A new approach to design triplet halfband filter banks based on balanced-uncertainty optimization

Gawande, Jayanand P.; Rahulkar, Amol D.; Holambe, Raghunath S.

doi:10.1016/j.dsp.2016.06.001

Cited by 10 publications

(3 citation statements)

References 28 publications

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“…We used a class of THFB given in [26] for medical image representation due to its desirable properties (symmetry, near orthogonality, regularity, time frequency localization and frequency selectivity). The detail literature on THFB is presented in [27]. In order to apply THFB to medical images, 2-D extension of wavelets is required.…”

Section: Wavelet Decomposition With Thfbmentioning

confidence: 99%

Content based medical image retrieval based on new efficient local neighborhood wavelet feature descriptor

2019

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This paper presents a new class of local neighborhood based wavelet feature descriptor (LNWFD) for content based medical image retrieval (CBMIR). To retrieve images effectively from large medical databases is backbone of diagnosis. Existing wavelet transform based medical image retrieval methods suffer from high length feature vector with confined retrieval performance. Triplet half-band filter bank (THFB) enhanced the properties of wavelet filters using three kernels. The influence of THFB has employed in the proposed method. First, triplet half-band filter bank (THFB) is used for single level wavelet decomposition to obtain four sub-bands. Next, the relationship among wavelet coefficients is exploited at each sub-band using 3 × 3 neighborhood window to form LNWFD pattern. The novelty of the proposed descriptor lies in exploring relation between wavelet transform values of pixels rather than intensity values which gives more detail local information in wavelet sub-bands. Thus, proposed feature descriptor is robust against illumination. Manhattan distance is used to compute similarity between query feature vector and feature vector of database. The proposed method is tested for medical image retrieval using OASIS-MRI, NEMA-CT, and Emphysema-CT databases. The average retrieval precisions achieved are 71.45%, 99.51% of OASIS-MRI and NEMA-CT databases for top ten matches considered respectively and 55.51% of Emphysema-CT database for top 50 matches. The superiority in terms of performance of the proposed method is confirmed by the experimental results over the well-known existing descriptors.

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Section: Wavelet Decomposition With Thfbmentioning

confidence: 99%

Content based medical image retrieval based on new efficient local neighborhood wavelet feature descriptor

2019

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“…The EFP is well known and has been used in the design of spline wavelets [3]. The EFP has the property that its coefficients are integers and the polynomial is symmetric.…”

Section: Design Of New Halfband Polynomialmentioning

confidence: 99%

“…One-dimensional (1-D) wavelet FBs provides optimal representation for 1-D piecewise smooth signals [2,3]. However, they fail to explore the geometric regularity that exists in majority of natural images and capture limited directional information.…”

Section: Introductionmentioning

confidence: 99%

Design of two-dimensional PR quincunx filter banks with Euler–Frobenius polynomial and lifting scheme

2019

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Two-dimensional (2-D) filter banks (FBs) have played a significant role in retrieving the directional information of images. In this paper, we propose a technique to design 2-D two-channel perfect reconstruction (PR) FBs with quincunx sampling. The proposed design method comprises two stages. In the first stage, we propose the design of a new halfband polynomial using Euler-Frobenius polynomial (EFP). This is constructed by imposing vanishing moment and PR constraints on EFP. The resulting new polynomial is a maximally flat Euler-Frobenius halfband polynomial (EFHBP). Later, in the second stage, EFHBP is used in a modified 2-D lifting scheme to design 2-D filters. The design examples for 2-D filters are presented and compared to existing filters. The performance shows that proposed filters have better regularity, symmetry and less energy of the error compared with existing FBs. Finally, performance of designed filters is evaluated in image denoising application.

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Automated Heart Rate Measurement Using Wavelet Analysis of Face Video Sequences

Wakankar

Gawande

2018

Lecture Notes in Networks and Systems

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A new approach to design triplet halfband filter banks based on balanced-uncertainty optimization

Cited by 10 publications

References 28 publications

Content based medical image retrieval based on new efficient local neighborhood wavelet feature descriptor

Content based medical image retrieval based on new efficient local neighborhood wavelet feature descriptor

Design of two-dimensional PR quincunx filter banks with Euler–Frobenius polynomial and lifting scheme

Automated Heart Rate Measurement Using Wavelet Analysis of Face Video Sequences

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