227 lines
9.1 KiB
Python
227 lines
9.1 KiB
Python
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# import SimpleITK as sitk
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# import os
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# import numpy as np
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# def standardize_origin(image, target_origin):
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# """將影像的原點標準化為目標原點"""
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# return sitk.Resample(
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# image,
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# image.GetSize(),
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# sitk.Transform(),
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# sitk.sitkLinear,
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# target_origin,
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# image.GetSpacing(),
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# image.GetDirection(),
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# 0,
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# image.GetPixelID()
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# )
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# def resample_image(image, reference_image, is_label=False):
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# resampler = sitk.ResampleImageFilter()
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# resampler.SetReferenceImage(reference_image)
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# resampler.SetTransform(sitk.Transform())
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# resampler.SetOutputSpacing(reference_image.GetSpacing())
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# resampler.SetSize(reference_image.GetSize())
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# resampler.SetOutputDirection(reference_image.GetDirection())
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# resampler.SetOutputOrigin(reference_image.GetOrigin())
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# if is_label:
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# resampler.SetInterpolator(sitk.sitkNearestNeighbor)
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# resampler.SetDefaultPixelValue(0)
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# else:
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# resampler.SetInterpolator(sitk.sitkLinear)
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# resampler.SetDefaultPixelValue(image.GetPixelIDValue())
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# return resampler.Execute(image)
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# def process_patient(t1c_image_path, ctctc_image_path, label_path, output_t1c_dir, output_ctctc_dir, output_label_dir, patient_id):
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# print(f"\nProcessing patient: {patient_id}")
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# # 讀取影像
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# t1c_image = sitk.ReadImage(t1c_image_path)
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# ctctc_image = sitk.ReadImage(ctctc_image_path)
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# label = sitk.ReadImage(label_path)
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# print(f" Original T1C image size: {t1c_image.GetSize()}, origin: {t1c_image.GetOrigin()}")
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# print(f" Original CT+CTC image size: {ctctc_image.GetSize()}, origin: {ctctc_image.GetOrigin()}")
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# print(f" Original label size: {label.GetSize()}, origin: {label.GetOrigin()}")
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# # 使用 T1C 影像作為參考
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# reference_image = t1c_image
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# # 重採樣 CT+CTC 影像以匹配 T1C 影像
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# resampled_ctctc = resample_image(ctctc_image, reference_image)
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# # 重採樣 label 以匹配 T1C 影像
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# resampled_label = resample_image(label, reference_image, is_label=True)
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# print(f" Resampled CT+CTC image size: {resampled_ctctc.GetSize()}, origin: {resampled_ctctc.GetOrigin()}")
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# print(f" Resampled label size: {resampled_label.GetSize()}, origin: {resampled_label.GetOrigin()}")
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# # 保存重採樣後的影像和標籤
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# t1c_output_path = os.path.join(output_t1c_dir, f"T1C_{patient_id}.nii.gz")
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# ctctc_output_path = os.path.join(output_ctctc_dir, f"CT+CTC_{patient_id}.nii.gz")
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# label_output_path = os.path.join(output_label_dir, f"label_{patient_id}.nii.gz")
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# sitk.WriteImage(t1c_image, t1c_output_path)
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# sitk.WriteImage(resampled_ctctc, ctctc_output_path)
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# sitk.WriteImage(resampled_label, label_output_path)
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# print(f" T1C image saved to: {t1c_output_path}")
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# print(f" Resampled CT+CTC image saved to: {ctctc_output_path}")
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# print(f" Resampled label saved to: {label_output_path}")
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# def process_directory(t1c_dir, ctctc_dir, label_dir, output_t1c_dir, output_ctctc_dir, output_label_dir):
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# print("Starting directory processing")
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# os.makedirs(output_t1c_dir, exist_ok=True)
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# os.makedirs(output_ctctc_dir, exist_ok=True)
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# os.makedirs(output_label_dir, exist_ok=True)
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# # 獲取所有病人 ID
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# patient_ids = set()
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# for filename in os.listdir(t1c_dir):
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# if filename.startswith("T1C_") and filename.endswith(".nii.gz"):
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# patient_id = filename[4:-7] # 去掉 "T1C_" 和 ".nii.gz"
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# patient_ids.add(patient_id)
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# for patient_id in patient_ids:
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# t1c_path = os.path.join(t1c_dir, f"T1C_{patient_id}.nii.gz")
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# ctctc_path = os.path.join(ctctc_dir, f"CT+CTC_{patient_id}.nii.gz")
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# label_path = os.path.join(label_dir, f"T1C_label_{patient_id}.nii.gz") # 假設 label 文件名與 T1C 相同
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# if os.path.exists(t1c_path) and os.path.exists(ctctc_path) and os.path.exists(label_path):
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# process_patient(t1c_path, ctctc_path, label_path, output_t1c_dir, output_ctctc_dir, output_label_dir, patient_id)
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# else:
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# print(f" WARNING: Missing files for patient {patient_id}")
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# # 主程序
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# t1c_dir = '/home/onlylian/T1C_image_folder'
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# ctctc_dir = '/home/onlylian/CT+CTC_image_folder'
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# label_dir = '/home/onlylian/T1C_label_folder' # 使用 T1C 的 label
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# output_t1c_dir = '/home/onlylian/resampled_T1C_image_2'
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# output_ctctc_dir = '/home/onlylian/resampled_CT+CTC_image_2'
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# output_label_dir = '/home/onlylian/resampled_label_2'
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# print("Starting image preprocessing")
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# process_directory(t1c_dir, ctctc_dir, label_dir, output_t1c_dir, output_ctctc_dir, output_label_dir)
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# print("All processing completed.")
