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Image Segmentation based on Histogram Analysis and Soft Thresholding

by T. V. Sai Krishna, A. Yesu Babu
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 78 - Number 5
Year of Publication: 2013
Authors: T. V. Sai Krishna, A. Yesu Babu
10.5120/13482-1185

T. V. Sai Krishna, A. Yesu Babu . Image Segmentation based on Histogram Analysis and Soft Thresholding. International Journal of Computer Applications. 78, 5 ( September 2013), 1-6. DOI=10.5120/13482-1185

@article{ 10.5120/13482-1185,
author = { T. V. Sai Krishna, A. Yesu Babu },
title = { Image Segmentation based on Histogram Analysis and Soft Thresholding },
journal = { International Journal of Computer Applications },
issue_date = { September 2013 },
volume = { 78 },
number = { 5 },
month = { September },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume78/number5/13482-1185/ },
doi = { 10.5120/13482-1185 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:51:16.689074+05:30
%A T. V. Sai Krishna
%A A. Yesu Babu
%T Image Segmentation based on Histogram Analysis and Soft Thresholding
%J International Journal of Computer Applications
%@ 0975-8887
%V 78
%N 5
%P 1-6
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Most researched area in the field of object oriented image processing procedure is efficient and effective image segmentation. Segmentation is a process of partitioning a digital image into multiple regions (sets of pixels), according to some homogeneity criterion. In this paper, we introduce a spatial domain segmentation framework based on the histogram analysis and soft threshold. The histogram analysis uses discontinuity and similarity properties of image statistics in tandem with distribution of pixels to define the binary label for a homogenous region. The soft threshold used for classification is determined based on the localized statistics of the image in consideration for merging of the regions. Simulation results and analysis would verify that the proposed algorithm shows good performance in image segmentation without choosing the region of interest.

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Index Terms

Computer Science
Information Sciences

Keywords

Image segmentation object recognition object extraction soft threshold medical image analysis

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