- SciPy - Home
- SciPy - Introduction
- SciPy - Environment Setup
- SciPy - Basic Functionality
- SciPy - Relationship with NumPy
- SciPy Clusters
- SciPy - Clusters
- SciPy - Hierarchical Clustering
- SciPy - K-means Clustering
- SciPy - Distance Metrics
- SciPy Constants
- SciPy - Constants
- SciPy - Mathematical Constants
- SciPy - Physical Constants
- SciPy - Unit Conversion
- SciPy - Astronomical Constants
- SciPy - Fourier Transforms
- SciPy - FFTpack
- SciPy - Discrete Fourier Transform (DFT)
- SciPy - Fast Fourier Transform (FFT)
- SciPy Integration Equations
- SciPy - Integrate Module
- SciPy - Single Integration
- SciPy - Double Integration
- SciPy - Triple Integration
- SciPy - Multiple Integration
- SciPy Differential Equations
- SciPy - Differential Equations
- SciPy - Integration of Stochastic Differential Equations
- SciPy - Integration of Ordinary Differential Equations
- SciPy - Discontinuous Functions
- SciPy - Oscillatory Functions
- SciPy - Partial Differential Equations
- SciPy Interpolation
- SciPy - Interpolate
- SciPy - Linear 1-D Interpolation
- SciPy - Polynomial 1-D Interpolation
- SciPy - Spline 1-D Interpolation
- SciPy - Grid Data Multi-Dimensional Interpolation
- SciPy - RBF Multi-Dimensional Interpolation
- SciPy - Polynomial & Spline Interpolation
- SciPy Curve Fitting
- SciPy - Curve Fitting
- SciPy - Linear Curve Fitting
- SciPy - Non-Linear Curve Fitting
- SciPy - Input & Output
- SciPy - Input & Output
- SciPy - Reading & Writing Files
- SciPy - Working with Different File Formats
- SciPy - Efficient Data Storage with HDF5
- SciPy - Data Serialization
- SciPy Linear Algebra
- SciPy - Linalg
- SciPy - Matrix Creation & Basic Operations
- SciPy - Matrix LU Decomposition
- SciPy - Matrix QU Decomposition
- SciPy - Singular Value Decomposition
- SciPy - Cholesky Decomposition
- SciPy - Solving Linear Systems
- SciPy - Eigenvalues & Eigenvectors
- SciPy Image Processing
- SciPy - Ndimage
- SciPy - Reading & Writing Images
- SciPy - Image Transformation
- SciPy - Filtering & Edge Detection
- SciPy - Top Hat Filters
- SciPy - Morphological Filters
- SciPy - Low Pass Filters
- SciPy - High Pass Filters
- SciPy - Bilateral Filter
- SciPy - Median Filter
- SciPy - Non - Linear Filters in Image Processing
- SciPy - High Boost Filter
- SciPy - Laplacian Filter
- SciPy - Morphological Operations
- SciPy - Image Segmentation
- SciPy - Thresholding in Image Segmentation
- SciPy - Region-Based Segmentation
- SciPy - Connected Component Labeling
- SciPy Optimize
- SciPy - Optimize
- SciPy - Special Matrices & Functions
- SciPy - Unconstrained Optimization
- SciPy - Constrained Optimization
- SciPy - Matrix Norms
- SciPy - Sparse Matrix
- SciPy - Frobenius Norm
- SciPy - Spectral Norm
- SciPy Condition Numbers
- SciPy - Condition Numbers
- SciPy - Linear Least Squares
- SciPy - Non-Linear Least Squares
- SciPy - Finding Roots of Scalar Functions
- SciPy - Finding Roots of Multivariate Functions
- SciPy - Signal Processing
- SciPy - Signal Filtering & Smoothing
- SciPy - Short-Time Fourier Transform
- SciPy - Wavelet Transform
- SciPy - Continuous Wavelet Transform
- SciPy - Discrete Wavelet Transform
- SciPy - Wavelet Packet Transform
- SciPy - Multi-Resolution Analysis
- SciPy - Stationary Wavelet Transform
- SciPy - Statistical Functions
- SciPy - Stats
- SciPy - Descriptive Statistics
- SciPy - Continuous Probability Distributions
- SciPy - Discrete Probability Distributions
- SciPy - Statistical Tests & Inference
- SciPy - Generating Random Samples
- SciPy - Kaplan-Meier Estimator Survival Analysis
- SciPy - Cox Proportional Hazards Model Survival Analysis
- SciPy Spatial Data
- SciPy - Spatial
- SciPy - Special Functions
- SciPy - Special Package
- SciPy Advanced Topics
- SciPy - CSGraph
- SciPy - ODR
- SciPy Useful Resources
- SciPy - Reference
- SciPy - Quick Guide
- SciPy - Cheatsheet
- SciPy - Useful Resources
- SciPy - Discussion
SciPy Image Segmentation
Image Segmentation in SciPy
Image segmentation in SciPy is the process of dividing an image into distinct regions or objects based on certain characteristics such as pixel intensity, color, texture or boundaries. The goal of Image Segmentation is to simplify the image for further analysis by identifying meaningful structures or separating the foreground from the background.
