Python skimage.morphology.convex_hull_image() Examples

The following are 7 code examples of skimage.morphology.convex_hull_image(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module skimage.morphology , or try the search function .
Example #1
Source File: getTerminationBifurcation.py    From Fingerprint-Feature-Extraction with MIT License 6 votes vote down vote up 
def getTerminationBifurcation(img, mask):
    img = img == 255;
    (rows, cols) = img.shape;
    minutiaeTerm = np.zeros(img.shape);
    minutiaeBif = np.zeros(img.shape);
    
    for i in range(1,rows-1):
        for j in range(1,cols-1):
            if(img[i][j] == 1):
                block = img[i-1:i+2,j-1:j+2];
                block_val = np.sum(block);
                if(block_val == 2):
                    minutiaeTerm[i,j] = 1;
                elif(block_val == 4):
                    minutiaeBif[i,j] = 1;
    
    mask = convex_hull_image(mask>0)
    mask = erosion(mask, square(5))         # Structuing element for mask erosion = square(5)
    minutiaeTerm = np.uint8(mask)*minutiaeTerm
    return(minutiaeTerm, minutiaeBif)
Example #2
Source File: prepare.py    From DeepLung with GNU General Public License v3.0 5 votes vote down vote up 
def process_mask(mask):
    convex_mask = np.copy(mask)
    for i_layer in range(convex_mask.shape[0]):
        mask1  = np.ascontiguousarray(mask[i_layer])
        if np.sum(mask1)>0:
            mask2 = convex_hull_image(mask1)
            if np.sum(mask2)>1.5*np.sum(mask1):
                mask2 = mask1
        else:
            mask2 = mask1
        convex_mask[i_layer] = mask2
    struct = generate_binary_structure(3,1)  
    dilatedMask = binary_dilation(convex_mask,structure=struct,iterations=10) 
    return dilatedMask
Example #3
Source File: volume.py    From pyAFQ with BSD 2-Clause "Simplified" License 5 votes vote down vote up 
def patch_up_roi(roi):
    """
    After being non-linearly transformed, ROIs tend to have holes in them.
    We perform a couple of computational geometry operations on the ROI to
    fix that up.

    Parameters
    ----------
    roi : 3D binary array
        The ROI after it has been transformed.

    sigma : float
        The sigma for initial Gaussian smoothing.

    truncate : float
        The truncation for the Gaussian

    Returns
    -------
    ROI after dilation and hole-filling
    """

    hole_filled = ndim.binary_fill_holes(roi > 0)
    try:
        return convex_hull_image(hole_filled)
    except QhullError:
        return hole_filled
Example #4
Source File: prepare.py    From lung_nodule_detector with MIT License 5 votes vote down vote up 
def process_mask(mask):
    convex_mask = np.copy(mask)
    for i_layer in range(convex_mask.shape[0]):
        mask1  = np.ascontiguousarray(mask[i_layer])
        if np.sum(mask1)>0:
            mask2 = convex_hull_image(mask1)
            if np.sum(mask2)>1.5*np.sum(mask1):
                mask2 = mask1
        else:
            mask2 = mask1
        convex_mask[i_layer] = mask2
    struct = generate_binary_structure(3,1)  
    dilatedMask = binary_dilation(convex_mask,structure=struct,iterations=10) 
    return dilatedMask
Example #5
Source File: prepare.py    From DeepSEED-3D-ConvNets-for-Pulmonary-Nodule-Detection with MIT License 5 votes vote down vote up 
def process_mask(mask):
    convex_mask = np.copy(mask)
    for i_layer in range(convex_mask.shape[0]):
        mask1  = np.ascontiguousarray(mask[i_layer])
        if np.sum(mask1)>0:
            mask2 = convex_hull_image(mask1)
            if np.sum(mask2)>1.5*np.sum(mask1):
                mask2 = mask1
        else:
            mask2 = mask1
        convex_mask[i_layer] = mask2
    struct = generate_binary_structure(3,1)  
    dilatedMask = binary_dilation(convex_mask,structure=struct,iterations=10) 
    return dilatedMask
Example #6
Source File: prepareLIDC.py    From DeepSEED-3D-ConvNets-for-Pulmonary-Nodule-Detection with MIT License 5 votes vote down vote up 
def process_mask(mask):
    convex_mask = np.copy(mask)
    for i_layer in range(convex_mask.shape[0]):
        mask1  = np.ascontiguousarray(mask[i_layer])
        if np.sum(mask1)>0:
            mask2 = convex_hull_image(mask1)
            if np.sum(mask2)>1.5*np.sum(mask1):
                mask2 = mask1
        else:
            mask2 = mask1
        convex_mask[i_layer] = mask2
    struct = generate_binary_structure(3,1)  
    dilatedMask = binary_dilation(convex_mask,structure=struct,iterations=10) 
    return dilatedMask
Example #7
Source File: segmentation_labelling.py    From kaggle-heart with MIT License 5 votes vote down vote up 
def wrapper_regions(bestregions, opening_param = 3, mshape = ((0,1,0),(1,1,1),(0,1,0)) ):

    zdim, xdim, ydim = bestregions.shape

    wregions = np.zeros_like(bestregions)

    for sidx in range(zdim):
        if np.sum(bestregions[sidx]) > 0:
            wregions[sidx] = convex_hull_image(bestregions[sidx])

    return wregions