| Title: | Feature Extraction from Female Brown Anole Lizard Dorsal Patterns |
|---|---|
| Description: | Provides a set of functions to efficiently recognize and clean the continuous dorsal pattern of a female brown anole lizard (Anolis sagrei) traced from 'ImageJ', an open platform for scientific image analysis (see <https://imagej.net> for more information), and extract common features such as the pattern sinuosity indices, coefficient of variation, and max-min width. |
| Authors: | Seong Hyun Hwang, Rachel Myoung Moon |
| Maintainer: | Seong Hyun Hwang <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.1.0 |
| Built: | 2025-03-13 03:30:22 UTC |
| Source: | https://github.com/cran/patternator |
A sample dorsal pattern pixel image of a female brown anole lizard traced from the ImageJ software.
anoleanole
A data.table with 1675 rows and 2 variables:
x-coordinate of a pixel
y-coordinate of a pixel
Moon and Kamath (2016), Examining the Ecological, Morphological, and Behavioral Correlates of Dorsal Pattern Variations in Female Brown Anole Lizards (Anolis sagrei).
The function clean_patterns implements a k-means clustering-based automatic cleaning of the continuous dorsal pattern of a female brown anole lizard
traced from the ImageJ software.
clean_patterns(data, kmeans = TRUE, seed = 123, outliers = TRUE)clean_patterns(data, kmeans = TRUE, seed = 123, outliers = TRUE)
data |
a data.table or data.frame: an input data should have two columns |
kmeans |
logical, whether to use k-means clustering to eliminate a reference pixel, if any. Defaults to TRUE. See the details below. |
seed |
a single value, interpreted as an integer with the default set to 123. |
outliers |
logical, whether to eliminate potential outliers in the x-coordinate even after removing the 1cm reference line with k-means clustering. Defaults to TRUE. |
clean_patterns implements a k-means clustering-based automatic cleaning of the continuous dorsal pattern of a female brown anole lizard, Anolis sagrei,
traced from ImageJ, an open source image processing program
designed for scientific multidimensional images. The function efficiently
eliminates the 1cm reference pixel and possible outliers in the x direction,
randomly chooses a mid-dorsal axis if there exist more than one,
chooses the largest x-coordinate if multiple x-coordinates are given per y-coordinate,
manages left or right dorsal pattern that heavily crosses over the mid-dorsal axis by first removing the mid-dorsal axis and then regrouping left and right pattern,
removes pixels through which left or right pattern crosses over since empirically it has little impact on the values of the extracted features, see extract_features function,
handles left or right dorsal pattern broken with a gap
Returns a data.table object with the following three columns:
x, y
the xy-coordinate of a pixel; type "numeric"
locthe location label of a pixel, one of LEFT, RIGHT, MID; type "character"
Seong Hyun Hwang, Rachel Myoung Moon
# load the sample dorsal pattern image data(anole) # plot of the pattern shows it contains the reference pixel plot(anole$x, anole$y) # remove the reference pixel, possible outliers and ambiguities cleaned <- clean_patterns(anole) # check the plot again plot(cleaned$x, cleaned$y)# load the sample dorsal pattern image data(anole) # plot of the pattern shows it contains the reference pixel plot(anole$x, anole$y) # remove the reference pixel, possible outliers and ambiguities cleaned <- clean_patterns(anole) # check the plot again plot(cleaned$x, cleaned$y)
The function extract_features efficiently extracts various features
such as the pattern sinuosity indices, coefficient of variation, and max-min width
from the output of clean_patterns.
extract_features(data)extract_features(data)
data |
a |
extract_features efficiently extracts common features from the continuous dorsal pattern of a female brown anole lizard, Anolis sagrei,
such as the pattern sinuosity indices, coefficient of variation, and max-min width.
The input data should either be a data.table or data.frame object with the columns indicating the xy-coordinates and the location of the pixels.
Returns a data.table object with the following columns:
lt_psi, rt_psi
left/right pattern sinuosity index (PSI), computed as lt_len / md_len and rt_len / md_len, respectively
av_psiaverage pattern sinuosity index (PSI), (ls_ind + rs_ind) / 2
lt_pcv, rt_pcv
left/right pattern coefficient of variation (PCV), computed by dividing the standard deviation of the distance values between mid-dorsal axis and left/right pattern by the average distance.
av_pcvaverage pattern coefficient of variation (PCV), (lt_pcv + rt_pcv) / 2
max_width, min_width
the maximum and the minimum width between the left and the right pattern
av_widthaverage width between the left and the right pattern
pmmpattern max-min width (PMM), (max_width - min_width) / av_width
pasypattern asymmetry index (PASY), computed by first subtracting the distance between mid-dorsal axis and left pattern from the corresponding distance between mid-dorsal axis and right pattern and then taking the average of the resulting differences; the closer to zero it is, the more symmetric the dorsal pattern is on average
lt_len, rt_len, md_len
the length (the count of pixels) of the left pattern, the right pattern, and the mid-dorsal axis, respectively
Seong Hyun Hwang, Rachel Myoung Moon
# load the sample dorsal pattern image data(anole) # clean the dorsal pattern and extract quantitative features features <- extract_features(clean_patterns(anole))# load the sample dorsal pattern image data(anole) # clean the dorsal pattern and extract quantitative features features <- extract_features(clean_patterns(anole))