scikit-image

scikit-image - Scientific Image Processing

scikit-image treats images as NumPy arrays. It provides a comprehensive suite of algorithms for filtering, feature detection, and object measurement, making it the standard for research-grade image analysis.

When to Use

• Preprocessing scientific images (noise reduction, contrast enhancement).
• Image segmentation (separating cells, particles, or regions of interest).
• Feature extraction (detecting edges, corners, blobs, or textures).
• Geometric transformations (rescaling, rotating, warping).
• Morphological operations (thinning, skeletonization, hole filling).
• Measuring object properties (area, perimeter, eccentricity).
• Restoring degraded images (deconvolution, inpainting).

Reference Documentation

Official docs: https://scikit-image.org/
User Guide: https://scikit-image.org/docs/stable/user_guide.html
Search patterns: skimage.filters, skimage.segmentation, skimage.feature, skimage.morphology

Core Principles

Images are NumPy Arrays

A grayscale image is a 2D array (M, N). A color image is a 3D array (M, N, 3). A multichannel 3D volume is (P, M, N, C).

Coordinate System

The origin (0, 0) is at the top-left corner. Coordinates are always represented as (row, column).

Data Types and Ranges

scikit-image handles various dtypes with specific ranges:

• uint8: 0 to 255
• uint16: 0 to 65535
• float: -1 to 1 or 0 to 1

Quick Reference

Installation

pip install scikit-image

Standard Imports

import numpy as np
import matplotlib.pyplot as plt
from skimage import io, filters, segmentation, feature, measure, morphology, color, util

Basic Pattern - Load and Filter

from skimage import io, filters, color

# Load image
image = io.imread('data.png')

# Convert to grayscale if needed
gray_image = color.rgb2gray(image)

# Apply a filter (e.g., Gaussian blur)
blurred = filters.gaussian(gray_image, sigma=2.0)

# Display
io.imshow(blurred)
io.show()