1. A method for detecting and segmenting a lesion from longitudinal, time series, or multiparametric imaging by utilizing spectral embedding-based active contour (SEAC), the method comprising: (a) obtaining a plurality of images from a longitudinal, time series, or multi-parametric imaging; (b) applying spectral embedding (SE) to the longitudinal, time series, or multi-parametric imaging in a pixel-wise fashion to reduce a plurality of time point images to a single parametric embedding image (PrEIm), wherein the spectral embedding (SE) produces three smallest eigenvectors at each pixel location in the single parametric image by presenting color values in the parametric embedding image (PrEIm); (c) calculating spatial tensor-based gradients on the parametric embedding image (PrEIm) derived from spectral embedding (SE) eigenvectors; (d) selecting a point within an object of interest in the parametric embedding image (PrEIm) to serve as an initialization for an active contour (AC) model describing boundaries of shapes in the parametric embedding image (PrEIm); (e) evolving the active contour (AC) model on the parametric embedding image (PrEIm), wherein the active contour model is simultaneously driven by both boundary and region information obtained from the parametric embedding image (PrEIm), wherein the evolving of the active contour (AC) model at a border between a foreground and background area of the parametric embedding image (PrEIm) stops when a gradient magnitude between the foreground and background areas is maximized and a difference between intensity distributions and region statistics for the foreground and background is maximized; and (f) detecting and segmenting the lesion based on morphological features derived from (e).