A Survey: Image Segmentation Techniques

Abstract

Abstract-Segmentation is considered as one of the main steps in image processing. It divides a digital image into multiple regions in order to analyze them. It is also used to distinguish different objects in the image. Several image segmentation techniques have been developed by the researchers in order to make images smooth and easy to evaluate. This paper presents a literature review of basic image segmentation techniques from last five years. Recent research in each of image segmentation technique is presented in this paper.

Modified Discrete Grey Wolf Optimizer Algorithm for Multilevel Image Thresholding

“…These methods optimized the threshold through optimization algorithm and obtained better results on image segmentation [4]. Moreover, Akay [27] compared ABC with PSO by employing between-class variance and Kapur's entropy as objective functions.…”

“…These areas or targets are the keys for image analysis and understanding. With the in-depth research of image segmentation technology, image segmentation techniques have been widely applied in various fields, such as medical analysis [2], image classification [3], object recognition [4], image copy detection [5], and motion estimation [6]. …”

“…The commonly used optimization functions include maximization of the entropy [11], maximization of the between-class variance (e.g., Otsu's method) [12], the use of the fuzzy similarity measure [13], and minimization of the Bayesian error [14]. Among them, Kapur's optimal entropy threshold method does not require prior knowledge, which can obtain desirable segmentation result for the nonideal bimodal histogram of images which make it the most widely used method [4]. All of these techniques were originally used for bilevel thresholding and then extended to multilevel thresholding areas.…”

A Novel Medical Image Segmentation and Classification Using Combined Feature Set and Decision Tree Classifier

“…Edge based segmentation are used to detect the sharp discontinuties in an image. Further such a edge based segmentation can also be extended using various color models [12][13][14][15][16]. This paper is organized in five sections.…”

Bi level kapurs entropy based image segmentation using particle swarm optimization

“…Several masking techniques are used for detecting edges.Region based segmentation can be categirised into two main categories: region growing and region spletting and mearging.In graph based segmentation technique a digital image is modelled as a graph and efficient graph partitioning algorithm is used for paritioning the graph.Again Image segmentation problem can be thought as a clustering problem where similar pixels can be grouped in the same cluster.A complete survey on different clustering algorithms can be found in [11]. A survey on different image segmentation technique can be found in [12]. Recently optimization based techniques are popular for image segmentation.…”