Mega voltage X-ray image segmentation and ambient noise removal

Iftekharuddin, Khan M.; Prajna, M R; Samanth, S.; Indhukuri, M.

doi:10.1109/iembs.2002.1106302

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…That is just one of many successful applications [13][14][15][16][24][25][26][27][28][29][30][31] for the PCNN in image segmentation. For example, [15] uses the PCNN to remove object's shadow in an image.…”

Section: Image Segmentationmentioning

confidence: 99%

Review of pulse-coupled neural networks

Wang

Cheng

et al. 2010

Image and Vision Computing

230

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Section: Image Segmentationmentioning

confidence: 99%

Review of pulse-coupled neural networks

Wang

Cheng

et al. 2010

Image and Vision Computing

230

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Section: Introductionmentioning

confidence: 99%

Image Segmentation by Using Grayscale Iteration Threshold Pulse Couple Neural Network

Zhou

Chen

et al. 2010

2010 2nd International Conference on Information Engineering and Computer Science

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A novel method, called grayscale iteration threshold pulse coupled neural network (GIT-PCNN) was proposed for image segmentation, which integrates grayscale iteration threshold with PCNN. PCNN has been widely used in image segmentation. However, satisfactory results are usually obtained at the expense of time-consuming selection of PCNN parameters and the number of iteration. In this method, traditional PCNN is simplified so that there is only one parameter to be determined. Furthermore, the PCNN threshold is determined iteratively by the grayscale of the original image so that the image is segmented through one time of firing process and no iteration or specific rule is needed as the iteration stop condition. The method demonstrates better performance and faster compared to those PCNN based segmentation algorithms which require the number of iterations and image entropy as iteration stop condition. Experimental results show the effectiveness of the proposed method on segmentation results and speed performance.

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“…Pulse-coupled Neural Network (PCNN)based on the phenomena of synchronous pulse firing in the visual cortex of cat, has been widely applied for image segmentation because of its biological vision advantages [2][3][4][5][6][7][8][9][10][11][12]. The existing segmentation algorithms based on PCNN exhibits some disadvantages: (1) some algorithms requires multiple PCNN parameters and a satisfactory result strongly depends on the parameters and there is so far no mathematical theory to explain the relations of segmentation results and parameters selections.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Grayscale Image Segmentation by Using GIT-PCANN

Li¹,

Guo

Zhang

et al. 2011

IJITCS

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PCNN has been widely used in image segmentation. However, satisfactory results are usually obtained at the expense of time-consuming selection of PCNN parameters and the number of iteration. A novel method, called grayscale iteration threshold pulse coupled neural network (GIT-PCNN) was proposed for image segmentation, which integrates grayscale iteration threshold with PCNN. In this method, traditional PCNN is simplified so that there is only one parameter to be determined. Furthermore, the PCNN threshold is determined iteratively by the grayscale of the original image so that the image is segmented through one time of firing process and no iteration or specific rule is needed as the iteration stop condition. The method demonstrates better performance and faster compared to those PCNN based segmentation algorithms which require the number of iterations and image entropy as iteration stop condition. Experimental results show the effectiveness of the proposed method on segmentation results and speed performance.

show abstract

Mega voltage X-ray image segmentation and ambient noise removal

Cited by 4 publications

References 5 publications

Review of pulse-coupled neural networks

Review of pulse-coupled neural networks

Image Segmentation by Using Grayscale Iteration Threshold Pulse Couple Neural Network

A Novel Method for Grayscale Image Segmentation by Using GIT-PCANN

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