Learning based objective evaluation of image segmentation algorithms

Image segmentation is an important component in image processing, pattern recognition and computer vision. Many segmentation algorithms have been proposed. However, segmentation methods for both noisy and noise-free images have not been studied in much detail. Neutrosophic set (NS), a part of neutrosophy theory, studies the origin, nature, and scope of neutralities, as well as their interaction with different ideational spectra. However, neutrosophic set needs to be specified and clarified from a technical point of view for a given application or field to demonstrate its usefulness. In this paper, we apply neutrosophic set and define some operations. Neutrosphic set is integrated with an improved fuzzy c-means method and employed for image segmentation. A new operation, α-mean operation, is proposed to reduce the set indeterminacy. An improved fuzzy c-means (IFCM) is proposed based on neutrosophic set. The computation of membership and the convergence criterion of clustering are redefined accordingly. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can segment images accurately and effectively. Especially, it can segment the clean images and the images having different gray levels and complex objects, which is the most difficult task for image segmentation.

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Comparison of Thresholding and Edge Detection Segmentation Techniques

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.860-863.2783 ◽

2013 ◽

Vol 860-863 ◽

pp. 2783-2786

Author(s):

Yu Bing Dong ◽

Hai Yan Wang ◽

Ming Jing Li

Keyword(s):

Image Segmentation ◽

Root Mean Square Error ◽

Edge Detection ◽

Mean Square Error ◽

Root Mean Square ◽

Experimental Results ◽

Mean Square ◽

Segmentation Method ◽

Segmentation Algorithms

Edge detection and thresholding segmentation algorithms are presented and tested with variety of grayscale images in different fields. In order to analyze and evaluate the quality of image segmentation, Root Mean Square Error is used. The smaller error value is, the better image segmentation effect is. The experimental results show that a segmentation method is not suitable for all images segmentation.

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Image Segmentation Techniques in Bone Structure Psychiatry

International Journal Of Engineering And Computer Science ◽

10.18535/ijecs/v9i07.4502 ◽

2020 ◽

Vol 9 (07) ◽

pp. 25102-25112

Author(s):

Ajayi Olayinka Adedoyin ◽

Olamide Timothy Tawose ◽

Olu Sunday Adetolaju

Keyword(s):

Image Segmentation ◽

Bone Structure ◽

Performance Comparison ◽

Imaging Systems ◽

Intrinsic Properties ◽

X Ray ◽

Practical Applications ◽

X Ray Imaging ◽

Segmentation Algorithms ◽

Bone Structures

Today, a large number of x-ray images are interpreted in hospitals and computer-aided system that can perform some intelligent task and analysis is needed in order to raise the accuracy and bring down the miss rate in hospitals, particularly when it comes to diagnosis of hairline fractures and fissures in bone joints. This research considered some segmentation techniques that have been used in the processing and analysis of medical images and a system design was proposed to efficiently compare these techniques. The designed system was tested successfully on a hand X-ray image which led to the proposal of simple techniques to eliminate intrinsic properties of x-ray imaging systems such as noise. The performance and accuracy of image segmentation techniques in bone structures were compared and these eliminated time wasting on the choice of image segmentation algorithms. Although there are several practical applications of image segmentation such as content-based image retrieval, machine vision, medical imaging, object detection, recognition tasks, etc., this study focuses on the performance comparison of several image segmentation techniques for medical X-ray images.

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