyplot as plt
plt.hist(image_data.flatten(), bins=256, range=(0, 255), density=True)

# 搴旂敤楂樻柉婊ゆ尝鍣?/span>
from scipy.ndimage import gaussian_filter
smoothed_image = gaussian_filter(image_data, sigma=2)

閫氳繃缁撳悎NumPy鍜孌ICOM锛屽彲浠ユ柟渚垮湴瀵瑰尰瀛﹀浘鍍忔暟鎹繘琛屽鐞嗐€佸垎鏋愬拰鍙鍖栵紝涓哄尰瀛﹀奖鍍忛鍩熺殑鐮旂┒鍜屽簲鐢ㄦ彁渚涗究鍒┿€?/p>