Automatic segmentation of wrist bone fracture area by  K-means pixel clustering from X-ray image

Kim, Kwang-Baek; Song, Doo Heon; Yun, Sangseok

doi:10.11591/ijece.v9i6.pp5205-5210

Cited by 8 publications

(6 citation statements)

References 16 publications

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“…Total 50 images for defect detection testing of ship building ceramic materials with various thicknesses -8mm, 10mm, 11mm, 16mm, and 22mm. In order to compare the performance of proposed PCM clustering, we implement Kmeans clustering as used in [17]. The result is summarized in Table 1 and PCM proposed in this paper is far better than standard K-means clustering.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Pixel clustering methods are designed to enhance the brightness contrast and form an object from input image. Automatic detection of an object from low contrast images by pixel clustering have been successful in medical domain with K-means [16][17][18] and Fuzzy C-means algorithm [19][20][21] as well as ART2 clustering in ceramic defect detection [2]. While being effective in most cases, such strategies need careful preprocessing for noise removal and image contrast enhancement.…”

Section: Introductionmentioning

confidence: 99%

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Automatic segmentation of ceramic materials with relaxed possibilistic C-Means clustering for defect detection

Kim

Song

Park

2020

IJEECS

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Auromatic inspection system is necessary for reliable quality control if ceramic materials to avoid operator subjectivity and fatigue in visual inspection. Automatic segmentation from material’s image is then the most important process to develop such an inspection system. In this paper, we propose a Possibilistic C-Means pixel clustering algorithm with fuzzy stretching to form the defect object in segmentation. In experiment using 50 images containing a certain amount of defects, the proposed method was successful in 49 cases or 98% of opportunities. That performance is roughly twice better than that of standard K-means clustering in defect object formationAuromatic inspection system is necessary for reliable quality control if ceramic materials to avoid operator subjectivity and fatigue in visual inspection. Automatic segmentation from material’s image is then the most important process to develop such an inspection system. In this paper, we propose a Possibilistic C-Means pixel clustering algorithm with fuzzy stretching to form the defect object in segmentation. In experiment using 50 images containing a certain amount of defects, the proposed method was successful in 49 cases or 98% of opportunities. That performance is roughly twice better than that of standard K-means clustering in defect object formation.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of ceramic materials with relaxed possibilistic C-Means clustering for defect detection

Kim

Song

Park

2020

IJEECS

Self Cite

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“…Image segmentation  ISSN: 2502-4752 is the process of dividing an image into parts or objects composing it in the image, i.e. the group of pixels or pixels in a similar area depending on some homogeneity criteria such as color, density, or texture, to define the border in an image [12][13][14]. Image clustering is one of the best methods that can be used for segmentation of images.…”

Section: Introductionmentioning

confidence: 99%

Stamps extraction using local adaptive k- means and ISODATA algorithms

Rajab

George

2021

IJEECS

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One of the main difficulties facing the certified documents documentary archiving system is checking the stamps system, but, that stamps may be contains complex background and surrounded by unwanted data. Therefore, the main objective of this paper is to isolate background and to remove noise that may be surrounded stamp. Our proposed method comprises of four phases, firstly, we apply k-means algorithm for clustering stamp image into a number of clusters and merged them using ISODATA algorithm. Secondly, we compute mean and standard deviation for each remaining cluster to isolate background cluster from stamp cluster. Thirdly, a region growing algorithm is applied to segment the image and then choosing the connected region to produce a binary mask for the stamp area. Finally, the binary mask is combined with the original image to extract the stamp regions. The results indicate that the number of clusters can be determined dynamically and the largest cluster that has minimum standard deviation (i.e., always the largest cluster is the background cluster). Also, show that the binary mask can be established from more than one segment to cover are all stamp’s disconnected pieces and it can be useful to remove the noise appear with stamp region.

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“…PCM, first proposed by Krishnapuram and Keller [25], is designed to overcome that noise sensitivity of FCM by relaxing constraint in clustering process. PCM has been applied to numerous medical imaging problems and obtained decent results [26][27][28][29]. However, usually the color Doppler ultrasound images give low contrast in intensity among hyper and hypotension areas and region where blood flow regurgitates.…”

Section: Introductionmentioning

confidence: 99%

Developing an automatic brachial artery segmentation and bloodstream analysis tool using possibilistic C-means clustering from color doppler ultrasound images

Park

Song

Kim

2021

IJECE

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Automatic segmentation of brachial artery and blood-flow dynamics are important for early detection of cardiovascular disease and other vascular endothelial malfunctions. In this paper, we propose a software that is noise tolerant and fully automatic in segmentation of brachial artery from color Doppler ultrasound images. Possibilistic C-Means clustering algorithm is applied to make the automatic segmentation. We use HSV color model to enhance the contrast of bloodstream area in the input image. Our software also provides index of hemoglobin distribution with respect to the blood flow velocity for pathologists to proceed further analysis. In experiment, the proposed method successfully extracts the target area in 59 out of 60 cases (98.3%) with field expert’s verification.

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Automatic segmentation of wrist bone fracture area by K-means pixel clustering from X-ray image

Cited by 8 publications

References 16 publications

Automatic segmentation of ceramic materials with relaxed possibilistic C-Means clustering for defect detection

Automatic segmentation of ceramic materials with relaxed possibilistic C-Means clustering for defect detection

Stamps extraction using local adaptive k- means and ISODATA algorithms

Developing an automatic brachial artery segmentation and bloodstream analysis tool using possibilistic C-means clustering from color doppler ultrasound images

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