A versatile technique for visual enhancement of medical ultrasound images

Gupta, Savita; Kaur, Lakhwinder; Chauhan, R. C.; Saxena, Sahaj

doi:10.1016/j.dsp.2006.12.001

Cited by 68 publications

(43 citation statements)

References 36 publications

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“…MR brain images are collected from websites: www.radquiz.com, mri.co.nz/medimgs, newsroom.ucla.edu, www.ajnr.org. Implementation is done in Matlab 7.5 (R2007b).The performance metrics [50], [51]: coefficient of correlation (CoC) and structural similarity index (SSIM) are computed for quantitative comparison. The quantitative results are presented in table I.…”

Section: Resultsmentioning

confidence: 99%

Performance Improvement of Fuzzy C-mean Algorithm for Tumor Extraction in MR Brain Images

Sohi¹,

Kaur²,

Gupta³

2012

IJCA

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Aim of this paper is to develop an efficient fuzzy c-mean based segmentation algorithm to extract tumor region from MR brain images. First, cluster centroids are initialized through data analysis of tumor region, which optimizes the standard fuzzy cmean algorithm. Next, reconstruction based morphological operations are applied to enhance its performance for brain tumor extraction. The results show that simple fuzzy c-mean could not segment the region of interest properly, whereas enhanced algorithm effectively extracts the tumor region. From comparison with existing segmentation methods, enhanced fuzzy c-mean algorithm emerges as the most effective algorithm for extracting region of interest.

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Section: Resultsmentioning

confidence: 99%

Performance Improvement of Fuzzy C-mean Algorithm for Tumor Extraction in MR Brain Images

Sohi¹,

Kaur²,

Gupta³

2012

IJCA

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“…Here the input image is decomposed into detailed and approximation components and hard or soft thresholding is applied [11][12]. Threshold selection plays an important role in noise reduction [16].…”

Section: Transform Domain Filtersmentioning

confidence: 99%

“…The motive of speckle reduction filters is smoothing of the input image with preservation of edge information because in ultrasonic images, edges contains the useful information that the experts are concerned about, but there is always a tradeoff between noise suppression and loss of information. Some filters can perform more smoothing [4][5][6][7][8] [15] but at the cost of edge information and others [9][10][11] are able to retain the important edges but with less smoothing. In this paper a technique is proposed that is able to use the combined property of both edges preserving and smoothing filters.…”

Section: Transform Domain Filtersmentioning

confidence: 99%

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A Relook and Renovation over State-of-Art Salt and Pepper Noise Removal Techniques

Lal¹,

Kaur²,

Gupta³

2015

IJIGSP

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Abstract-Speckle is a multiplicative noise that degrades the quality of ultrasound images and its presence makes the visual inspection difficult. In addition, it limits the professional application of image processing techniques such as automatic lesion segmentation. So speckle reduction is an essential step before further processing of ultrasonic images. Numerous techniques have been developed to preserve the edges while reducing speckle noise, but these filters avoid smoothing near the edges to preserve fine details. The objective of this work is to suggest a new technique that enhances B-Scan breast ultrasound images by increasing the speckle reduction capability of an edge sensitive filter. In the proposed technique a local statics based filter is applied in the non homogeneous regions, on the output of an edge preserving filter and an edge map is used to retain the original edges. Experiments are conducted using synthetic test image and real time ultrasound images. The effectiveness of the proposed technique is evaluated qualitatively by experts and quantitatively in terms of various quality metrics. Results indicate that proposed method can reduce more noise and simultaneously preserve important diagnostic edge information in breast ultrasound images.

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“…As part of this research, main emphasis is on the assessment of suitability of image recognition systems in uncontrolled environment and their ability to use in real-world. The metrics coefficient of correlation (CoC) measure the degree of correlation between two images and SSIM can measure structural similarity between two images [23]. In this paper, these simple metrics, CoC and SSIM are used for image recognition, which are often used as image quality measures.…”

Section: Introductionmentioning

confidence: 99%

Image Recognition using Coefficient of Correlation and Structural SIMilarity Index in Uncontrolled Environment

Kaur¹,

Kaur²,

Gupta³

2012

IJCA

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The goal of this paper is to analyse and improve the performance of metrics like Coefficient of Correlation (CoC) and Structural Similarity Index (SSIM) for image recognition in real-time environment. The main novelties of the methods are; it can work under uncontrolled environment and no need to store multiple copies of the same image at different orientations. The values of CoC and SSIM get changed if images are rotated or flipped or captured under bad/highly illuminated conditions. To increase the recognition accuracy, the input test image is pre-processed. First, discrete wavelet transform is applied to recognize the image captured under bad illuminated and dull lightning conditions. Second, to make the method rotation invariant, the test image is compared against the stored database image without and with rotations in the horizontal, vertical, diagonal, reverse diagonal and flipped directions. The image recognition performance is evaluated using the Recognition Rate and Rejection Rate. The results indicate that recognition performance of Correlation Coefficient and SSIM gets improved with rotations and discrete wavelet transform. Also it was observed that CoC with proposed modifications yield better results as compared to state of the art enhanced Principal Component Analysis and Enhanced Subspace Linear Discriminant Analysis.

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A versatile technique for visual enhancement of medical ultrasound images

Cited by 68 publications

References 36 publications

Performance Improvement of Fuzzy C-mean Algorithm for Tumor Extraction in MR Brain Images

Performance Improvement of Fuzzy C-mean Algorithm for Tumor Extraction in MR Brain Images

A Relook and Renovation over State-of-Art Salt and Pepper Noise Removal Techniques

Image Recognition using Coefficient of Correlation and Structural SIMilarity Index in Uncontrolled Environment

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