  Large sequences of images (or movies) can now be obtained on an unprecedented
scale, which poses fundamental challenges to the existing image analysis
techniques. The challenges include heterogeneity, (automatic) alignment,
multiple comparisons, potential artifacts, and hidden noises. This paper
introduces our MATLAB package, Longitudinal Image Sequence Analysis (LISA), as
a one-stop ensemble of image processing and analysis tool for comparing a
general class of images from either different times, sessions, or subjects.
Given two contrasting sequences of images, the image processing in LISA starts
with selecting a region of interest in two representative images, followed by
automatic or manual segmentation and registration. Automatic segmentation
de-noises an image using a mixture of Gaussian distributions of the pixel
intensity values, while manual segmentation applies a user-chosen intensity
cut-off value to filter out noises. Automatic registration aligns the
contrasting images based on a mid-line regression whereas manual registration
lines up the images along a reference line formed by two user-selected points.
The processed images are then rendered for simultaneous statistical comparisons
to generate D, S, T, and P-maps. The D map represents a curated difference of
contrasting images, the S map is the non-parametrically smoothed differences,
the T map presents the variance-adjusted, smoothed differences, and the P-map
provides multiplicity-controlled p-values. These maps reveal the regions with
significant differences due to either longitudinal, subject-specific, or
treatment changes. A user can skip the image processing step to dive directly
into the statistical analysis step if the images have already been processed.
Hence, LISA offers flexibility in applying other image pre-processing tools.
LISA also has a parallel computing option for high definition images.
