Explain Image Segmentation : Techniques and Applications - GeeksforGeeks (2024)

Image segmentation is one of the key computer vision tasks, It separates objects, boundaries, or structures within the image for more meaningful analysis. Image segmentation plays an important role in extracting meaningful information from images, enabling computers to perceive and understand visual data in a manner that humans understand, view, and perceive. In this article let us discuss in detail image segmentation, types of image segmentation, how image segmentation is done, and its use cases in different domains.

What is Image Segmentation?

Image segmentation is a fundamental technique in digital image processing and computer vision. It involves partitioning a digital image into multiple segments (regions or objects) to simplify and analyze an image by separating it into meaningful components, Which makes the image processing more efficient by focusing on specific regions of interest. A typical image segmentation task goes through the following steps:

1. Groups pixels in an image based on shared characteristics like colour, intensity, or texture.
2. Assigns a label to each pixel, indicating its belonging to a specific segment or object.
3. The resulting output is a segmented image, often visualized as a mask or overlay highlighting the different segments.

Why do we need Image Segmentation?

Image segmentation is crucial in computer vision tasks because it breaks down complex images into manageable pieces. It’s like separating ingredients in a dish. By isolating objects (things) and backgrounds (stuff), image analysis becomes more efficient and accurate. This is essential for tasks like self-driving cars identifying objects or medical imaging analyzing tumours. Understanding the image’s content at this granular level unlocks a wider range of applications in computer vision.

Image segmentation vs. object detection vs. image classification

The comparison between Image segmentation, object detection and image classification are as follows:

Semantic Classes in Image Segmentation: Things and Stuff.

In semantic image segmentation, we categorize image pixels based on their semantic meaning, not just their visual properties. This classification system often uses two main categories: Things and Stuff.

• Things: Things refer, to countable objects or distinct entities in an image with clear boundaries, like people, flowers, cars, animals etc. So, the segmentation of “Things” aims to label individual pixels in the image to specific classes by delineating the boundaries of individual objects within the image
• Stuff: Stuff refers to specific regions or areas in an image different elements in an image like background or repeating patterns of similar materials which can not be counted like road, sky and grass which may not have clear boundaries but play a crucial role in understanding the overall context in an image. The segmentation of “Stuff” involves grouping of pixels in an image into clearly identifiable regions based on the common properties like colour, texture or context.

Semantic segmentation

Semantic Segmentation is one of the different types of image segmentation where a class label is assigned to image pixels using deep learning (DL) algorithm. In Semantic Segmentation, collections of pixels in an image are identified and classified by assigning a class label based on their characteristics such as colour, texture and shape. This provides a pixel-wise map of an image (segmentation map) to enable more detailed and accurate image analysis.

For example, all pixels related to a ‘tree’ would be labelled the same object name without distinguishing between individual trees. Another example would be, group of people in an image would be labelled as single object as ‘persons’, instead of identifying individual people.

Instance segmentation

Instance segmentation in image segmentation of computer vision task is a more sophisticated feature which involves identifying and delineating each individual object within an image. So instance segmentation goes beyond just identifying objects in an image, but also delineate the exact boundaries of each individual instance of that object.

So, the key focus of instance segmentation is to differentiate between separate objects of the same class. for example, if there are many cats in a image, instance segmentation would identify and outline each specific cat. The segmentation map is created for each individual pixel and separate labels are assigned to specific object instances by creating different coloured labels which will represent different ‘cat’ in the group of cats in an image.

Instance segmentation is useful in autonomous vehicles to identify individual objects like pedestrians, other vehicles and any objects along the navigation route. In medical imaging, analysing scan images for detection of specific abnormalities are useful for early detection of cancer and other organ conditions.

Panoptic segmentation

Panoptic segmentation goes a step further in image segmentation of computer vision tasks, by combining the features and processes of semantic and instance segmentation techniques. So the panoptic segmentation algorithm creates a comprehensive image analysis by simultaneously classifying every pixel and identifying distinct object instances of the same class.

