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https://github.com/fitushar/automatic_breast_region_extraction_using_python

This Repo will contain Python Implementation of an Automatic approach to Extraction Breast Region from Memogram
https://github.com/fitushar/automatic_breast_region_extraction_using_python

breast-region-extraction cv cv2 image-processing medical-image-analysis medical-image-processing medical-image-segmentation python tomography

Last synced: 29 days ago
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This Repo will contain Python Implementation of an Automatic approach to Extraction Breast Region from Memogram

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# Automatic_Breast_Region_Extraction_using_python_OpenCV

Braest region extraction from the memogram often the initaial stage to do image analysis or dignosis. This Repo contains Python Implementation of an Automatic approach to Extraction Breast Region from Memogram. All you need to do is to set the threshold based on your memogram vendor, Different vendors could have different ranges



Here is public dataset to try the code: https://www.dclunie.com/pixelmedimagearchive/upmcdigitalmammotomocollection/index.html



### Breast Region Extraction Algorithm works as following: Jupyter notebook have all the codes

```ruby  

   i) Load Dicom and Threshold based on your vendor

  ii) Connected Component Analaysis and Keep the Largest Mask

 iii) Erode to remove unwanted object in memo.

  iv) Label Erode Image and Replace all Non-ROI with background pixel

   v) Finally dilat the mask to recover the eroded Breast Region

```  



![Simple Preocess](https://github.com/fitushar/Automatic_Breast_Region_Extraction_using_python/blob/master/Figure/Process.png)



## Code is below

```ruby

## import Libraries

import cv2

import numpy as np

import os, glob

import sys

import pydicom

from scipy.misc import imsave,imread

import pandas as pd

from skimage import measure

from skimage.measure import label

import matplotlib.pyplot as plt



## Getting The Brest_Refion_using_Connected_Component_Analaysis

def select_largest_obj(img_bin, lab_val=255, smooth_boundary=False, kernel_size=15):

    n_labels, img_labeled, lab_stats, _ = cv2.connectedComponentsWithStats(img_bin, connectivity=8, ltype=cv2.CV_32S)

    largest_obj_lab_roi = np.argmax(lab_stats[1:, 4]) + 1

    largest_mask_roi = np.zeros(img_bin.shape, dtype=np.uint8)

    largest_mask_roi[img_labeled == largest_obj_lab_roi] = lab_val

    if smooth_boundary:

        kernel_ = np.ones((kernel_size, kernel_size), dtype=np.uint8)

        largest_mask_roi = cv2.morphologyEx(largest_mask_roi, cv2.MORPH_OPEN, kernel_)

    return largest_mask_roi



## Dicom Fliping_name

def Flip_memo(dicom_file):

    img=dicom_file.pixel_array

    try:

        flip=dicom_file[0x0018,0x7034].value.lower()

        if 'y' in flip:

            img=np.fliplr(img)

    except:

        pass

    return img



## Generate Breast Region Bases on Threshold Image

def Generate_Brest_roi_mask(dcm_img):

    Threshold_img = np.array(dcm_img, dtype=np.uint8)

    mask = select_largest_obj(Threshold_img , lab_val=255, smooth_boundary=True, kernel_size=15)

    #Erosion

    Erosion_kernel = np.ones((125,125),np.uint8)

    Erosion = cv2.erode(mask,Erosion_kernel,iterations =1)

    #Label-The Image

    lab_mask_init = label(Erosion,background=0)

    lab_ind,counts_ind = np.unique(lab_mask_init,return_counts  = True)

    #-Shorting the label

    uni_feat,counts_feat = np.unique(lab_mask_init,return_counts = True)

    sorted_feat = uni_feat[np.flipud(np.argsort(counts_feat))]

    sorted_counts = np.flipud(np.sort(counts_feat))

    

    if sorted_feat[0]==0:

        memo_region_index=sorted_feat[1]

    else:

        memo_region_index=sorted_feat[0]

    black=np.where((lab_mask_init!=memo_region_index))

    #black=np.where((lab_mask_init!=1))

    Erosion[black]=0

    # Finally Filling some regions

    Erosion=Erosion.astype('bool').astype('uint8')

    Dilat_kernel= np.ones((125,125),np.uint8)

    dilat = cv2.dilate(Erosion,Dilat_kernel,iterations =1)

    # if you want to refine the mask more than do one more step dilation

    #Dilat_kernel2= np.ones((10,10),np.uint8)

    #Dilat_kernel2=cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(25,25))

    #dilat = cv2.dilate(dilat,Dilat_kernel2,iterations =1)

    return dilat

#plot    

def Ploting_function(dcm,dcm_roi):



    f, axarr = plt.subplots(1,3,figsize=(30,30));

    axarr[0].imshow(dcm, cmap=plt.cm.gray);

    axarr[0].axis('off')

    axarr[0].set_title('Memo',fontsize = 30)



    axarr[1].imshow(dcm_roi, cmap='gray',alpha=1);

    axarr[1].axis('off')

    axarr[1].set_title('Breast Region mask',fontsize = 30)



    axarr[2].imshow(dcm, cmap='gray',alpha=1);

    axarr[2].imshow(dcm_roi, cmap='copper',alpha=0.5);

    axarr[2].axis('off')

    axarr[2].set_title('Breast ROI Overlay',fontsize = 30)



    plt.tight_layout()

    #plt.close()

    return

```



### Sample Run:

```ruby

one_dcm='1.3.6.1.4.1.5962.99.1.2280943358.716200484.1363785608958.532.0.dcm'

ds  =pydicom.read_file(one_dcm)

dcm = Flip_memo(ds)

ret,thresh = cv2.threshold(dcm,60,255,0)

Breast_roi=Generate_Brest_roi_mask(thresh)

Ploting_function(dcm,Breast_roi)

```

![Simple Preocess](https://github.com/fitushar/Automatic_Breast_Region_Extraction_using_python/blob/master/Figure/demo1.png)

![Simple Preocess](https://github.com/fitushar/Automatic_Breast_Region_Extraction_using_python/blob/master/Figure/demo2.png)

![Simple Preocess](https://github.com/fitushar/Automatic_Breast_Region_Extraction_using_python/blob/master/Figure/demo3.png)