A multiple covariance approach for cell detection of Gram-stained smears images

Abstract

Microscope examination of Gram stained clinical specimens is used for aiding the diagnosis of patients with infectious diseases. In high volume pathology laboratories, this manual microscopy examination is considered time consuming and labour intensive. Unfortunately, despite the great benefits offered from the application of Computer Aided Diagnosis (CAD) systems, to our knowledge, the highest automation stage for Gram stained slide analysis is only at the preanalytical process. This paper takes the first steps towards the application of computer vision to direct smear, Gram stained images. To that end, we present a novel Gram stain image dataset. In addition, we also propose a multiple covariance approach for leukocyte and epithelial cell detection in Gram stain images. Each covariance matrix represents a particular image region characterising the cell's deformed structure. As covariance matrices form points on an Symmetric Positive Definite (SPD) manifold, the traditional Euclidean-based analysis cannot be used. As such, we first map the manifold points into the Reproducing Kernel Hilbert Space (RKHS). The analysis is done via a novel kernel similarity function that allows comparison between sets of covariance matrices. The proposed approach is contrasted, in the proposed dataset, with two recent state of the art methods in pedestrian detection: Histogram Of Gradient (HOG) and the traditional single covariance matrix approach. We found that the proposed approach outperformed both of these methods.

SlideNet: Fast and Accurate Slide Quality Assessment Based on Deep Neural Networks

“…One publicly available dataset with Gram stain images focuses on the classification of micro-organisms within the image slides [10]. The dataset used in [10] contains 150 colour images with positive and negative examples of leukocyte and epithelial cells.…”

“…One publicly available dataset with Gram stain images focuses on the classification of micro-organisms within the image slides [10]. The dataset used in [10] contains 150 colour images with positive and negative examples of leukocyte and epithelial cells. Another dataset presented in [9] contains 320 stained slide images not only using the Gram stain technique but also other different staining techniques.…”