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Visualize ROI Overlap and Dice Coefficient

Source: R/visualization.r
plot_roi_overlap.Rd

Visually compares two segmentation masks (ROIs) by overlaying them on the original thermal image. This function is primarily used for validation purposes: to compare an automated segmentation (filled layer) against a manual ground truth (contour line). It automatically calculates and displays the Dice Similarity Coefficient (DSC) in the title.

Usage

plot_roi_overlap(
  img_obj1,
  img_obj2,
  title = NULL,
  color = "green",
  alpha = 0.5,
  line_color = "white",
  palette = "inferno"
)

Arguments

img_obj1

A 'BioThermR' object. Typically the Automated/Predicted segmentation. This object must contain the raw thermal matrix. Its mask will be plotted as a filled area.

img_obj2

A 'BioThermR' object. Typically the Manual/Ground Truth segmentation. Its mask will be plotted as a contour outline. Dimensions must match img_obj1.

title

String. Custom title for the plot. If NULL (default), the title shows "ROI overlap (DICE: X.XXX)".

color

String. Fill color for the img_obj1 mask. Default is "green".

alpha

Numeric. Transparency level for the img_obj1 mask (0 to 1). Default is 0.5.

line_color

String. Line color for the img_obj2 contour. Default is "white".

palette

String. Color palette for the background thermal image (passed to scale_fill_viridis_c). Default is "inferno".

Value

A ggplot object showing the overlay.

Details

The visualization consists of three layers:

  1. Background: The raw thermal image from img_obj1.

  2. Prediction (img_obj1): The processed mask from the first object, rendered as a semi-transparent filled raster (default green).

  3. Ground Truth (img_obj2): The processed mask from the second object, rendered as a contour outline (default white).

The function calculates the Dice Similarity Coefficient (DSC) using the formula: $$DSC = \frac{2 \times |X \cap Y|}{|X| + |Y|}$$ where X and Y are the set of pixels in the two masks. A DSC of 1 indicates perfect overlap.

Examples

if (FALSE) { # \dontrun{
# 1. Automated Segmentation
auto_obj <- roi_segment_ebimage(my_image)

# 2. Manual Segmentation
manual_obj <- roi_filter_interactive(my_image)

# 3. Compare them
plot_roi_overlap(img_obj1 = auto_obj,
                 img_obj2 = manual_obj,
                 color = "blue",
                 line_color = "red")
} # }