BIOCAT: a pattern recognition platform for customizable biological image classification and annotation

Jie Zhou,Santosh Lamichhane,Bing Ye,Hanchuan Peng,Gabriella Sterne

doi:10.1186/1471-2105-14-291

Abstract

BackgroundPattern recognition algorithms are useful in bioimage informatics applications such as quantifying cellular and subcellular objects, annotating gene expressions, and classifying phenotypes. To provide effective and efficient image classification and annotation for the ever-increasing microscopic images, it is desirable to have tools that can combine and compare various algorithms, and build customizable solution for different biological problems. However, current tools often offer a limited solution in generating user-friendly and extensible tools for annotating higher dimensional images that correspond to multiple complicated categories.ResultsWe develop the BIOimage Classification and Annotation Tool (BIOCAT). It is able to apply pattern recognition algorithms to two- and three-dimensional biological image sets as well as regions of interest (ROIs) in individual images for automatic classification and annotation. We also propose a 3D anisotropic wavelet feature extractor for extracting textural features from 3D images with xy-z resolution disparity. The extractor is one of the about 20 built-in algorithms of feature extractors, selectors and classifiers in BIOCAT. The algorithms are modularized so that they can be “chained” in a customizable way to form adaptive solution for various problems, and the plugin-based extensibility gives the tool an open architecture to incorporate future algorithms. We have applied BIOCAT to classification and annotation of images and ROIs of different properties with applications in cell biology and neuroscience.ConclusionsBIOCAT provides a user-friendly, portable platform for pattern recognition based biological image classification of two- and three- dimensional images and ROIs. We show, via diverse case studies, that different algorithms and their combinations have different suitability for various problems. The customizability of BIOCAT is thus expected to be useful for providing effective and efficient solutions for a variety of biological problems involving image classification and annotation. We also demonstrate the effectiveness of 3D anisotropic wavelet in classifying both 3D image sets and ROIs.

Highlights

Pattern recognition algorithms are useful in bioimage informatics applications such as quantifying cellular and subcellular objects, annotating gene expressions, and classifying phenotypes
For the CHO set, by using object statistics alone, which is a set of 7 features, BIOimage Classification and Annotation Tool (BIOCAT) achieves comparable results with Wndcharm and 6.3% more than the original literature (Random Forest is used as the classifier)
Wndchrm’s need of calculating thousands of complex features can lead to a much higher computational complexity which would make it slow on larger images (Wndchrm does not have a classification functionality for 3D images which would be more computational demanding.) BIOCAT’s adaptive design, on the other hand, allows the best suitable algorithms to be selected for a given biological image classification problem, which can sometimes be simple, as in the case of the CHO problem

Summary

Introduction

Pattern recognition algorithms are useful in bioimage informatics applications such as quantifying cellular and subcellular objects, annotating gene expressions, and classifying phenotypes. To provide effective and efficient image classification and annotation for the ever-increasing microscopic images, it is desirable to have tools that can combine and compare various algorithms, and build customizable solution for different biological problems. Pattern recognition algorithms have gained momentum in automatic analysis and quantification of biological images. Pattern recognition uses a trained classifier to automatically assign an image to a category of interest. The trained model can be used to predict unseen images’ category, with applications such as protein expression annotation and characterization, cell phenotype determination/counting, and subcellular protein arrangement [15,16,17,18,19,20]

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Conclusion