C# 图像处理中的很多像素实例

我有一个很大的内存问题似乎是由许多XElement实例引起的

我有一个TabControl，它在每个选项卡中显示一个带有单元格图片的图像（想象一个黑色图片，上面有大约150个圆圈，是单元格）

我写了一个算法来检测这些细胞，并得到它们的直径等参数

问题来了：在空闲状态下，程序使用大约200mb的内存。当我开始检测时，它会上升到3.5GB。我使用VisualStudio的内置内存分析器和第三方内存分析器来查找问题。XElement实例在3500时开始处于空闲状态，在检测之后，它们处于45000个实例，我不知道为什么会有这么多

在检测开始时，我为图像的每个块启动算法（图片约为25000x25000像素，因此我必须将它们划分为块）：

while（startY+1500

算法方法：

 ObservableCollection<ImageObjectModel> ioms = selectedWorkspaceModel.ImageObjectModels;           
        result.Add(CellDetection.CellCategorizer(getBitmapSource(startX,startY,sizeX,sizeY), ImageDataAccessor.ImageDataSource.PixelSpacing.SpacingX, parameterSet)); //CellCategorizer actually does the detection of the cells. The getBitmapSource is an in-built method from the zeiss.micro library
        result.Last().BlockDescriptor = new BlockDescriptor(startX, startY, 1500, 1500); //position of block in image

        if (result[count] != null)
        {

            int cellResultLength = result[count].CellResults.Count;
            for (int i = 0; i < cellResultLength; i++)  //All of the stuff in the loop is just to get the calculated cells in its own viewmodel to show a gallary of cells afterwars. This is not the problem
            {
                int startXCell = result[count].CellResults[i].CellsRectangle.X;

                int startYCell = result[count].CellResults[i].CellsRectangle.Y;
                int blockDX = result[count].BlockDescriptor.X;
                int blockDY = result[count].BlockDescriptor.Y;
                int rectangleWidth = result[count].CellResults[i].CellsRectangle.Width;
                int rectangleHeight = result[count].CellResults[i].CellsRectangle.Height;
                double cellDiameter = result[count].CellResults[i].CellDiameter;
                double feretMin = result[count].CellResults[i].FeretMinimum;
                double feretMax = result[count].CellResults[i].FeretMaximum;
                var state = result[count].CellResults[i].State;
                ImageBounds b = new ImageBounds(ImageBound);
                b.StartX = (startXCell + blockDX) - 10;
                b.StartY = (startYCell + blockDY) - 10;
                b.SizeX = rectangleWidth + 20;
                b.SizeY = rectangleHeight + 20;

                ImageObjectModel imageObjectModel = new ImageObjectModel(new ImageDataAccessor(selectedWorkspaceModel.ImageDataSource,b));

                if (state == CellResult.CellState.Match)
                {
                    imageObjectModel.State = ImageObjectModel.ClassificationState.Match;
                }
                else
                {
                    imageObjectModel.State = ImageObjectModel.ClassificationState.Mismatch;
                }

                imageObjectModel.StartX = (startXCell + blockDX);
                imageObjectModel.StartY = (startYCell + blockDY);

                imageObjectModel.CellDiameter = cellDiameter;
                imageObjectModel.FeretMax = feretMax;
                imageObjectModel.FeretMin = feretMin;

               ioms.Add(imageObjectModel); //Adds the iom to the list so that I can draw a rectangle around the cell after the detection
            }

        }

observeCollection ioms=selectedWorkspaceModel.ImageObjectModels；
结果.Add（CellDetection.CellCategorizer（getBitmapSource（startX、startY、sizeX、sizeY）、ImageDataAccessor.ImageDataSource.PixelSpacingx、parameterSet））//CellClassifier实际上是检测细胞的。getBitmapSource是来自蔡司微库的内置方法
result.Last（）.BlockDescriptor=新的块描述符（startX、startY、1500、1500）//块在图像中的位置
如果（结果[计数]！=null）
{
int-cellResultLength=result[count].CellResults.count；
for（int i=0；i

