Deep Learning Based Hybrid Clustering Technique Using Brain Tumor Segmentation

Authors

  • V. Vinay Kumar, S.Sai Krishna

Abstract

Image segmentation refers to the way toward apportioning a image into totally unrelated
districts. It tends to be considered as the most fundamental and essential cycle for encouraging the
delineation, characterization, and visualization of area of importance in any medical image.
Regardless of escalated research, segmentation stays a difficult issue because of the assorted image
content, cluttered objects, occlusion, non-uniform object surface, and different variables. There are
numerous calculations and methods accessible for image segmentation yet at the same time their
necessities to build up a proficient, quick strategy of clinical image segmentation.
This paper presents an effective image segmentation approach utilizing K-means grouping
procedure incorporated with morphological operations. It is trailed by thresholding and level set
segmentation stages to give an accurate brain tumor detection. The proposed procedure can get
advantages of the K-means clustering for image segmentation in the parts of insignificant calculation
time. What's more, it can get points of interest of morphological operations are the parts of
exactness. The experimental results clarify the effectiveness of our projected approach to apply with
a high level of segmentation problems by means of improving the division quality and exactness in
minimal execution time.

Published

2020-11-01

Issue

Section

Articles