Image segmentation algorithm based on superpixel clustering

Lin, Cong; Ding, Shifei; Wang, Lijuan; Zhang, Aijuan; Jia, Weikuan

doi:10.1049/iet-ipr.2018.5439

Cited by 36 publications

(36 citation statements)

References 16 publications

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“…In future, this work is planned to be extended, such that it can deal with 3D images. Fast FCM based [10] Similarity matrix based [14] Spectral clustering based [22] Proposed work…”

Section: Discussionmentioning

confidence: 99%

“…An image segmentation algorithm based on superpixel clustering is presented in [14]. This work employs the superpixel pre-processing technique to split the image into several superpixel regions.…”

Section: Review Of Literaturementioning

confidence: 99%

“…This section compares the work performance of the proposed clustering algorithm with the existing approaches such as binary edge map [8], fast FCM based [10], similarity matrix based [14] and spectral clustering based [22]. The experimental results attained by the proposed clustering algorithm in terms of accuracy, precision, recall, F-measure and time consumption are presented in the following figures 4 and 5.…”

Section: Comparative Analysis With Existing Approachesmentioning

confidence: 99%

“…Binary edge map [8] Fast FCM based [10] Similarity matrix based [14] Spectral clustering based [22] Fig .6. Time consumption analysis The proposed work shows better results at the cost of reasonable time consumption, which better than some existing techniques.…”

Section: Performance Measuresmentioning

confidence: 99%

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Automated Digital Image Clustering Algorithm Based on Colour Distance and IDBSCAN

Sangeetha*,

Velmani,

Rajakumar

2019

IJITEE

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Data clustering is inevitable for crucial data analytic based applications. Though data clustering algorithms are capacious in the literature, there is always a room for efficient data clustering algorithms. This is due to the uncontrollable growth of data and its utilization. The data clustering may consider any of the data formats such as text, images, audio, video and so on. Due to the increasing utilization trend of digital images, this work intends to present a data clustering algorithm for digital images, which is based colour distance and Improvised DBSCAN (IDBSCAN) algorithm. The proposed IDBSCAN completely weeds out the annoying process of setting the initial parameters such as 𝜺 and 𝒎𝒊𝒏𝒑𝒕𝒔 by setting them automatically. The performance of the proposed work is analysed in terms of clustering accuracy, precision, recall, Fmeasure and time consumption rates. The proposed work outperforms the existing approaches with reasonable time consumption.

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“…In future, this work is planned to be extended, such that it can deal with 3D images. Fast FCM based [10] Similarity matrix based [14] Spectral clustering based [22] Proposed work…”

Section: Discussionmentioning

confidence: 99%

Section: Review Of Literaturementioning

confidence: 99%

Section: Comparative Analysis With Existing Approachesmentioning

confidence: 99%

Section: Performance Measuresmentioning

confidence: 99%

See 2 more Smart Citations

Automated Digital Image Clustering Algorithm Based on Colour Distance and IDBSCAN

Sangeetha*,

Velmani,

Rajakumar

2019

IJITEE

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“…Further studies are necessary in the area of image processing for clarifying the results of a chest x-ray and to look into the abnormalities that are not visible. Image segmentation is considered the first and the most important step in image processing which assists doctors to diagnose and make decisions [2]. There exists a variety of techniques for lung segmentation of which thresholding is the simplest one.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Lungs in Chest Radiographs using Optimized Clustering Technique

V J*,

Krishnakumar

2020

IJRTE

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Lung Cancer is considered the most common type of disease, when compared to that of the other types. It is one of the leading types of cancer that causes majority of the patient death worldwide. It is often detected only at the later stage as they are usually diagnosed at their advanced stages. Survival of patients with lung cancer is almost impossible. They often die within one year after the onset of clinical symptoms. Screening and early detection play an important role in saving the life of a patient. Chest radiography and computerized tomography scans are the techniques mostly used to diagnose and detect tumor in lungs. They require less radiation dose, and is available in most of the diagnostic centers. Their cost is also less when compared to the other techniques used for diagnosis. Nodule detection by using conventional radiographs is still not much effective, so there arises a need for alternative image processing techniques to improve the efficiency of detection. Image segmentation is considered as the first step in processing an image. Further analysis of the image would be made more effective if segmentation is efficient. There exits many segmentation algorithms based on clustering and thresholding approaches. In this paper, a bimodal, optimized and modified k-means algorithm is developed to segment the chest images.

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Link-Based Cluster Ensemble Method for Improved Meta-clustering Algorithm

Shao

Ding

2020

IFIP Advances in Information and Communication Technology

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Ensemble clustering has become a hot research field in intelligent information processing and machine learning. Although significant progress has been made in recent years, there are still two challenging issues in the current ensemble clustering research. First of all, most ensemble clustering algorithms tend to explore similarity at the level of object but lack the ability to explore information at the level of cluster. Secondly, many ensemble clustering algorithms only focus on the direct relationship, while ignoring the indirect relationship between clusters. In order to solve these two problems, a link-based meta-clustering algorithm (L-MCLA) have been proposed in this paper. A series of experiment results demonstrate that the proposed algorithm not only produces better clustering effect but is also less influenced by different ensemble sizes.

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Image segmentation algorithm based on superpixel clustering

Cited by 36 publications

References 16 publications

Automated Digital Image Clustering Algorithm Based on Colour Distance and IDBSCAN

Automated Digital Image Clustering Algorithm Based on Colour Distance and IDBSCAN

Segmentation of Lungs in Chest Radiographs using Optimized Clustering Technique

Link-Based Cluster Ensemble Method for Improved Meta-clustering Algorithm

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