smoother.simulation.histology

Extract spatial patterns from a given histology image

Attributes

ImageContainer

Functions

get_features_matrix(crop, layer)	Extract features from a given crop
extract_features_from_image(image, crop_size, layer)	Extract features from a given image
cal_membership_value(membership_mat, feature_df, ...)	Calculate fuzzy-c-means membership values

Module Contents

smoother.simulation.histology.ImageContainer = None

smoother.simulation.histology.get_features_matrix(crop: squidpy.im.ImageContainer, layer)

Extract features from a given crop

Parameters:

• crop – crop of a image

• layer – Image layer in crop that should be processed

Returns:

the extracted features at this crop

Return type:

df_1

smoother.simulation.histology.extract_features_from_image(image: squidpy.im.ImageContainer, crop_size, layer)

Extract features from a given image

Parameters:

• image – squidpy ImageContainer

• crop_size – the # of crops wanted [n_row_crops, n_col_crops]

• layer – Image layer in image that should be processed

Returns:

the extracted features at all crops

Return type:

features_df

smoother.simulation.histology.cal_membership_value(membership_mat, feature_df, cluster_centers, m)

Calculate fuzzy-c-means membership values

Parameters:

• membership_mat – membership matrix n_location x n_cluster

• feature_df – features matrix n_location x n_feature

• cluster_centers – center point for clusters n_cluster x n_feature

• m – m value required by FCM

Returns:

updated membership matrix n_location x n_cluster

Return type:

membership_mat