So, from an image with multiple cars and pedestrians in an traffic signal, the panoptic segmentation would label all ‘pedestrians’ and ‘cars’ (semantic segmentation) and draw bounding boxes around them to identify and segment each individual persons and cars and also classifying the different surrounding scenarios like road signals, traffic lights and all other building or backgrounds. So panoptic segmentation detects and interprets everything within a given image.

Panoptic segmentation leverages the strengths of fully convolutional networks (FCN) for semantic context and Mask R-CNN for instance-specific details, which gives a combined output for achieving a more holistic and nuanced understanding of visual data.

Traditional image segmentation techniques

The traditional image segmentation techniques which formed the foundation of modern image segmentation methods using deep learning algorithms, uses thresholding, edge detection, Region-Based Segmentation, clustering algorithms and Watershed Segmentation. These techniques are more reliant on principle of image processing, mathematical operation and heuristics to separate an image into meaningful regions.

• Thresholding: This method involves selecting a threshold value and classifying image pixels between foreground and background based on intensity values
• Edge Detection: Edge detection method identify abrupt change in intensity or discontinuation in the image. It uses algorithms like Sobel, Canny or Laplacian edge detectors.
• Region-based segmentation: This method segments the image into smaller regions and iteratively merges them based on predefined attributes in colour, intensity and texture to handle noise and irregularities in the image.
• Clustering Algorithm: This method uses algorithms like K-means or Gaussian models to group object pixels in an image into clusters based on similar features like colour or texture.
• Watershed Segmentation:The watershed segmentation treats the image like a topographical map where the watershed lines are identifies based on pixel intensity and connectivity like water flowing down different valleys.

These traditional methods offer basic techniques of image segmentation with limitations, but provide foundation for more advanced methods.

Deep learning image segmentation models

Deep learning image segmentation models are a powerful technique which leverages the neural network architecture to automatically divide an image into different segments and extract features from images for accurate analysis and segmentation tasks.

Below are some of the popular deep learning models used for image segmentation:

• U-Net: This model uses U-Shaped network to efficiently segment medical images. This model is very efficient in working with small amount of data and provide precise segmentation.
• Fully Convolutional Network (FCN):This model has the ability to process image of any size and output spatial maps. This is achieved by replacing fully connected layers in a conventional CNN with convolutional layers. This helps in segmenting an entire image pixel by pixel.
• SegNet: This model includes a encoder-decoder network, used for tasks like scene understanding and object recognition. The encoder here captures the context in the image and the decoder performs the precise localization and segmentation objects by using the context.
• DeepLab: The key feature of DeepLab is the use of atrous convolutions used to capture multi-scale context with multiple parallel filters.
• Mask R-CNN: This model extents the Faster R-CNN object detection framework, by adding a branch for predicting segmentation masks alongside bounding box regression.
• Vision Transformer (ViT): A new model that applies transformers to image segmentation. The image is divided into patches and processes them sequentially to understand the global context of the image.

Applications of Image segmentation

Below are the list of different uses cases of Image Segmentation in Image processing:

• Autonomous Vehicles: Image segmentation helps autonomous vehicles in identifying and segmenting objects like real time road lane detections, vehicles, pedestrians, traffic signs for safe navigation.
• Medical Imaging Analysis: Image segmentation used for segmenting organs, tumours and other anatomical structures from medical images like X-Rays, MRIs, and CT Scans, helps in diagnosis and treatment planning.
• Satellite Image Analysis: Used in analysing satellite images for landcover classification, urban planning, and environmental changes.
• Object Detection and Tracking: Segmenting different objects in image or video for different tasks like person detection, anomaly detection, and detecting different activities in security systems.
• Content Moderation: Used in monitoring and segmenting inappropriate content from images or videos for social media platforms.
• Smart Agriculture: Image segmentation methods are used by farmers and agronomists for crop health monitoring, estimating yield and detect plant diseases from images and videos.
• Industrial Inspection: Image segmentation helps in manufacturing process for quality control, detecting defects in products.

Conclusion:

In this article about Image Segmentation in image process, we have discussed about one of the key computer vision tasks and how this process helps image processing and analysis in many different fields including medical image analytics for diagnosis and planning better treatment methods. Also this article delves into the traditional image segmentation models over how advanced deep learning models are used today in image processing and segmentation tasks.