simpleITK后处理，滤除不符合的点

# 后处理文件，去除独立的小点

import os
import SimpleITK as sitk
import numpy as np
import tqdm

def postProcessing(image,file_name,reseve_area_minvalue):
    """
    对标签的後处理
    :param image: sitk image图像
    :param reseve_area_minvalue: 保留下来的最小连通区域面积
    :return: None. 将处理後的文件写入本地
    """
    closingImage = sitk.BinaryMorphologicalClosing(image,[3,3,3])
    imageArray = sitk.GetArrayFromImage(image)

    ccFilter = sitk.ConnectedComponentImageFilter()
    ccFilter.SetFullyConnected(True)
    outputMask = ccFilter.Execute(closingImage)

    lssFilter = sitk.LabelShapeStatisticsImageFilter()
    lssFilter.Execute(outputMask)

    numConnectedLabel = ccFilter.GetObjectCount()

    # 连通域label从1开始，0表示背景
    areaLabels = []
    for i in range(1,numConnectedLabel+1):
        area = lssFilter.GetNumberOfPixels(i)
        if area>=reseve_area_minvalue:
            areaLabels.append(i) # 把这个块区域的label存进去

    # print(len(areaLabels))
    npOutputMask = sitk.GetArrayFromImage(outputMask)
    resMask = np.zeros_like(npOutputMask).astype("uint8")
    for labelIndex in areaLabels:
        # print(labelIndex)
        resMask[npOutputMask==labelIndex] = imageArray[npOutputMask==labelIndex]

    outImage = sitk.GetImageFromArray(resMask)
    outImage.SetDirection(image.GetDirection())
    outImage.SetSpacing(image.GetSpacing())
    outImage.SetOrigin(image.GetOrigin())
    savePath = os.path.join(saveDir,file_name)
    sitk.WriteImage(outImage,savePath)

if __name__ == '__main__':
    workDir = r"I:\2023label_1\label"
    # workDir = "mergeThreeLabel"
    saveDir = r"I:\2023label_1\label2"
    fileNames = os.listdir(workDir)
    for fileName in tqdm.tqdm(fileNames):
        filePath = os.path.join(workDir,fileName)
        orgImage = sitk.ReadImage(filePath)

        postProcessing(orgImage,fileName,500)
        # input('zzz')