Image Segmentation

The most important task in image processing and analysis is image segmentation. In digital image processing and analysis, it is a widely used technique to divide a picture into many sections or areas, frequently according to the properties of the image's pixels. Given that it is essential for assessing and diagnosing a certain ailment, it is helpful for medical imaging. Before doing any feature segmentation, the region-of-interest (ROI) within the sample must be precisely identified.

History of Image Segmentation

Researchers in computer vision have been focusing on picture segmentation nonstop since the 1970s. Traditional segmentation techniques primarily concentrate on identifying and extracting information from a single image, which frequently calls for specialized knowledge and human involvement. Nevertheless, extracting high-level semantic information from photographs is challenging. Identification of common items from a group of images is the task of co-segmentation methods, which necessitates the acquisition of specific previous information. These techniques fall under the category of semi-supervised or weakly supervised techniques as the image annotation they use is optional. Deep neural network-based picture segmentation techniques have become more and more in demand as large-scale fine-grained annotated image datasets have been progressively enhanced.

Feature representation, model construction, and optimization are only a few of the numerous obstacles that remain in image segmentation research, although its many accomplishments. Specifically, gradient vanishing, overfitting, imbalanced classes, sparse or restricted annotations, and lengthy training times continue to pose significant obstacles for semantic segmentation. As a result, it's essential to compile an organized list of the segmentation techniques now in use, particularly the more advanced ones. From the standpoint of algorithm development, we evaluate and describe the current picture segmentation techniques, go into detail about their inner workings, and list a few significant image segmentation algorithms.

Advancements in Image Segmentation Techniques

The advancements in the image segmentation techniques are briefed here through comparing the classic segmentation techniques with other segmentation techniques.

Classic Segmentation Techniques

For grayscale images, the traditional segmentation techniques were put forth. These algorithms primarily take into account gray-level discontinuity in some places and gray-level similarity in others. Generally speaking, edge detection relies on gray-level discontinuity, whereas region division relies on gray-level similarity. The process of segmenting a color image into distinct areas or superpixels based on pixel similarity and then combining these superpixels is known as color image segmentation. Some of the classic segmentation methods are listed below:

Edge Detection
Region Division
Graph Theory
Clustering Method
Random Walks

Co-Segmentation Techniques

High-level semantic information about an image is hard to come by because traditional segmentation techniques often concentrate on extracting features from a single image. In collaborative segmentation, sometimes called co-segmentation, common foreground areas are automatically extracted from many pictures without human interaction in order to gain previous knowledge.

Few of the co-segmentation methods are listed below:

MRF-Based Co-Segmentation
Co-Segmentation Based on Random Walks
Co-Segmentation Based on Active Contours
Clustering-Based Co-Segmentation
Co-Segmentation Based on Graph Theory
Co-Segmentation Based on Thermal Diffusion
Object-Based Co-Segmentation

Semantic Segmentation Based on Deep Learning

The intricacy of picture features and object differences (such as scale and posture) have significantly increased due to the ongoing improvement of image collection technology. It is challenging to get good segmentation results from low-level features (such as color, brightness, and texture), and feature extraction techniques based on heuristic or manual rules are unable to satisfy the intricate requirements of modern image segmentation, which highlights the greater generalization ability of image segmentation techniques.

Prior to the application of deep learning to the area of image segmentation, semantic segmentation classifiers were often constructed using random forests and semantic texton forests. In the last several years, segmentation tasks have seen an increase in the application of deep learning algorithms, leading to notable improvements in both performance and segmentation impact. In the original method, a neural network is trained by dividing the image into tiny patches, after which the pixels are classified. Since fixed-size pictures are needed for the neural network's fully connected layers, this patch classification approach has been used.

The deep-learning based semantic segmentation techniques are mentioned below:

Encoder–Decoder Architecture
Skip Connections
Dilated Convolution
Multiscale Feature Extraction
Attention Mechanisms

Top Image Segmentation Projects Using MATLAB

Top Image Segmentation Projects Using MATLAB are listed in this section. The final year students can refer to these projects so that they can get ideas for completing their Image Segmentation Final Year Projects Using MATLAB.