dm_preprocess.py 文件源码

def remove_pectoral(self, img, breast_mask, high_int_threshold=.8, 
                        morph_kn_size=3, n_morph_op=7, sm_kn_size=25):
        '''Remove the pectoral muscle region from an input image

        Args:
            img (2D array): input image as a numpy 2D array.
            breast_mask (2D array):
            high_int_threshold ([int]): a global threshold for high intensity 
                    regions such as the pectoral muscle. Default is 200.
            morph_kn_size ([int]): kernel size for morphological operations 
                    such as erosions and dilations. Default is 3.
            n_morph_op ([int]): number of morphological operations. Default is 7.
            sm_kn_size ([int]): kernel size for final smoothing (i.e. opening). 
                    Default is 25.
        Returns:
            an output image with pectoral muscle region removed as a numpy 
            2D array.
        Notes: this has not been tested on .dcm files yet. It may not work!!!
        '''
        # Enhance contrast and then thresholding.
        img_equ = cv2.equalizeHist(img)
        if high_int_threshold < 1.:
            high_th = int(img.max()*high_int_threshold)
        else:
            high_th = int(high_int_threshold)
        maxval = self.max_pix_val(img.dtype)
        _, img_bin = cv2.threshold(img_equ, high_th, 
                                   maxval=maxval, type=cv2.THRESH_BINARY)
        pect_marker_img = np.zeros(img_bin.shape, dtype=np.int32)
        # Sure foreground (shall be pectoral).
        pect_mask_init = self.select_largest_obj(img_bin, lab_val=maxval, 
                                                 fill_holes=True, 
                                                 smooth_boundary=False)
        kernel_ = np.ones((morph_kn_size, morph_kn_size), dtype=np.uint8)
        pect_mask_eroded = cv2.erode(pect_mask_init, kernel_, 
                                     iterations=n_morph_op)
        pect_marker_img[pect_mask_eroded > 0] = 255
        # Sure background - breast.
        pect_mask_dilated = cv2.dilate(pect_mask_init, kernel_, 
                                       iterations=n_morph_op)
        pect_marker_img[pect_mask_dilated == 0] = 128
        # Sure background - pure background.
        pect_marker_img[breast_mask == 0] = 64
        # Watershed segmentation.
        img_equ_3c = cv2.cvtColor(img_equ, cv2.COLOR_GRAY2BGR)
        cv2.watershed(img_equ_3c, pect_marker_img)
        img_equ_3c[pect_marker_img == -1] = (0, 0, 255)
        # Extract only the breast and smooth.
        breast_only_mask = pect_marker_img.copy()
        breast_only_mask[breast_only_mask == -1] = 0
        breast_only_mask = breast_only_mask.astype(np.uint8)
        breast_only_mask[breast_only_mask != 128] = 0
        breast_only_mask[breast_only_mask == 128] = 255
        kernel_ = np.ones((sm_kn_size, sm_kn_size), dtype=np.uint8)
        breast_only_mask = cv2.morphologyEx(breast_only_mask, cv2.MORPH_OPEN, 
                                            kernel_)
        img_breast_only = cv2.bitwise_and(img_equ, breast_only_mask)

        return (img_breast_only, img_equ_3c)