binary.segmentation {SAFARI}

Segmentation Procedure for Binary Images

Description

Identify and segment the regions of interest (ROI) in a reconstructed binary image, while having the option to extract quantitative shape-based features.

Usage

binary.segmentation(
  x,
  id,
  filter = NA,
  k = 3,
  categories = c("none", "geometric", "boundary", "topological")
)

Arguments

x

a binary matrix that represents the reconstructed image.

id

a named character vector that contains the ID's pertaining to the sample.

filter

an integer vector that indicates the filtering procedure.

k

an integer that specifies the factor to enlarge the regions.

categories

a character string or vector, see compute.features.

Details

The argument id specifies the ID's corresponding to the sample. A usual example that is used if the argument is an unnamed vector is composed of the following entries:

• cohort: name of cohort the sample belongs to;

• patient.id: unique identifier for patient the sample belongs to;

• slide.id: unique identifier for sample.

The argument filter specifies how the ROI should be filtered. There are two ways to filter them, either by only specifying a minimum net area or by additionally specifying the n largest regions to keep in a two element vector. The default value is NA which sets the minimum net area as one-fourth the largest region.

The argument categories specifies the shape features to compute. The default is "none" which computes no features.

Value

A list object whose components are the following:

• desc: a data.frame of the shape features corresponding to each segmented ROI;

• holes: an integer matrix that contains the holes within each ROI, labeled according to the regions;

• id: a character vector that is identical to the id argument passed;

• k: argument k passed to function;

• n: an integer that indicates the number of segmented regions;

• plg.chains: a list where each component is the polygonal chain of a segmented ROI;

• regions: an integer matrix that contains the segmented ROI, labeled from largest to smallest.

Reconstructed Binary Image

To produce a reconstructed binary image, a greyscale scan or an image easily converted to binary, resulting from different modalities, is converted to a matrix representation, usually by standard image processing algorithms.

The resulting matrix is composed of two integer values that help represent the regions of interest in the scan. Usually, as in the case of pathology imaging, these are empty regions and tumor tissues, where we refer to the integer values as categories. We note that the regions of interest must be represented by 1's and what we consider the empty regions by 0's.

Examples

# load libraries
library(SAFARI)

# load sample
data("rBPS")

# segmentation procedure
rBPS <- binary.segmentation(
    rBPS,
    id = c("NLST", "AA00474", "11030"),
    filter = 150,
    categories = c("geometric", "boundary", "topological")
)