Morphological image sharpening by double structuring elements for ultrasound images

Masayoshi, T.; Toshihiro, Nishimura; Akira, Shogo

doi:10.1109/iembs.2004.1403463

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Masayoshi T et al [29] addressed the classical problem of detecting low level structures in images using image segmentation. M Niwu, et al [6] represented that ANN s show a paradigm for machine learning and can be used in a variety of ways for image segmentation.…”

Section: Introductionmentioning

confidence: 99%

Effective evaluation of tumour region in brain MR images using hybrid segmentation

Verma

Urooj

Rituvijay

2014

2014 International Conference on Computing for Sustainable Global Development (INDIACom)

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The aim of this work is to validate a quantitative technique to extract different attributes from MR images. A method known as hybrid segmentation that combines threshold segmentation, watershed segmentation, edge detection and morphological operators is considered jointly. This combined technique is experimented with MR scanned images of human brains to detect tumour. Exact size and location of tumour is detected using present hybrid segmentation technique.

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Section: Introductionmentioning

confidence: 99%

Effective evaluation of tumour region in brain MR images using hybrid segmentation

Verma

Urooj

Rituvijay

2014

2014 International Conference on Computing for Sustainable Global Development (INDIACom)

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“…This second thing makes them less suitable to enhance specific medical features in the image. Important issues of the image sharpening for biomedical ultrasound images are as follows [18]: In ultrasound images, as stated above, preserving structures (edges and shapes) in the image is more important than determining accurate grey level values. For example, one might only wish to extract a region of interest from a particular ultrasound image.…”

Section: Introductionmentioning

confidence: 99%

Medical feature based evaluation of structuring elements for morphological enhancement of ultrasonic images

Shrimali

Anand

Kumar

et al. 2009

Journal of Medical Engineering & Technology

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This paper investigates the use of morphology-based nonlinear filters, and performs deterministic and statistical analysis of the linear combinations of the filters for the image quality enhancement of B-mode ultrasound images. The fact that the structuring element shape greatly influences the output of the filter, is one of the most important features of mathematical morphology. The present reported work comparatively evaluates the structuring elements for morphological liver image enhancement and verifies the hypothesis that the speckles visible in US images are short, slightly 'banana-shaped' white lines. Initially, five different liver images were morphologically filtered using 10 different structuring elements and then the filtered images were assessed quantitatively. Image quality parameters such as peak signal-to-noise ratio, mean square error and correlation coefficient have been used to evaluate the performance of the morphological filters with different structuring elements. To endorse the observation of the quantitative analysis, the filtered images were then evaluated qualitatively, based on the image features looked into by the medical fraternity. The evaluation parameters have been taken on the basis of the suggestions made by a group of radiologists. The results of the processed images were then evaluated by a different group of radiologists. A multi-point rank order method has been used to identify small differences or trends in observation. The subjective analysis by radiologists indicates that morphological filter using line shaped structuring element with length 2 performs better than the other structuring elements. These observations were found to be in line with the observations of quantitative analysis.

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Research advances and prospects of mathematical morphology in image processing

Zhao

Wang

2008

2008 IEEE Conference on Cybernetics and Intelligent Systems

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Morphological image sharpening by double structuring elements for ultrasound images

Cited by 3 publications

References 13 publications

Effective evaluation of tumour region in brain MR images using hybrid segmentation

Effective evaluation of tumour region in brain MR images using hybrid segmentation

Medical feature based evaluation of structuring elements for morphological enhancement of ultrasonic images

Research advances and prospects of mathematical morphology in image processing

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