A general framework for complex network-based image segmentation

Abstract

With the recent advances in complex networks theory, graph-based techniques for image segmentation has attracted great attention recently. In order to segment the image into meaningful connected components, this paper proposes an image segmentation general framework using complex networks based community detection algorithms. If we consider regions as communities, using community detection algorithms directly can lead to an over-segmented image. To address this problem, we start by splitting the image into small regions using an initial segmentation. The obtained regions are used for building the complex network. To produce meaningful connected components and detect homogeneous communities, some combinations of color and texture based features are employed in order to quantify the regions similarities. To sum up, the network of regions is constructed adaptively to avoid many small regions in the image, and then, community detection algorithms are applied on the resulting adaptive similarity matrix to obtain the final segmented image. Experiments are conducted on Berkeley Segmentation Dataset and four of the most influential community detection algorithms are tested. Experimental results have shown that the proposed general framework increases the segmentation performances compared to some existing methods.

Texture classification in aerial photographs using multiscale and multilayer complex networks

“…The fundamental limitation of image segmentation based on CNs due to the excessive numbers of nodes in the network is overcome by the concept of super-pixels [10]. Mourchid et al [11] proposed a framework that used a weighted region adjacency graph to represent an image as a network, where regions represented nodes in the network. This framework was based on community detection algorithms in graphs because networks are growing exponentially in size, variety and complexity.…”

A Novel Method for Lung Image Processing Using Complex Networks

“…The various densities interweave in their geometrical placements to create textures. Since texture recognition has a very high accuracy percentage when a complex network approach is used [24], [25], [26], this paper aims to implement such a technique customized for DILD.…”

Enhancing Imagistic Interstitial Lung Disease Diagnosis by Using Complex Networks

“…The remaining pixels are split into layers according to their specific HU band, obtaining one separate image for every layer respectively. The resulting images are then transformed into complex networks according to specific predefined attachment rules, in a manner similar to conversion of grayscale images into complex networks presented in [52], [53], [54]: nodes with similar HU values (within the range of 50 HU units) and closer than 4px away are considered to be linked, while all noncompliant ones are detached. In other words, any two visual points in the lung which are very close together and have a similar shade (density), are very probably part of the same type of tissue, whether it is affected or healthy.…”