A color image segmentation approach for content-based image retrieval

The color image segmentation is a fundamental requirement for microscopic biopsy image analysis and disease detection. In this paper, a hybrid combination of color k-means and marker control watershed based segmentation approach is proposed to be applied for the segmentation of cell and nuclei of microscopic biopsy images. The proposed approach is tested on breast cancer microscopic data set with ROI segmented ground truth images. Finally, the results obtained from proposed framework are compared with the results of popular segmentation algorithms such as Fuzzy c-means, color k-means, texture based segmentation as well as adaptive thresholding approaches. The experimental analysis shows that the proposed approach is providing better results in terms of accuracy, sensitivity, specificity, FPR (false positive rate), global consistency error (GCE), probability random index (PRI), and variance of information (VOI) as compared to other segmentation approaches.

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A Novel Segmentation Method of Roads and Slopes for Collapse Recognition

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.663.137 ◽

2013 ◽

Vol 663 ◽

pp. 137-143

Author(s):

Zong Ling Yan ◽

Yuan Yuan Jia ◽

Zhong Shi He

Keyword(s):

Image Segmentation ◽

Traffic Safety ◽

Color Image ◽

Recognition System ◽

Color Image Segmentation ◽

Experimental Result ◽

Region Merging ◽

Color Features ◽

Geometrical Features ◽

Segmentation Approach

Color image processing is seldom used in the recognition of roads and slopes collapse. And the application can bring great advantages to the traffic safety. Color image segmentation is the first and key step of the recognition system. By analyzing existing methods of color image segmentation, several drawbacks have been discovered. This paper proposed a novel and efficient segmentation approach which is suitable for the recognition of collapse. The Region of Interests (ROIs), i.e. the roads and slopes, was obtained with the ingenious use of the images characters. According to combine K-means clustering with region merging, connected-component algorithm and close operation, the roads and slopes are segmented with the statistical color features, geometrical features and the location of the objects. Experimental result shows feasibility and efficiency of the proposed approach.

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K-Hyperline Clustering-Based Color Image Segmentation Robust to Illumination Changes

Symmetry ◽

10.3390/sym10110610 ◽

2018 ◽

Vol 10 (11) ◽

pp. 610 ◽

Cited By ~ 3

Author(s):

Senquan Yang ◽

Pu Li ◽

HaoXiang Wen ◽

Yuan Xie ◽

Zhaoshui He

Keyword(s):

Image Processing ◽

Image Segmentation ◽

Clustering Algorithm ◽

Color Image ◽

Clustering Algorithms ◽

Color Image Segmentation ◽

Color Classification ◽

Clustering Problem ◽

Illumination Changes ◽

Segmentation Approach

Color image segmentation is very important in the field of image processing as it is commonly used for image semantic recognition, image searching, video surveillance or other applications. Although clustering algorithms have been successfully applied for image segmentation, conventional clustering algorithms such as K-means clustering algorithms are not sufficiently robust to illumination changes, which is common in real-world environments. Motivated by the observation that the RGB value distributions of the same color under different illuminations are located in an identical hyperline, we formulate color classification as a hyperline clustering problem. We then propose a K-hyperline clustering algorithm-based color image segmentation approach. Experiments on both synthetic and real images demonstrate the outstanding performance and robustness of the proposed algorithm as compared to existing clustering algorithms.

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A Fast Color Image Segmentation Approach Using GDF with Improved Region-Level Ncut

Mathematical Problems in Engineering ◽

10.1155/2018/8508294 ◽

2018 ◽

Vol 2018 ◽

pp. 1-14 ◽

Cited By ~ 2

Author(s):

Ying Li ◽

Shuliang Wang ◽

Caoyuan Li ◽

Zhenkuan Pan ◽

Weizhong Zhang

Keyword(s):

Image Segmentation ◽

Time Complexity ◽

Color Image ◽

Feature Space ◽

Color Images ◽

Color Image Segmentation ◽

Novel Approach ◽

Normalized Cuts ◽

Segmentation Approach ◽

Image Pixels

Color image segmentation is fundamental in image processing and computer vision. A novel approach, GDF-Ncut, is proposed to segment color images by integrating generalized data field (GDF) and improved normalized cuts (Ncut). To start with, the hierarchy-grid structure is constructed in the color feature space of an image in an attempt to reduce the time complexity but preserve the quality of image segmentation. Then a fast hierarchy-grid clustering is performed under GDF potential estimation and therefore image pixels are merged into disjoint oversegmented but meaningful initial regions. Finally, these regions are presented as a weighted undirected graph, upon which Ncut algorithm merges homogenous initial regions to achieve final image segmentation. The use of the fast clustering improves the effectiveness of Ncut because regions-based graph is constructed instead of pixel-based graph. Meanwhile, during the processes of Ncut matrix computation, oversegmented regions are grouped into homogeneous parts for greatly ameliorating the intermediate problems from GDF and accordingly decreasing the sensitivity to noise. Experimental results on a variety of color images demonstrate that the proposed method significantly reduces the time complexity while partitioning image into meaningful and physically connected regions. The method is potentially beneficial to serve object extraction and pattern recognition.

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