Abstract
Various systems have been proposed to support biological image analysis, with the intent of decreasing false annotations and reducing the heavy burden on biologists. These systems generally comprise a feature extraction method and a classification method. Task-oriented methods for feature extraction leverage characteristic images for each problem, and they are very effective at improving the classification accuracy. However, it is difficult to utilize such feature extraction methods for versatile task in practice, because few biologists specialize in Computer Vision and/or Pattern Recognition to design the task-oriented methods. Thus, in order to improve the usability of these supporting systems, it will be useful to develop a method that can automatically transform the image features of general propose into the effective form toward the task of their interest. In this paper, we propose a semi-supervised feature transformation method, which is formulated as a natural coupling of principal component analysis (PCA) and linear discriminant analysis (LDA) in the framework of graph-embedding. Compared with other feature transformation methods, our method showed favorable classification performance in biological image analysis.
Highlights
In biological image analysis, biologists manually identify and/or classify the images captured via a microscope
The data usually comprise a large number of images, and the analysis imposes a heavy burden on biologists, which increases the risk of false annotations
We propose an efficient method for transforming features; it is based on principal component analysis (PCA), which directly uses a discriminant criterion for labeled input data
Summary
Biologists manually identify and/or classify the images captured via a microscope. In order to improve both efficiency and accuracy, there is a great demand for developing a system to support biologists with image annotation. Many such systems have been proposed [1,2,3,4,5], and some of them are currently being used in biological and medical research. These supporting systems, which analyze biological images, are generally constructed based on feature extraction and classification methods. The improvement is limited when the method is applied to an unexpected task
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