Image Processing Segmentation Techniques

Segmentation Overview

• Segmentation is pivotal for distinguishing objects of interest from the background in image analysis.
• Techniques used for segmentation include thresholding and edge finding, critical for separating the foreground from background.

Key Segmentation Concepts

• Segmentation methods are application-dependent; no universal technique guarantees success across all images.
• All segmentation techniques have limitations and are not perfect.

Segmentation Algorithms

• Common algorithms for feature extraction include:
  • Thresholding
  • Histogram-based segmentation
  • Region-based segmentation
  • Edge-based segmentation
  • Template matching

Thresholding

• Global thresholding transforms gray-level images to binary images based on a single threshold value for the entire image.
• Otsu's Method maximizes between-class variance to automatically determine an optimal threshold, particularly effective for bimodal or multimodal distributions.
• Local thresholding calculates thresholds for smaller image regions, addressing issues like varying lighting conditions and preserving image details.

Adaptive Thresholding

• Mean Adaptive Thresholding defines thresholds using the average intensity of local pixels.
• Gaussian Adaptive Thresholding applies a weighted average, prioritizing pixels closer to the center of the region, enhancing segmentation accuracy.

Applications of Thresholding

• Object detection in various fields, including medical imaging.
• Quality control in manufacturing through defect detection.
• Noise reduction and edge detection to identify object boundaries.

Histogram-Based Segmentation

• Techniques depend on the histogram of the image; preprocessing steps include histogram equalization and smoothing to improve contrast and minimize noise.

Automated Histogram Methods

• Triangle Algorithm identifies optimal threshold values by maximizing distance between histogram peaks and a constructed line.
• Histogram Peak Technique finds peaks representing background and object, setting thresholds accordingly.

Region-Based Segmentation

• Pixels are grouped into regions based on homogeneity criteria, categorizing parts of the object versus the background.
• Region growing algorithms cluster nearby pixels into labeled regions based on specified characteristics.

Split and Merge Technique

• This technique segments images by recursively splitting them into subregions and merging similar adjacent regions based on homogeneity.

Edge-Based Segmentation

• Involves detecting edges in images, which can be complicated by noise.
• Edge detection methods include search-based (derivative analysis) and zero-crossing methods (second derivative analysis).

Edge Detection Goals

• Aims to identify points with significant intensity changes, preserving essential structural information while reducing data volume.
• First-order differential methods, such as detecting maxima in gradient values, help mark edges effectively.### Edge Detection Basics
• The image gradient vector is fundamental in edge detection.
• The simplest operator for calculating gradients is Robert's Cross, which uses diagonal masks.
• Prewitt operator applies a 3x3 convolution mask to detect vertical edges.
• The Sobel operator gives more weight to the center cell in its gradient calculations.

Edge Detection Algorithms

• Prewitt and Sobel Algorithms: Both use convolution masks to detect horizontal and vertical edge components.
• The outputs from both are combined to create a gradient map.
• Magnitude of the gradient is computed and subjected to non-maxima suppression to retain only local maxima.
• Thresholding eliminates small local maxima caused by noise, finalizing the edge map.

Chain Code for Contours

• Chain code tracks the direction as one moves along the contour of an object, noting movements in a clockwise manner.
• Each movement direction is represented by a code, describing the contour in a position-independent, orientation-dependent way.
• Even codes correspond to horizontal/vertical directions, while odd codes correspond to diagonal movements.

Properties of Chain Codes

• Changes in consecutive chain codes indicate a change in contour direction, marking corners.
• Perimeter is calculated using chain codes with even codes contributing a length of 1 and odd codes contributing a length of √2.
• The absolute coordinates of the contour's starting pixel and the chain code together provide a comprehensive contour description.

Alternative Contour Encoding

• Crack Codes: Encode the 'cracks' or gaps between the contour and background, using four directions instead of eight.
• Run Codes: Represent runs of consecutive pixels along a row but may suffer from errors due to image rotation or noise.

Deep Learning for Segmentation

• CNNs are employed for brain image segmentation, classifying tissues such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF).
• Normalization adjusts gray values to enhance contrast, often utilizing Local Adaptive Histogram Equalization (CLAHE) for improved results.

CNN Architecture for Image Segmentation

• CNN architecture features convolutional layers (CL), pooling layers, and a fully connected layer.
• In a 3D CNN, two convolution layers, max pooling layers, and a single output layer cooperate to analyze the images.
• Convolution layers apply filters to capture image features, while pooling layers reduce representation size and computational load.

U-Net Model

• U-Net is specifically designed for biomedical image segmentation; it assigns pixel classes semantically.
• It utilizes an encoder-decoder architecture where images are transformed into vectors and reconstructed, enhancing the model's robustness with limited datasets.
• The network structure includes four encoder blocks and four decoder blocks linked by a bridge.

Residual U-Net Elements

• A deep residual network utilizes residual blocks comprising layers such as batch normalization (BN) and ReLU activation.
• The encoder path starts with input resizing and implements batch normalization and convolution.
• Decoding involves upsampling, concatenation, and stacks of convolutional layers with ReLU activation.

Specifics of ReLU and Up Sampling Layers

• ReLU Layer: Applies a threshold operation to input elements, setting negatives to zero for non-linear transformation.
• Up Sampling Layer: Doubles the dimensions of input without weights, necessary for reconstructing image sizes post-convolution.

This quiz covers key concepts and methods in image segmentation within the field of image processing. It explores various techniques like thresholding, edge-based, and region-based segmentation. Test your knowledge on these fundamental concepts and their applications in image analysis.