A Review of Medical Image Segmentation Algorithms

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.