最后是使用AForge库的细胞分类方法：

 if (_inputSource == null) throw new Exception();
        var inputBitmap = BitmapFromSource(_inputSource);
        if (!validatePixelFormat(inputBitmap))
        {
            inputBitmap = transformBitmap(inputBitmap);
        }
        BitmapData bitmapData = inputBitmap.LockBits(
     new Rectangle(0, 0, inputBitmap.Width, inputBitmap.Height),
     ImageLockMode.ReadWrite, inputBitmap.PixelFormat);

        filter2.CenterColor = new RGB(255, 255, 255);
        filter2.Radius = 430;
        filter2.ApplyInPlace(bitmapData);

        filter.CoupledSizeFiltering = true;
        filter.MinWidth = 10;
        filter.MinHeight = 10;
        filter.MaxHeight = Int32.MaxValue;
        filter.MaxWidth = Int32.MaxValue;
        filter.ApplyInPlace(bitmapData);

        bc.FilterBlobs = true;
        bc.MinWidth = 10;
        bc.MinHeight = 10;
        bc.MaxHeight = Int32.MaxValue;
        bc.MaxWidth = Int32.MaxValue;
        bc.CoupledSizeFiltering = false;
        bc.ObjectsOrder = ObjectsOrder.Size;
        bc.ProcessImage(bitmapData);
        Blob[] blobs = bc.GetObjectsInformation();
        BlockResult resultOfBlock = new BlockResult();
        inputBitmap.UnlockBits(bitmapData);
        foreach (var rec in blobs)
        {
            var cuttedImage = inputBitmap.Clone(rec.Rectangle, inputBitmap.PixelFormat);//Need to get every cell seperated to perform further analysis
            double cellDiameter = Math.Sqrt(((rec.Area*(pixelSize*pixelSize)) / Math.PI)) * 2;
            cellDiameter *= 1000;
            var feretMax = calculateFeretMax( pixelSize,cuttedImage); //Similar things like here happen in this method so I don't want to spam you with extra code
            var feretMin = calculateFeretMin( pixelSize,cuttedImage);//Similar things like here happen in this method so I don't want to spam you with extra code
            CellResult result = new CellResult(Math.Round(cellDiameter,2),Math.Round( feretMax,2),Math.Round( feretMin,2));
            int countAccepted = 0;
            if ((feretMin > set.FeretMinimumMin && feretMin < set.FeretMinimumMax)) countAccepted++;
            if ((feretMax < set.FeretMaximumMax && feretMax > set.FeretMaximumMin)) countAccepted++;
            if ((cellDiameter > set.MinDiameter && cellDiameter < set.MaxDiameter)) countAccepted++;

            if(countAccepted>2) result.State = CellResult.CellState.Match;
            else if(countAccepted==2) result.State = CellResult.CellState.Unclassified;
            else result.State = CellResult.CellState.Mismatch;
            result.CellsRectangle = rec.Rectangle;
            resultOfBlock.CellResults.Add(result);

        }


        return resultOfBlock;

