Abstract

INTRODUCTION: Image segmentation in medical physics plays a vital role in image analysis to identify the affected tumour. The process of subdividing an image into its constituent parts that are homogeneous in feature is called Image segmentation, and this process concedes to extract some useful information. Numerous image segmentation techniques have been developed, and these techniques conquer different restrictions on conventional medical segmentation techniques. This paper presents a review of medical image segmentation techniques and statistical mechanics based on the novel method named as Lattice Boltzmann method (LBM). The beauty of LBM is to augment the computational speed in the process of medical image segmentation with an accuracy and specificity of more than 95% compared to traditional methods. As there is not much information on LBM in medical physics, it is intended to present a review of the research progress of LBM.OBJECTIVE: As there is no review paper on the research progress of the LB method, this paper presents a review with an objective to give some thought regarding the different segmentation for medical image and novel LB method to advance interest for future investigation and exploration in medical image segmentation.METHODS: This paper in attendance a short review of medical image segmentation techniques based on Thresholding, Region-based, Clustering, Edge detection, Model-based and the novel method Lattice Boltzmann method (LBM).CONCLUSION: In this paper, we outlined various segmentation techniques applied to medical images, emphasize that none of these problem areas has been acceptably settled, and all of the algorithms depicted are available for broad improvement. Since LBM has the benefits of speed and adaptability of modelling to guarantee excellent image processing quality with a reasonable amount of computer resources, we predict that this method will become a new research hotspot in image processing.

Highlights

  • Image segmentation in medical physics plays a vital role in image analysis to identify the affected tumour

  • This paper in attendance a short review of medical image segmentation techniques based on Thresholding, region-based, Clustering, edge detection, model-based, and the novel method Lattice Boltzmann method (LBM) to augment the computational speed, which is based on the microscopic description of the macroscopic physical process

  • As there is no review paper on the LB method's research progress, this paper presents a review to give some thought regarding the different segmentation for medical images and novel LB method to advance interest for future investigation and exploration in medical image segmentation

Read more

Summary

Introduction

Image processing techniques have become progressively significant in a wide assortment of applications with sophisticated techniques and instruments. Segmentation is significant in medical image analysis and intends to draw out some details from the images. Those images can be utilized for high-level image understanding. These review articles are driven by classifying the methods utilized for processing pixel data voxel data and their applications in diagnosis, treatment planning and follow up studies; methods that have been applied in practice, a shortcoming still exists, an obvious one is a computational speed. This paper in attendance a short review of medical image segmentation techniques based on Thresholding, region-based, Clustering, edge detection, model-based, and the novel method Lattice Boltzmann method (LBM) to augment the computational speed, which is based on the microscopic description of the macroscopic physical process. As there is no review paper on the LB method's research progress, this paper presents a review to give some thought regarding the different segmentation for medical images and novel LB method to advance interest for future investigation and exploration in medical image segmentation

Segmentation techniques
Thresholding approach
Region-based methods
Clustering approach
Edge detection
Model-based algorithms
Findings
Conclusion

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.