I'm new to image processing, I'm trying to segment lung CT images by Kmeans by the following:

import numpy as np
import re
import pandas as pd
from skimage import morphology
from skimage import measure
from PIL import Image
from sklearn.cluster import KMeans
from skimage.transform import resize
from glob import glob
import sys
import os
import cv2
from scipy.ndimage.morphology import binary_dilation,generate_binary_structure
from skimage.morphology import convex_hull_image

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)>2*np.sum(mask1):
                mask2 = mask1
            mask2 = mask1
        convex_mask[i_layer] = mask2
    struct = generate_binary_structure(3,1)
    dilatedMask = binary_dilation(convex_mask,structure=struct,iterations=10)

    return dilatedMask

def lumTrans(img):
    lungwin = np.array([-1200.,600.])
    newimg = (img-lungwin[0])/(lungwin[1]-lungwin[0])
    newimg = (newimg*255).astype('uint8')
    return newimg

def lungSeg(imgs_to_process,output,name):

    if os.path.exists(output+'/'+name+'_clean.npy') : return
    imgs_to_process = Image.open(imgs_to_process)
    img_to_save = imgs_to_process.copy()
    img_to_save = np.asarray(img_to_save).astype('uint8')

    imgs_to_process = lumTrans(imgs_to_process)    
    imgs_to_process = np.expand_dims(imgs_to_process, axis=0)
    x,y,z = imgs_to_process.shape 
    img_array = imgs_to_process.copy()  
    A1 = int(y/(512./100))
    A2 = int(y/(512./400))

    A3 = int(y/(512./475))
    A4 = int(y/(512./40))
    A5 = int(y/(512./470))
    for i in range(len(imgs_to_process)):
        img = imgs_to_process[i]
        x,y = img.shape
        #Standardize the pixel values
        allmean = np.mean(img)
        allstd = np.std(img)
        img = img-allmean
        img = img/allstd
        # Find the average pixel value near the lungs
        # to renormalize washed out images
        middle = img[A1:A2,A1:A2] 
        mean = np.mean(middle)  
        max = np.max(img)
        min = np.min(img)
        kmeans = KMeans(n_clusters=2).fit(np.reshape(middle,[np.prod(middle.shape),1]))
        centers = sorted(kmeans.cluster_centers_.flatten())
        threshold = np.mean(centers)
        thresh_img = np.where(img<threshold,1.0,0.0)  # threshold the image
        eroded = morphology.erosion(thresh_img,np.ones([4,4]))
        dilation = morphology.dilation(eroded,np.ones([10,10]))
        labels = measure.label(dilation)
        label_vals = np.unique(labels)
        regions = measure.regionprops(labels)
        good_labels = []
        for prop in regions:
            B = prop.bbox
            if B[2]-B[0]<A3 and B[3]-B[1]<A3 and B[0]>A4 and B[2]<A5:
        mask = np.ndarray([x,y],dtype=np.int8)
        mask[:] = 0
        for N in good_labels:
            mask = mask + np.where(labels==N,1,0)
        mask = morphology.dilation(mask,np.ones([10,10])) # one last dilation
        imgs_to_process[i] = mask

    m1 = imgs_to_process
    convex_mask = m1
    dm1 = process_mask(m1)
    dilatedMask = dm1
    Mask = m1
    extramask = dilatedMask ^ Mask
    bone_thresh = 180
    pad_value = 0

    sliceim = img_array
    sliceim = sliceim*dilatedMask+pad_value*(1-dilatedMask).astype('uint8')
    bones = sliceim*extramask>bone_thresh
    sliceim[bones] = pad_value

    x,y,z = sliceim.shape
    if not os.path.exists(output): 
    img_to_save[sliceim.squeeze()==0] = 0
    im = Image.fromarray(img_to_save)

    im.save(output + name + '.png', 'PNG')

The lung segment method calling:

lungSeg(image_path, new_image_path, image_name)

The problem is the segmented lung still contains white borderers like this:

segmented lung (output):

segmented lung

unsegmented lung (input):

unsegmented lung

The whole code here https://colab.research.google.com/drive/1gdZi7dv2bo4MNgR9suuM1ZCMItSWXggl?usp=sharing


Try using Erode and Dilation Operations to remove the white area.

  • $\begingroup$ Please add further details to expand on your answer, such as working code or documentation citations. $\endgroup$
    – Community Bot
    Sep 9 at 10:33

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