Superpixel-based segmentation of muscle fibers in multi-channel microscopy
0
Citation
0
Reference
20
Related Paper
Keywords:
Market Segmentation
Tracing
Cite
Superpixel, becomes popular for use in computer vision applications, is an image patch that adheres well to image boundaries. It has better alignment with intensity edges than rectangular patch and also capable of carrying more information. Intuitively, superpixels segment similar pixels to create visually meaningful entities while completely reducing the number of primitives for consequent image processing tasks. The main objective of superpixel segmentation, also known as image over-segmentation, is to create a consistent pixels grouping. This paper aims to present an insight of existing cluster based superpixel segmentation algorithms. Additionally, the parametric comparison of these algorithms is highlighted to establish possible research direction.
Segmentation-based object categorization
Cite
Citations (3)
This paper presents a bi-level multi-objective evolutionary image segmentation approach based on texture-based color features. There are many approaches for image segmentation. The majority of these approaches utilize either texture or color features of images. However, in most cases, only the texture or the color features are not sufficient for superior segmentation. Our proposed approach addresses this issue and employs a bi-level segmentation approach integrating both types of features. This approach uses color histogram-based texture feature in the first level of segmentation. In the second level, a multi-objective evolutionary algorithm is applied to the generated segmented images to produce the final set of non-dominated segmented results by optimizing three objectives simultaneously. The proposed approach is able to partition test images in a number of segments consistent with human visual perception. In term of quantitative evaluation, our approach also provides better results than existing approaches as shown in experimental results.
Segmentation-based object categorization
Color histogram
Region growing
Feature (linguistics)
Cite
Citations (4)
Robustness
Feature (linguistics)
Segmentation-based object categorization
Cite
Citations (5)
This paper proposes an interactive image-segmentation method which is based on superpixel. To achieve fast segmentation, the method is used to establish a Graphcut model using superpixels as nodes, and a new energy function is proposed. Experimental results demonstrate that the authors’ method has excellent performance in terms of segmentation accuracy and computation efficiency compared with other segmentation algorithm based on pixels.
Segmentation-based object categorization
Cite
Citations (0)
Non-uniform gray distribution and blurred edges often result in bias during the superpixel segmentation of medical images of magnetic resonance imaging (MRI). To this end, we propose a novel superpixel segmentation algorithm by integrating texture features and improved simple linear iterative clustering (SLIC). First, a 3D histogram reconstruction model is used to reconstruct the input image, which is further enhanced by gamma transformation. Next, the local tri-directional pattern descriptor is used to extract texture features of the image; this is followed by an improved SLIC superpixel segmentation. Finally, a novel clustering-center updating rule is proposed, using pixels with gray difference with original clustering centers smaller than a predefined threshold. The experiments on the Whole Brain Atlas (WBA) image database showed that, compared to existing state-of-the-art methods, our superpixel segmentation algorithm generated significantly more uniform superpixels, and demonstrated the performance accuracy of the superpixel segmentation in both fuzzy boundaries and fuzzy regions.
Cite
Citations (16)
In this letter, we propose a hierarchical segmentation (HS) method for color images, which can not only maintain the segmentation accuracy, but also ensure a good speed. In our method, HS adopts the fuzzy simple linear iterative clustering (Fuzzy SLIC) to obtain an over-segmentation result. Then, HS uses the fast fuzzy C-means clustering (FFCM) to produce the rough segmentation result based on superpixels. Finally, HS takes the non-iterative K-means clustering using priority queue (KPQ) to refine the segmentation result. In the validation experiments, we tested our method and compared it with state-of-the-art image segmentation methods on the Berkeley (BSD500) benchmark under different types of noise. The experiment results show that our method outperforms state-of-the-art techniques in terms of accuracy, speed and robustness.
Robustness
Segmentation-based object categorization
Benchmark (surveying)
Region growing
Cite
Citations (7)
Confetti fluorescence and other multi-color genetic labelling strategies are useful for observing stem cell regeneration and for other problems of cell lineage tracing. One difficulty of such strategies is segmenting the cell boundaries, which is a very different problem from segmenting color images from the real world. This paper addresses the difficulties and presents a superpixel-based framework for segmentation of regenerated muscle fibers in mice.We propose to integrate an edge detector into a superpixel algorithm and customize the method for multi-channel images. The enhanced superpixel method outperforms the original and another advanced superpixel algorithm in terms of both boundary recall and under-segmentation error. Our framework was applied to cross-section and lateral section images of regenerated muscle fibers from confetti-fluorescent mice. Compared with "ground-truth" segmentations, our framework yielded median Dice similarity coefficients of 0.92 and higher.Our segmentation framework is flexible and provides very good segmentations of multi-color muscle fibers. We anticipate our methods will be useful for segmenting a variety of tissues in confetti fluorecent mice and in mice with similar multi-color labels.
Tracing
Market Segmentation
Cite
Citations (23)
Hue
HSL and HSV
Cite
Citations (1)
Superpixel segmentation targets at grouping pixels in an image into atomic regions whose boundaries align well with the natural object boundaries. This paper first proposes a new feature representation for superpixel segmentation that holistically embraces color, contour, texture, and spatial features. Then, we introduce a clustering-based discriminability measure to iteratively evaluate the importance of different features. Integrating the feature representation and the discriminability measure, we propose a novel content-adaptive superpixel (CAS) segmentation algorithm. CAS is able to automatically and iteratively adjust the weights of different features to fit various properties of image instances. Experiments on several challenging datasets demonstrate that the proposed CAS outperforms the state-of-the-art methods and has a low computational cost.
Feature (linguistics)
Representation
Segmentation-based object categorization
Cite
Citations (61)
Developing segmentation techniques for overlapping cells has become a major hurdle for automated analysis of cervical cells. In this paper, an automated three-stage segmentation approach to segment the nucleus and cytoplasm of each overlapping cell is described. First, superpixel clustering is conducted to segment the image into small coherent clusters that are used to generate a refined superpixel map. The refined superpixel map is passed to an adaptive thresholding step to initially segment the image into cellular clumps and background. Second, a linear classifier with superpixel-based features is designed to finalize the separation between nuclei and cytoplasm. Finally, edge and region based cell segmentation are performed based on edge enhancement process, gradient thresholding, morphological operations, and region properties evaluation on all detected nuclei and cytoplasm pairs. The proposed framework has been evaluated using the ISBI 2014 challenge dataset. The dataset consists of 45 synthetic cell images, yielding 270 cells in total. Compared with the state-of-the-art approaches, our approach provides more accurate nuclei boundaries, as well as successfully segments most of overlapping cells.
Hierarchical clustering
Cite
Citations (28)