Note: none of the programs or subprograms listed bellow are guaranteed in any way. They are relased under the Creative Commons GNU General Public License. To view a copy of this license, visit http://creativecommons.org/licenses/GPL/2.0. If you use any of the softwares below, please cite the accompanying article.


Software for assessing acinar dropout from histopathology images

This software implements framework to perform quantitative analysis on histopathological images of pancreas towards the goal of quantifying Acinar dropout. This program is capable of segmenting Acinar components in pancreas tissue as well as calculating the ratio of Acinar regions over all tissue regions. The method is described in Eisses et al paper "A Computer-Based Automated Algorithm for Assessing Acinar Dropout after Experimental Pancreatitis", PLOS One, PLOS ONE, 9(10), e110220, 2014. To use the software, download this file, and start by reading the ReadMeFirst.pdf file.


Transport and other lagrangian transforms for signal and image analysis

We've developed a series of 1D and 2D signal/image transforms for estimation, detection, and pattern recognition problems. Please visit our project page for links to several softwares.


Cell modeling and simulation from microscopy images (CellOrganizer)

Software for synthecizing models of cellular structures directly from microscopy images. The effort is in collaboration with Prof. Robert Murphy at Carnegie Mellon University. Software, examples, tutorials, and papers can be downloaded from the project website: CellOrganizer


Cell and nuclear segmentation from microscopy images

Matlab code, including GUI interface, for segmenting cells and nuclei from microscopy images with accurate borders. The work is described in Chen et al's, Cytometry al, pp. 495-507, 2013 paper (html) , as well as, ISBI 2012, pp. 768-771, (html) . Code . Start by reading "Tutorial.pdf" file.


Filament segmentation from microscopy images

Matlab code for extracting centerline location of cytoskeleton filaments from 2D microscopy images. The work is described in Basu et al's, Journal of Microscopy, 2014 (in press) paper. Code . Start by reading "readme.txt" file.


Morphometry using penalized Fisher discriminant analysis

Matlab code for computing a penalized version of the popular Fisher LDA procedure (FLDA). The FLDA is modified to include a least squares-type data reconstruction term, akin to the popular PCA procedure. The work is described in Wang et al's, Pattern Recognition Letters, pp. 2128-2135, 2011 paper (pdf) . Code . Start by reading "Generate_figures.m".


Nuclear morphometry image dataset

The data used in Wang et al's, Cytometry 77A, pp. 485-494, 2010. (pdf) . Data . The data consists of matlab 2D arrays, each containing one grayscale image of one segmented nucleus. See "readme.rtf" file inside.


Adaptive bases nonrigid image registration (r3d)

Software for registering (aligning) three dimensional images nonrigidly using the algorithm described in Rohde et al's "The adaptive bases algorithm for intensity based nonrigid registration", IEEE TMI 2003, vol 22, pp. 1470-1479 (pdf). The software was written in the C++ programming language and has been known to compile and function properly in Linux, Unix, and Apple workstations. Readme, code, test data, manual.


Interpolation artifacts in sub-pixel image registration

Matlab software for reproducing a portion of the figures 8 and 9 from Rohde et al's "Interpolation artifacts in sub-pixel image registration", IEEE TIP 2009, v. 18, pp. 333-345 (pdf) . To produce these figures simply download the source code, and type "image_translation_experiment" on the Matlab prompt.




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