if（_inputSource==null）抛出新异常（）；
var inputBitmap=BitmapFromSource（_inputSource）；
如果（！validatePixelFormat（inputBitmap））
{
inputBitmap=转换位图（inputBitmap）；
}
BitmapData BitmapData=inputBitmap.LockBits(
新矩形（0，0，inputBitmap.Width，inputBitmap.Height），
ImageLockMode.ReadWrite，inputBitmap.PixelFormat）；
filter2.CenterColor=新的RGB（255、255、255）；
过滤器2.半径=430；
filter2.ApplyInPlace（位图数据）；
filter.CoupledSizeFiltering=true；
filter.MinWidth=10；
filter.MinHeight=10；
filter.MaxHeight=Int32.MaxValue；
filter.MaxWidth=Int32.MaxValue；
filter.ApplyInPlace（位图数据）；
bc.FilterBlobs=true；
bc.MinWidth=10；
bc.MinHeight=10；
bc.MaxHeight=Int32.MaxValue；
bc.MaxWidth=Int32.MaxValue；
bc.CoupledSizeFiltering=false；
bc.ObjectsOrder=ObjectsOrder.Size；
ProcessImage（位图数据）；
Blob[]blobs=bc.GetObjectsInformation（）；
BlockResult resultOfBlock=新的BlockResult（）；
inputBitmap.解锁位（bitmapData）；
foreach（BLOB中的var rec）
{
var cuttedImage=inputBitmap.Clone（rec.Rectangle，inputBitmap.PixelFormat）；//需要将每个单元格分离以执行进一步的分析
双细胞直径=Math.Sqrt（（（rec.Area*（pixelSize*pixelSize））/Math.PI））*2；
细胞直径*=1000；
var feretMax=calculateFeretMax（pixelSize，cuttedImage）；//类似的事情在这个方法中发生，所以我不想用额外的代码向您发送垃圾邮件
var feretMin=calculateFeretMin（像素大小，切割图像
 if (_inputSource == null) throw new Exception();
        var inputBitmap = BitmapFromSource(_inputSource);
        if (!validatePixelFormat(inputBitmap))
        {
            inputBitmap = transformBitmap(inputBitmap);
        }
        BitmapData bitmapData = inputBitmap.LockBits(
     new Rectangle(0, 0, inputBitmap.Width, inputBitmap.Height),
     ImageLockMode.ReadWrite, inputBitmap.PixelFormat);

        filter2.CenterColor = new RGB(255, 255, 255);
        filter2.Radius = 430;
        filter2.ApplyInPlace(bitmapData);

        filter.CoupledSizeFiltering = true;
        filter.MinWidth = 10;
        filter.MinHeight = 10;
        filter.MaxHeight = Int32.MaxValue;
        filter.MaxWidth = Int32.MaxValue;
        filter.ApplyInPlace(bitmapData);

        bc.FilterBlobs = true;
        bc.MinWidth = 10;
        bc.MinHeight = 10;
        bc.MaxHeight = Int32.MaxValue;
        bc.MaxWidth = Int32.MaxValue;
        bc.CoupledSizeFiltering = false;
        bc.ObjectsOrder = ObjectsOrder.Size;
        bc.ProcessImage(bitmapData);
        Blob[] blobs = bc.GetObjectsInformation();
        BlockResult resultOfBlock = new BlockResult();
        inputBitmap.UnlockBits(bitmapData);
        foreach (var rec in blobs)
        {
            var cuttedImage = inputBitmap.Clone(rec.Rectangle, inputBitmap.PixelFormat);//Need to get every cell seperated to perform further analysis
            double cellDiameter = Math.Sqrt(((rec.Area*(pixelSize*pixelSize)) / Math.PI)) * 2;
            cellDiameter *= 1000;
            var feretMax = calculateFeretMax( pixelSize,cuttedImage); //Similar things like here happen in this method so I don't want to spam you with extra code
            var feretMin = calculateFeretMin( pixelSize,cuttedImage);//Similar things like here happen in this method so I don't want to spam you with extra code
            CellResult result = new CellResult(Math.Round(cellDiameter,2),Math.Round( feretMax,2),Math.Round( feretMin,2));
            int countAccepted = 0;
            if ((feretMin > set.FeretMinimumMin && feretMin < set.FeretMinimumMax)) countAccepted++;
            if ((feretMax < set.FeretMaximumMax && feretMax > set.FeretMaximumMin)) countAccepted++;
            if ((cellDiameter > set.MinDiameter && cellDiameter < set.MaxDiameter)) countAccepted++;

            if(countAccepted>2) result.State = CellResult.CellState.Match;
            else if(countAccepted==2) result.State = CellResult.CellState.Unclassified;
            else result.State = CellResult.CellState.Mismatch;
            result.CellsRectangle = rec.Rectangle;
            resultOfBlock.CellResults.Add(result);

        }


        return resultOfBlock;