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import SimpleITK as sitk
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import os
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import numpy as np
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def standardize_origin(image, target_origin):
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"""將影像的原點標準化為目標原點"""
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return sitk.Resample(
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image,
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image.GetSize(),
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sitk.Transform(),
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sitk.sitkLinear,
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target_origin,
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image.GetSpacing(),
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image.GetDirection(),
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0,
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image.GetPixelID()
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)
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def resample_image(image, reference_image, is_label=False):
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"""重採樣影像以匹配參考影像"""
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resampler = sitk.ResampleImageFilter()
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resampler.SetReferenceImage(reference_image)
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resampler.SetTransform(sitk.Transform())
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resampler.SetOutputSpacing(reference_image.GetSpacing())
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resampler.SetSize(reference_image.GetSize())
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resampler.SetOutputDirection(reference_image.GetDirection())
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resampler.SetOutputOrigin(reference_image.GetOrigin())
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if is_label:
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resampler.SetInterpolator(sitk.sitkNearestNeighbor)
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resampler.SetDefaultPixelValue(0)
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else:
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resampler.SetInterpolator(sitk.sitkLinear)
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resampler.SetDefaultPixelValue(image.GetPixelIDValue())
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return resampler.Execute(image)
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def process_patient(t1_image_path, label_path, output_t1_dir, output_label_dir, patient_id):
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"""處理單個病人的影像"""
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print(f"\nProcessing patient: {patient_id}")
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# 讀取影像
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t1_image = sitk.ReadImage(t1_image_path)
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label = sitk.ReadImage(label_path)
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print(f" Original T1 image size: {t1_image.GetSize()}, origin: {t1_image.GetOrigin()}")
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print(f" Original label size: {label.GetSize()}, origin: {label.GetOrigin()}")
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# 使用 T1 影像作為參考
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reference_image = t1_image
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# 重採樣 label 以匹配 T1 影像
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resampled_label = resample_image(label, reference_image, is_label=True)
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print(f" Resampled label size: {resampled_label.GetSize()}, origin: {resampled_label.GetOrigin()}")
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# 保存處理後的影像和標籤
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t1_output_path = os.path.join(output_t1_dir, f"T1_{patient_id}.nii.gz")
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label_output_path = os.path.join(output_label_dir, f"label_{patient_id}.nii.gz")
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sitk.WriteImage(t1_image, t1_output_path)
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sitk.WriteImage(resampled_label, label_output_path)
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print(f" T1 image saved to: {t1_output_path}")
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print(f" Resampled label saved to: {label_output_path}")
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def process_directory(t1_dir, label_dir, output_t1_dir, output_label_dir):
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"""處理整個目錄的影像"""
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print("Starting directory processing")
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# 創建輸出目錄
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os.makedirs(output_t1_dir, exist_ok=True)
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os.makedirs(output_label_dir, exist_ok=True)
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# 獲取所有病人 ID
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patient_ids = set()
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for filename in os.listdir(t1_dir):
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if filename.startswith("T1_") and filename.endswith(".nii.gz"):
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patient_id = filename[3:-7] # 去掉 "T1_" 和 ".nii.gz"
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patient_ids.add(patient_id)
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total_patients = len(patient_ids)
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processed_count = 0
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for patient_id in patient_ids:
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processed_count += 1
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print(f"\nProcessing patient {processed_count}/{total_patients}: {patient_id}")
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t1_path = os.path.join(t1_dir, f"T1_{patient_id}.nii.gz")
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label_path = os.path.join(label_dir, f"T1_label_{patient_id}.nii.gz")
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if os.path.exists(t1_path) and os.path.exists(label_path):
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try:
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process_patient(t1_path, label_path, output_t1_dir, output_label_dir, patient_id)
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except Exception as e:
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print(f" ERROR processing patient {patient_id}: {str(e)}")
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else:
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print(f" WARNING: Missing files for patient {patient_id}")
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if not os.path.exists(t1_path):
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print(f" Missing T1 image: {t1_path}")
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if not os.path.exists(label_path):
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print(f" Missing label: {label_path}")
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# 主程序
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if __name__ == "__main__":
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# 設定輸入輸出路徑
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t1_dir = '/mnt/1218/onlylian/T1_image_folder_test'
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label_dir = '/mnt/1218/onlylian/T1_label_folder_test'
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output_t1_dir = '/mnt/1218/onlylian/resampled_T1_image_test'
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output_label_dir = '/mnt/1218/onlylian/resampled_T1_label_test'
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print("Starting image preprocessing")
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try:
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process_directory(t1_dir, label_dir, output_t1_dir, output_label_dir)
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print("\nAll processing completed successfully.")
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except Exception as e:
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print(f"\nERROR: Processing failed with error: {str(e)}")
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