Euler Vector for Search and Retrieval of Gray-Tone Images

Bishnu, Arijit; Bhattacharya, Bhargab B.; Kundu, Malay K.; Murthy, C. A.; Acharya, Tinku

doi:10.1109/tsmcb.2005.846642

Cited by 16 publications

(9 citation statements)

References 43 publications

(37 reference statements)

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“…Several features proposed in the literature are invariant to translation (Bishnu et al, 2005), rotation (Kokare et al, 2006;Bishnu et al, 2005;Rallabandi and Rallabandi, 2008;AlGarni and Hamiane, 2008;Sastry et al, 2004;Xie, 2004), size (Bishnu et al, 2005;AlGarni and Hamiane, 2008;Lo et al, 2007) or illumination (Greenspan and Pinhas, 2007). In medical applications, rotation and size invariance are mostly necessary if we want to retrieve images similar in shape: typically, if we want to retrieve anatomical regions (Rahman et al, 2007;Horsthemke et al, 2007) or if we want to characterize lesions or patterns within a user-defined ROI (Kim et al, 2006) or within an automatically segmented region (Yu et al, 2005;Siadat et al, 2005;Antani et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Wavelet optimization for content-based image retrieval in medical databases

Quellec

Lamard

Cazuguel

et al. 2010

Medical Image Analysis

175

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We propose in this article a Content-Based Image Retrieval method for diagnosis aid in medical fields. In the proposed system, images are indexed in a generic fashion, without extracting domain-specific features: a signature is built for each image from its wavelet transform. These image signatures characterize the distribution of wavelet coefficients in each subband of the decomposition. A distance measure is then defined to compare two image signatures and thus retrieve the most similar images in a database when a query image is submitted by a physician. To retrieve relevant images from a medical database, the signatures and the distance measure must be related to the medical interpretation of images. As a consequence, we introduce several degrees of freedom in the system so that it can be tuned to any pathology and image modality. In particular, we propose to adapt the wavelet basis, within the lifting scheme framework, and to use a custom decomposition scheme. Weights are also introduced between subbands. All these parameters are tuned by an optimization procedure, using the medical grading of each image in the database to define a performance measure. The system is assessed on two medical image databases: one for diabetic retinopathy follow up and one for screening mammography, as well as a general purpose database. Results are promising: a mean precision of 56.50%, 70.91% and 96.10% is achieved for these three databases, when five images are returned by the system.

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Section: Introductionmentioning

confidence: 99%

Wavelet optimization for content-based image retrieval in medical databases

Quellec

Lamard

Cazuguel

et al. 2010

Medical Image Analysis

175

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“…The Euler number of an image is defined as the number of connected components minus the number of holes in the image. If C and H denote the number of connected components and the number of holes in a digital image, respectively, then its Euler number E = C − H [2,20,31]. The ordered pair (C, H) is called the Euler pair of a digital image.…”

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confidence: 99%

Minimum-Weight Edge Discriminators in Hypergraphs

Bhattacharya¹,

Das

Ganguly

2014

Electron. J. Combin.

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In this paper we introduce the concept of minimum-weight edge-discriminators in hypergraphs, and study its various properties. For a hypergraph H = (V, E ), a function λ :, for every two distinct hyperedges Ei, Ej ∈ E . An optimal edge-discriminator on H, to be denoted by λH, is an edge-discriminator on H satisfying v∈V λH(v) = min λ v∈V λ(v), where the minimum is taken over all edge-discriminators on H. We prove that any hypergraph H = (V, E ), with |E | = n, satisfies v∈V λH(v) ≤ n(n + 1)/2, and equality holds if and only if the elements of E are mutually disjoint. For r-uniform hypergraphs H = (V, E ), it follows from results on Sidon sequences that v∈V λH(v) ≤ |V| r+1 + o(|V| r+1 ), and the bound is attained up to a constant factor by the complete r-uniform hypergraph. Next, we construct optimal edge-discriminators for some special hypergraphs, which include paths, cycles, and complete r-partite hypergraphs. Finally, we show that no optimal edge-discriminator on any hypergraph H = (V, E ), with |E | = n (≥ 3), satisfies v∈V λH(v) = n(n + 1)/2 − 1. This shows that not all integer values between n and n(n + 1)/2 can be the weight of an optimal edge-discriminator of a hypergraph, which, in turn, raises many other interesting combinatorial questions.1991 Mathematics Subject Classification. 05C65, 05C78, 90C27.

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“…The images are also rescaled such that the dynamic range of the intensity levels is mapped to [0,255]. Since, Euler number is easily computable [4], Euler-vector of a gray-tone image also provides a quick combinatorial signature. The Euler vector of a image is found to remain near invariant under inclusion of salt and pepper or gaussian noise followed by filtering, and also under JPEG compression.…”

Section: Euler Vector Computationmentioning

confidence: 99%

“…Determination of a compact set of parameters for a gray-tone image which is easy to compute, suitable for efficient database search, and admits robustness against transformations and noise, is now highly needed in the emerging domain of the Internet technology. Earlier approaches to image characterization include,, i) spatial features like amplitude and histogram descriptors; ii) transform features like Fourier descriptor, DCT, iii) shape based features like area, Euler number, center of mass, moments, eccentricity, etc., iv) syntactic features based on structural peculiarities, v) statistical and structural texture features [l, 21. In this work, we discuss a new parameter called Euler vector [3] of a gray-tone image. For a binary image, Euler number (genus) is defined as the difference between the number of connected components (objects) and the number of holes [l].…”

Section: Introductionmentioning

confidence: 99%

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Content based image retrieval: related issues using Euler vector

Bishnu

Bhunre

Bhattacharya

et al.

Proceedings. International Conference on Image Processing

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A combinatorial characterization of a gray-tone image called Euler vector is discussed. Euler vector comprises a 4-tuple, where each element is an integer representing the Euler number of the partial binary image formed by the four most significant bit planes of the gray-tone image. The vector is topological& invariant and can be used for image indexing and retrieval. The Euler vector for all the images in the database can be arranged using any multidimensional data structure. For retrieval, a query range is to be defined around the query image vector: We use a simple statistical technique to specifV the query range. Next, we propose a modification in the Kd-Tree construction to build a simple hybrid tree that supports efficient adaptive clustering and indexing. The same data structure is used,for clustering and indexing.

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Euler Vector for Search and Retrieval of Gray-Tone Images

Cited by 16 publications

References 43 publications

Wavelet optimization for content-based image retrieval in medical databases

Wavelet optimization for content-based image retrieval in medical databases

Minimum-Weight Edge Discriminators in Hypergraphs

Content based image retrieval: related issues using Euler vector

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