In SciPy the scipy.ndimage module provides a variety of tools and algorithms for image segmentation. These are primarily used in pre-processing, feature extraction and analysis tasks in scientific and engineering applications.
Key concepts in Image segmentation
Image segmentation is an essential process in image analysis and computer vision which involves in the division of an image into meaningful regions. Below are the key concepts that underpin this technique −
Segmentation Objective
The main goal of the Image segmentation is to simplify an image by identifying and isolating regions or objects of interest. This mainly focus on pixels within a segment share similar properties like intensity, color or texture and distinct segments differ significantly.
Homogeneity & Discontinuity
In image segmentation, the goal is to partition an image into regions that are meaningful for further analysis or processing. There are two key concepts that often guide this segmentation process are homogeneity and discontinuity. These concepts help in defining the criteria for how pixels or regions of an image should be grouped or separated.
Homogeneity
Homogeneity refers to the similarity or uniformity within a region of an image. In the view of image segmentation it is the property of pixels or regions that share similar characteristics such as color, intensity, texture or other features.
The role of Homogeneity in Segmentation is to seek the group pixels with similar characteristics into the same region. For example pixels that have similar intensity or color values are considered part of the same homogeneous region and a region is considered homogeneous if its pixels are within a defined range of similarity.
Discontinuity
Discontinuity refers to abrupt changes or differences in pixel values that separate different regions in an image. These discontinuities can occur in terms of intensity, color or texture and are typically found at object boundaries.
Discontinuity-based segmentation techniques attempt to identify edges or boundaries where there is a sharp contrast in pixel values. These discontinuities are typically used to divide an image into separate regions or objects.
Segmentation based on discontinuity often focuses on detecting edges or boundaries between different regions, which are areas where the pixel intensities or features change abruptly.
Types of Image Segmentation Techniques
Following different types of Image Segmentation techniques available in SciPy −
| Segmentation Technique | Description | Implementation | Applications |
|---|---|---|---|
| Thresholding | Segments the image based on pixel intensity, classifying pixels above or below a threshold as different regions. | No separate function is available | Global Thresholding, Adaptive Thresholding |
| Region-Based Segmentation | Groups pixels into regions based on shared properties like intensity, color, or texture. | ndimage.label(), measurements.find_objects() | Connected Component Labeling, Watershed Segmentation |
| Edge-Based Segmentation | Detects boundaries between regions by identifying rapid changes in intensity (edges). | ndimage.sobel(), ndimage.laplace() | Sobel Edge Detection, Laplacian of Gaussian for edge finding |
| Morphological Operations | Refines segmentation by modifying the shape of regions (erosion, dilation, opening, closing). | ndimage.binary_erosion(), ndimage.binary_dilation() | Noise removal, Filling gaps, Object merging |
| Connected Component Labeling | Labels each connected component (region) in the image, commonly used for identifying distinct regions. | ndimage.label(), ndimage.find_objects() | Object counting, Region labeling for further analysis or processing |
Applications of Image Segmentation
Image segmentation using SciPy is widely applied in various domains. Below are some of the primary applications of image segmentation −
- Medical Imaging: Segmentation helps in detecting and delineating regions of interest such as tumors, organs and lesions in medical scans e.g., MRI, CT.
- Object Recognition & Detection: Image Segmentation is used to identify and classify objects or regions within an image based on characteristics such as color, texture or intensity.
- Image Preprocessing for Computer Vision: Segmentation is often used as a preprocessing step for tasks like feature extraction, object tracking or classification.
- Remote Sensing: Applied to analyze and interpret images from satellites or drones for land use classification, vegetation detection or water bodies segmentation.
- Robotics & Automation: Segmentation aids robots in understanding their environment by separating obstacles and free space for navigation.
- Surveillance & Security: Applied in surveillance cameras for monitoring, detecting people, and recognizing specific objects or regions.
- Face Recognition & Emotion Detection: Segmentation helps in identifying key facial features and regions for emotion or identity recognition.
- Art and Historical Preservation: It is used to segment and analyze old or damaged artwork for restoration purposes.