Special Issue(24-21):Image Processing Techniques for Improved Medical Image Segmentation | International Journal of Automation and Smart Technology

Image Processing Techniques for Improved Medical Image Segmentation

The process of extracting the desired object (organ) from a medical image (2D or 3D) can be done manually, semi-automatically, or totally automatically. Medical image segmentation is fundamentally the same as natural image segmentation. Medical image processing starts with obtaining the raw data from CT or MRI scans and reconstructing them into a format that can be utilised by the appropriate software. An image processing usual input is a 3D bitmap or grayscale intensities with a grid of voxels (3D pixels). Picture segmentation is the process of breaking down a picture into a group of pixel-rich sections that can be represented by a labelled image or a mask. Image segmentation is the process of breaking down a picture into a group of pixel-rich sections that can be represented by a labelled image or a mask. By separating a picture into segments, one can process only the critical segments of the image instead of processing the complete image. Images with distinct boundaries between objects and backdrop can benefit from this technique.

Medical image processing commonly uses threshold-based segmentation to detect anomalies like cancer. It is also used in traffic analysis for vehicle detection and tracking. There are four main segments in the worldwide radiology market: services, type, end-user, and region. The global market is divided into segments based on services: computed tomography, positron emission tomography (PET), ultrasound, nuclear medicine, X-rays, fusion imaging, and magnetic resonance imaging (MRI). To identify the ideal target population, markets can be segmented in a variety of ways. Market segmentation can be done in five different ways: psychographic, behavioural, geographic, and firmographic. Watersheds, region expanding, k-means clustering, thresholding, and histogram-based bundling are examples of previous techniques. More sophisticated algorithms, however, rely on sparsity-based techniques, conditional and Markov random fields, graph cuts, and active contours. Semantic and instance segmentation are the two main categories of image segmentation. While related items in instance segmentation receive separate labels, all objects of the same kind are recognized using a single class label in semantic segmentation. Image segmentation has several benefits, such as multiple copy systems, faster processing, and ease of data storage. The necessity for great precision and findings that are prone to error are drawbacks. In the realm of computer vision, segmenting an image into meaningful and coherent parts is a key problem.

Numerous applications, including object recognition, image manipulation, and scene interpretation, depend on this vital activity. Regions of interest (ROIs) are extracted from 3D image data, such as those from CT or MRI scans, as part of the process of medical image segmentation. Every imaging technology has a different use. For instance, radiography is frequently used to take pictures of bone structures in order to check for fractures. The brain and other interior tissues are frequently imaged with magnetic resonance imaging (MRI) scanners, especially when high-resolution images are required. Faster, more sophisticated processing of pictures is made possible by image segmentation, which divides an image's complex visual data into precisely formed segments. Articles are invited that explore Image Processing Techniques for Improved Medical Image Segmentation. Case studies and practitioner perspectives are also welcome.

Potential topics include but are not limited to the following:

The fundamental concepts and overview of segmenting medical images.
An overview of methods for segmenting medical images.
Techniques, algorithms, and applications for segmenting medical images.
Deep learning-based medical image categorization.
A modified Otsu method-based medical picture segmentation algorithm.
Segmentation of medical images using enhanced watershed method and k-means clustering.
A revolutionary method for enhancing medical image segmentation.
better FCM for medical picture segmentation.
An overview of segmenting medical images interactively.
Current medical image segmentation investigation.
An overview of techniques for segmenting medical images using deep learning.
Enhanced watershed transform with previous knowledge for medical picture segmentation.
A multi-dimensional statistical feature-based approach to segment medical images.
A manual for processing and analysing medical images.