… the sparse reconstruction method is employed to roughly remove the background and accentuate
the nuclei of pathological images… sampled from the pathological images and fed to the …

… The reason that we choose the cosine similarity is because contrast variations often exist in
digitized pathology images due to unstable staining, and the cosine similarity is proven to be …

… image, we propose to utilize sparse reconstruction to generate the probability map by comparing
the reconstructed image … algorithm for pathological images. The detection step exploits …

… general umbrella of sparse reconstructions, … images can be used to reconstruct full images
from undersampled data. This review describes the basic ideas behind sparse reconstruction …

… Similar to the PCA-TV model, we alternate between image decomposition steps using
the PCA-Sparse-TV model and registration to the brain-extracted atlas. We use a total of six …

… to the low-rank part and to decompose only the pathological image into its low-rank and
sparse components1. Instead, we completely replace the LRS decomposition. Specifically, we …

… pathologic structures [35-37] and even speckle patterns, most of the sparse reconstruction
… general dictionary to represent complex structures and textures in the ocular OCT images. …

… for low-resolution image analysis algorithms embedded in a multi-… Sparse coding and
dynamic sampling constitute the … images and designed in collaboration with a pathology …

… segment pathological lungs in 3-D low-dose CT images. Sparse shape composition is
integrated with the eigenvector space shape prior model, called eigenspace sparse shape …

… image, we propose to utilize sparse reconstruction to generate the probability map by comparing
the reconstructed image … algorithm for pathological images. The detection step exploits …