Quantitative Analysis of Diffusion Weighted MR Images of Brain Tumor Using Signal Intensity Gradient Technique

Shanbhag, S. S.; Udupi, G. R.; Patil, K. M.; Ranganath, K.

doi:10.1155/2014/619081

Cited by 4 publications

(3 citation statements)

References 28 publications

(32 reference statements)

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“…Similarly, the trend of cost reduction will almost remains same for other image processing applications [4–8, 23–27] either implemented as parallel or sequential architecture. The [6] and [23] applications are comprised of four anti-quadrant symmetric filters and four non-quadrant symmetric filters for performing respective filtering tasks.…”

Section: Discussionmentioning

confidence: 97%

“…Firstly it provides the capability within a single design for efficiently implementing a wide range of filter types and secondly it offers a further cost reduction via resource sharing for implementing those image-processing applications which require multiple types of filters sequentially for performing diversified image processing tasks. The diversified filter requirement for different applications ranges from biomedical [4, 5, 22, 23], computer vision [6, 24], surveillance and navigation [7, 25], industrial [26, 27] to geophysics [8] etc. In contrast to our proposed versatile framework, the previously reported structurally optimized designs such as [18] and [19] does not offer further cost reduction for such applications.…”

Section: Discussionmentioning

confidence: 99%

“…The [6] and [23] applications are comprised of four anti-quadrant symmetric filters and four non-quadrant symmetric filters for performing respective filtering tasks. The [27] requires two anti-quadrant symmetric filters and two non-quadrant symmetric filters.…”

Section: Discussionmentioning

confidence: 99%

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A Low Cost Structurally Optimized Design for Diverse Filter Types

et al. 2016

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A wide range of image processing applications deploys two dimensional (2D)-filters for performing diversified tasks such as image enhancement, edge detection, noise suppression, multi scale decomposition and compression etc. All of these tasks require multiple type of 2D-filters simultaneously to acquire the desired results. The resource hungry conventional approach is not a viable option for implementing these computationally intensive 2D-filters especially in a resource constraint environment. Thus it calls for optimized solutions. Mostly the optimization of these filters are based on exploiting structural properties. A common shortcoming of all previously reported optimized approaches is their restricted applicability only for a specific filter type. These narrow scoped solutions completely disregard the versatility attribute of advanced image processing applications and in turn offset their effectiveness while implementing a complete application. This paper presents an efficient framework which exploits the structural properties of 2D-filters for effectually reducing its computational cost along with an added advantage of versatility for supporting diverse filter types. A composite symmetric filter structure is introduced which exploits the identities of quadrant and circular T-symmetries in two distinct filter regions simultaneously. These T-symmetries effectually reduce the number of filter coefficients and consequently its multipliers count. The proposed framework at the same time empowers this composite filter structure with additional capabilities of realizing all of its Ψ-symmetry based subtypes and also its special asymmetric filters case. The two-fold optimized framework thus reduces filter computational cost up to 75% as compared to the conventional approach as well as its versatility attribute not only supports diverse filter types but also offers further cost reduction via resource sharing for sequential implementation of diversified image processing applications especially in a constraint environment.

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