RESUMEN
This paper proposes a medical image fusion method in the non-subsampled shearlet transform (NSST) domain to combine a gray-scale image with the respective pseudo-color image obtained through different imaging modalities. The proposed method applies a novel improved dual-channel pulse-coupled neural network (IDPCNN) model to fuse the high-pass sub-images, whereas the Prewitt operator is combined with maximum regional energy (MRE) to construct the fused low-pass sub-image. First, the gray-scale image and luminance of the pseudo-color image are decomposed using NSST to find the respective sub-images. Second, the low-pass sub-images are fused by the Prewitt operator and MRE-based rule. Third, the proposed IDPCNN is utilized to get the fused high-pass sub-images from the respective high-pass sub-images. Fourth, the luminance of the fused image is obtained by applying inverse NSST on the fused sub-images, which is combined with the chrominance components of the pseudo-color image to construct the fused image. A total of 28 diverse medical image pairs, 11 existing methods, and nine objective metrics are used in the experiment. Qualitative and quantitative fusion results show that the proposed method is competitive with and even outpaces some of the existing medical fusion approaches. It is also shown that the proposed method efficiently combines two gray-scale images.
RESUMEN
This publisher's note amends information in the Funding section of Appl. Opt.59, 5642 (2020).APOPAI0003-693510.1364/AO.391234.
RESUMEN
Multi-focus image fusion is defined as "the combination of a group of partially focused images of a same scene with the objective of producing a fully focused image." Normally, transform-domain-based image fusion methods preserve the textures and edges in the blend image, but many are translation variant. The translation-invariant transforms produce the same size approximation and detail images, which are more convenient to devise the fusion rules. In this work, a translation-invariant multi-focus image fusion approach using the à-trous wavelet transform is introduced, which uses fractal dimension as a clarity measure for the approximation coefficients and Otsu's threshold to fuse the detail coefficients. The subjective assessment of the proposed method is carried out using the fusion results of nine state-of-the-art methods. On the other hand, eight fusion quality metrics are considered for the objective assessment. The results of subjective and objective assessment on grayscale and color multi-focus image pairs illustrate that the proposed method is competitive and even better than some of the existing methods.
