Virus Texture Analysis Using Local Binary Patterns and Radial Density Profiles

Abstract

We investigate the discriminant power of two local and two global texture measures on virus images. The viruses are imaged using negative stain transmission electron microscopy. Local binary patterns and a multi scale extension are compared to radial density profiles in the spatial domain and in the Fourier domain. To assess the discriminant potential of the texture measures a Random Forest classifier is used. Our analysis shows that the multi scale extension performs better than the standard local binary patterns and that radial density profiles in comparison is a rather poor virus texture discriminating measure. Furthermore, we show that the multi scale extension and the profiles in Fourier domain are both good texture measures and that they complement each other well, that is, they seem to detect different texture properties. Combining the two, hence, improves the discrimination between virus textures.

Evaluation of noise robustness for local binary pattern descriptors in texture classification

“…They were chosen to have different characteristics in terms of number of classes, number of samples, class homogeneity with regards to scale, perspective, and illumination. The texture datasets are Brodatz [13], KTH-TIPS2b [14], Kylberg [15], Mondial Marmi [16], UIUC [17], and a Virus texture dataset [11]. Figure 1 shows four samples from four classes in each of the six datasets.…”

“…http://jivp.eurasipjournals.com/content/2013/1/17 The Virus dataset was first used in [11], and is based on transmission electron microscopy images of 15 different virus types. The virus types vary both in size (diameters from 25 to 270 nm) and shape; some are icosahedral while others are elongated.…”

“…The virus types vary both in size (diameters from 25 to 270 nm) and shape; some are icosahedral while others are elongated. Texture patches are extracted as disk-shaped regions with the same diameter as the viruses, centered in automatically (not always correctly) segmented virus particles, see [11] for more details. The texture samples are then resampled to the same size (41 × 41 pixels) using a Lanczos kernel with a sinc window of a = 2.…”

“…In brief, binary codes with more transitions between '0' and '1' than a specific value (typically two) are called nonuniform. In this way, the number of possible binary codes decreases but at the same time some important information may be lost, see for example [10,11]. This is why both uniform and non-uniform binary codes are considered in this article.…”

Vectorial approximations of infinite-dimensional covariance descriptors for image classification

“…For this task, we used the Virus dataset [5], which contains images of 15 different virus classes. Each class has 100 segmented grayscale images, with a resolution of 41×41 pixels, all formed by transmission electron microscopy.…”