Automatic Tracing and Segmentation of Rat Mammary Fat Pads in MRI Image Sequences Based on Cartoon-Texture Model

The growth patterns of mammary fat pads and glandular tissues inside the fat pads may be related with the risk factors of breast cancer.Quantitative measurements of this relationship are available after segmentation of mammary pads and glandular tissues.Rat fat pads may lose continuity along image sequences or adjoin similar intensity areas like epidermis and subcutaneous regions.A new approach for automatic tracing and segmentation of fat pads in magnetic resonance imaging(MRI) image sequences is presented,which does not require that the number of pads be constant or the spatial location of pads be adjacent among image slices.First,each image is decomposed into cartoon image and texture image based on cartoon-texture model.They will be used as smooth image and feature image for segmentation and for targeting pad seeds,respectively.Then,two-phase direct energy segmentation based on Chan-Vese active contour model is applied to partitioning the cartoon image into a set of regions,from which the pad boundary is traced iteratively from the pad seed.A tracing algorithm based on scanning order is proposed to accurately trace the pad boundary,which effectively removes the epidermis attached to the pad without any post processing as well as solves the problem of over-segmentation of some small holes inside the pad.The experimental results demonstrate the utility of this approach in accurate delineation of various numbers of mammary pads from several sets of MRI images.

Adaptive contourlet-wavelet iterative shrinkage/thresholding for remote sensing image restoration

In this paper, we present an adaptive two-step contourlet-wavelet iterative shrinkage/thresholding （TcwlST） algorithm for remote sensing image restoration. This algorithm can be used to deal with various linear inverse problems （LIPs）, including image deconvolution and reconstruction. This algorithm is a new version of the famous two-step iterative shrinkage/thresholding （TWIST） algorithm. First, we use the split Bregrnan Rudin-Osher-Fatemi （ROF） model, based on a sparse dictionary, to decompose the image into cartoon and texture parts, which are represented by wavelet and contourlet, respectively. Second, we use an adaptive method to estimate the regularization parameter and the shrinkage threshold. Finally, we use a linear search method to find a step length and a fast method to accelerate convergence. Results show that our method can achieve a signal-to-noise ratio improvement （ISNR） for image restoration and high convergence speed.