Component

This package contains some MatLab tools for multi-scale image processing. Briefly, the tools include: - Recursive multi-scale image decompositions (pyramids), including Laplacian pyramids, QMFs, Wavelets, and steerable pyramids. These operate on 1D or 2D signals of arbitrary dimension. Data structures are compatible with the MatLab wavelet toolbox. - Fast 2D convolution routines, with subsampling and boundary-handling. - Fast point-operations, histograms, histogram-matching. - Fast synthetic image generation: sine gratings, zone plates, fractals, etc. - Display routines for images and pyramids. These include several auto-scaling options, rounding to integer zoom factors to avoid resampling artifacts, and useful labeling (dimensions and gray-range).

It is a trainable interest point (anatomical landmarks) detection algorithm. It requires images and interest point coordinates. It can run independantly (using csv files to describe coordinates) or it can be executed using Cytomine.

Typical application: Morphometric studies (e.g. in zebrafish/drosphila development)

Used in: Evaluation and Comparison of Anatomical Landmark Detection Methods for Cephalometric X-Ray Images: A Grand Challenge http://dx.doi.org/10.1109/TMI.2015.2412951 Automatic localization of interest points in zebrafish images with tree-based methods 

This is the "prediction step" of the Pyxit segmentation model builder. It is a learnable segmentation algorithm based on ground-truth images and segmentation mask. It learns a multiple output pixel classification algorithm. It downloads from Cytomine-Core annotation images+alphamasks from project(s), build a segmentation (pixel classifier) model which is saved locally. Typical application: tumor detection in tissues in histology slides. 

This is a learnable segmentation algorithm based on ground-truth images and segmentation mask. It learns a multiple output pixel classification algorithm. It downloads from Cytomine-Core annotation images+alphamasks from project(s), build a segmentation (pixel classifier) model which is saved locally. Typical application: tumor detection in tissues in histology slides. It is based on "Fast Multi-Class Image Annotation with Random Subwindows and Multiple Output Randomized Trees" http://orbi.ulg.ac.be/handle/2268/12205 and was used in "A hybrid human-computer approach for large-scale image-based measurements using web services and machine learning" http://orbi.ulg.ac.be/handle/2268/162084?locale=en

This module is for applying classification models on objects. It downloads from Cytomine-Core annotation images and coordinate of annotated objects from project(s) and build a annotation classification model which is saved locally. It downloads from Cytomine-Core annotations images from an image (e.g. detected by an object finder), apply a classification model (previously saved locally), and uploads to Cytomine-Core annotation terms (in a userjob layer).