Image Segmentation Based on Superpixels and Saliency

2014 ◽  
Vol 701-702 ◽  
pp. 312-315 ◽  
Author(s):  
Qi Chen ◽  
Xing Ben Yang ◽  
Yun Hong Chen ◽  
Dan Dan Li
Keyword(s):  
Image Segmentation ◽  
Visual Inspection ◽  
Clustering Algorithm ◽  
Segmentation Method ◽  
Practical Applications ◽  
Saliency Model ◽  
Affinity Propagation Clustering ◽  
Cut Method ◽  
Image Separation ◽  
High Level

Image segmentation plays an important role in computer vision and image processing to interpret and analyze an acquired image. Separation of objects or image regions is usually required for high-level image comprehension in practical applications involving visual inspection. In this paper, a novel automatic image segmentation method is proposed. To extract the foreground of the image automatically, we combine saliency model based on superpixels with the affinity propagation clustering algorithm in an unsupervised manner, and use graph cut method to obtain the segmentation results.

A novel image segmentation method based on fast density clustering algorithm

2018 ◽  
Vol 73 ◽  
pp. 92-110 ◽  
Author(s):  
Jinyin Chen ◽  
Haibin Zheng ◽  
Xiang Lin ◽  
Yangyang Wu ◽  
Mengmeng Su
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Segmentation Method ◽  
Density Clustering

scholarly journals A Novel Brain MRI Image Segmentation Method Using an Improved Multi-View Fuzzy c-Means Clustering Algorithm

2021 ◽  
Vol 15 ◽  
Author(s):  
Lei Hua ◽  
Yi Gu ◽  
Xiaoqing Gu ◽  
Jing Xue ◽  
Tongguang Ni
Keyword(s):  
Image Segmentation ◽  
Brain Tissue ◽  
Adaptive Learning ◽  
Clustering Algorithm ◽  
Brain Mri ◽  
Segmentation Method ◽  
Learning Mechanism ◽  
Fcm Algorithm ◽  
The Brain ◽  
Mri Image

Background: The brain magnetic resonance imaging (MRI) image segmentation method mainly refers to the division of brain tissue, which can be divided into tissue parts such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The segmentation results can provide a basis for medical image registration, 3D reconstruction, and visualization. Generally, MRI images have defects such as partial volume effects, uneven grayscale, and noise. Therefore, in practical applications, the segmentation of brain MRI images has difficulty obtaining high accuracy.Materials and Methods: The fuzzy clustering algorithm establishes the expression of the uncertainty of the sample category and can describe the ambiguity brought by the partial volume effect to the brain MRI image, so it is very suitable for brain MRI image segmentation (B-MRI-IS). The classic fuzzy c-means (FCM) algorithm is extremely sensitive to noise and offset fields. If the algorithm is used directly to segment the brain MRI image, the ideal segmentation result cannot be obtained. Accordingly, considering the defects of MRI medical images, this study uses an improved multiview FCM clustering algorithm (IMV-FCM) to improve the algorithm’s segmentation accuracy of brain images. IMV-FCM uses a view weight adaptive learning mechanism so that each view obtains the optimal weight according to its cluster contribution. The final division result is obtained through the view ensemble method. Under the view weight adaptive learning mechanism, the coordination between various views is more flexible, and each view can be adaptively learned to achieve better clustering effects.Results: The segmentation results of a large number of brain MRI images show that IMV-FCM has better segmentation performance and can accurately segment brain tissue. Compared with several related clustering algorithms, the IMV-FCM algorithm has better adaptability and better clustering performance.

Improvement of the Image Sub-Segmentation for Identification and Differentiation of Atypical Regions

2017 ◽  
Vol 32 (01) ◽  
pp. 1860011 ◽  
Author(s):  
B. Ojeda-Magaña ◽  
J. Quintanilla-Domínguez ◽  
R. Ruelas ◽  
L. Gómez Barba ◽  
D. Andina
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Digital Images ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Representative Element ◽  
First Case ◽  
Segmented Object ◽  
Segmentation Problem ◽  
Black Color

A new sub-segmentation method has been proposed in 2009 which, in digital images, help us to identify the typical pixels, as well as the less representative pixels or atypical of each segmented region. This method is based on the Possibilistic Fuzzy c-Means (PFCM) clustering algorithm, as it integrates absolute and relative memberships. Now, the segmentation problem is related to isolate each one of the objects present in an image. However, and considering only one segmented object or region represented by gray levels as its only feature, the totality of pixels is divided in two basic groups, the group of pixels representing the object, and the others that do not represent it. In the former group, there is a sub-group of pixels near the most representative element of the object, the prototype, and identified here as the typical pixels, and a sub-group corresponding to the less representative pixels of the object, which are the atypical pixels, and generally located at the borders of the pixels representing the object. Besides, the sub-group of atypical pixels presents greater tones (brighter or towards the white color) or smaller tones (darker or towards black color). So, the sub-segmentation method offers the capability to identify the sub-region of atypical pixels, although without performing a differentiation between the brighter and the darker ones. Hence, the proposal of this work contributes to the problem of image segmentation with the improvement on the detection of the atypical sub-regions, and clearly recognizing between both kind of atypical pixels, because in many cases only the brighter or the darker atypical pixels are the ones that represent the object of interest in an image, depending on the problem to be solved. In this study, two real cases are used to show the contribution of this proposal; the first case serves to demonstrate the pores detection in soil images represented by the darker atypical pixels, and the second one to demonstrate the detection of microcalcifications in mammograms, represented in this case by the brighter atypical pixels.

Sign in / Sign up

Export Citation Format

Share Document