texture features

Primary tabs

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Local Phase Quantization (LPQ) descriptors

Package/Library: 
MATLAB
Matlab Image Processing Toolbox
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BSIF (Binarized Statistical Image Features)

Package/Library: 
MATLAB
Matlab Image Processing Toolbox
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Local BInary Pattern (general code)

Package/Library: 
MATLAB
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EBImage

EBImage is an open source image processing and analysis package for the statistical programming language R. Its primary goal is to enable automated analysis of large sets of images such as those obtained in high-throughput screening. An extensive range of basic image processing and feature extraction methods are provided. EBImage is available through the Bioconductor software project (www.bioconductor.org).

Strengths

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Mahotas

This library gives the numpy-based infrastructure functions for image processing with a focus on bioimage informatics. It provides image filtering and morphological processing as well as feature computation (both image-level features such as Haralick texture features and SURF local features). These can be used with other Python-based libraries for machine learning to build a complete analysis pipeline.

Mahotas is appropriate for users comfortable with programming or builders of end-user tools.

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CellProfiler

CellProfiler is designed to enable biologists without training in computer vision or programming to quantitatively measure cell or whole-organism phenotypes from thousands of images automatically. The researcher creates an analysis pipeline from modules that find cells and cell compartments, measure features of those cells to form a rich, quantitative dataset that characterizes the imaged site in all of its heterogeneity.

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CellCognition

Introduction

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VIGRA

VIGRA is a free C++ and Python library that provides fundamental image processing and analysis algorithms. Its generic architecture allows it to be used in many different application contexts and ecosystems. It is designed as an intelligent library (using the C++ template mechanism) which allows users to write code at a fairly high level of abstraction and optimizes away the abstraction overhead upon compilation. It can therefore work efficiently on very large data and forms the basis of ilastik and CellCognition.

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