Mammographic feature enhancement by multiscale analysis

Abstract

Introduces a novel approach for accomplishing mammographic feature analysis by overcomplete multiresolution representations. The authors show that efficient representations may be identified within a continuum of scale-space and used to enhance features of importance to mammography. Methods of contrast enhancement are described based on three overcomplete multiscale representations: 1) the dyadic wavelet transform (separable), 2) the phi-transform (nonseparable, nonorthogonal), and 3) the hexagonal wavelet transform (nonseparable). Multiscale edges identified within distinct levels of transform space provide local support for image enhancement. Mammograms are reconstructed from wavelet coefficients modified at one or more levels by local and global nonlinear operators. In each case, edges and gain parameters are identified adaptively by a measure of energy within each level of scale-space. The authors show quantitatively that transform coefficients, modified by adaptive nonlinear operators, can make more obvious unseen or barely seen features of mammography without requiring additional radiation. The authors' results are compared with traditional image enhancement techniques by measuring the local contrast of known mammographic features. They demonstrate that features extracted from multiresolution representations can provide an adaptive mechanism for accomplishing local contrast enhancement. By improving the visualization of breast pathology, one can improve chances of early detection while requiring less time to evaluate mammograms for most patients.

Objective Quality Assessment Measurement for Typhoon Cloud Image Enhancement

“…We will compare the performance between the proposed algorithm (ZCJ) [14], histogram equalization (HE), AFL method [15], WYQ method [16], XLZ method [17] and un-sharpened mask method (USM). Fig.1 (a)-(b) respectively represent original typhoon cloud image (typhoon No.…”

A New CAD System for Breast Microcalcifications Diagnosis

“…3) Features-based techniques: consider the processed mammographic image characteristics and include morphological operations, wavelet transform and fractal approach [9,47].…”

“…Therefore, several contrast enhancement techniques have been developed, in order to solve this issue and facilitate lesions detection [9,14,22,31,43,47,54,56] as described in the previous section.…”

Generalized symmetric interpolating wavelets

“…The theory of interpolating wavelets based on a subdivision scheme has attracted much attention recently [ 1,9,12,13,17,22,27,29,40,42,[45][46][47][48][49][54][55][56]65,66]. Because the digital sampling space is exactly homomorphic to the multiscale spaces generated by interpolating wavelets, the wavelet coefficients can be obtained from linear combinations of discrete samples rather than from traditional inner product integrals.…”