Optimizing automated characterization of liver fibrosis histological images by investigating color spaces at different resolutions

Texture analysis (TA) of histological images has recently received attention as an automated method of characterizing liver fibrosis. The colored staining methods used to identify different tissue components reveal various patterns that contribute in different ways to the digital texture of the image. A histological digital image can be represented with various color spaces. The approximation processes of pixel values that are carried out while converting between different color spaces can affect image texture and subsequently could influence the performance of TA. Conventional TA is carried out on grey scale images, which are a luminance approximation to the original RGB (Red, Green, and Blue) space. Currently, grey scale is considered sufficient for characterization of fibrosis but this may not be the case for sophisticated assessment of fibrosis or when resolution conditions vary. This paper investigates the accuracy of TA results on three color spaces, conventional grey scale, RGB, and Hue-Saturation-Intensity (HSI), at different resolutions. The results demonstrate that RGB is the most accurate in texture classification of liver images, producing better results, most notably at low resolution. Furthermore, the green channel, which is dominated by collagen fiber deposition, appears to provide most of the features for characterizing fibrosis images. The HSI space demonstrated a high percentage error for the majority of texture methods at all resolutions, suggesting that this space is insufficient for fibrosis characterization. The grey scale space produced good results at high resolution; however, errors increased as resolution decreased

Performance analysis of fuzzy aggregation operations for combining classifiers for natural textures in images

One objective for classifying pixels belonging to specific textures in natural images is to achieve the best performance in classification as possible. We propose a new unsupervised hybrid classifier. The base classifiers for hybridization are the Fuzzy Clustering and the parametric Bayesian, both supervised and selected by their well-tested performance, as reported in the literature. During the training phase we estimate the parameters of each classifier. During the decision phase we apply fuzzy aggregation operators for making the hybridization. The design of the unsupervised classifier from supervised base classifiers and the automatic computation of the final decision with fuzzy aggregation operations, make the main contributions of this paper

Human-Centered Object-Based Image Retrieval

Contains fulltext : 32598.pdf (publisher's version ) (Closed access)ICAPR 200

Disruption of the P2X7 purinoceptor gene abolishes chronic inflammatory and neuropathic pain.

The P2X(7) purinoceptor is a ligand-gated cation channel, expressed predominantly by cells of immune origin, with a unique phenotype which includes release of biologically active inflammatory cytokine, interleukin (IL)-1beta following activation, and unique ion channel biophysics observed only in this receptor family. Here we demonstrate that in mice lacking this receptor, inflammatory (in an adjuvant-induced model) and neuropathic (in a partial nerve ligation model) hypersensitivity is completely absent to both mechanical and thermal stimuli, whilst normal nociceptive processing is preserved. The knockout animals were unimpaired in their ability to produce mRNA for pro-IL-1beta, and cytometric analysis of paw and systemic cytokines from knockout and wild-type animals following adjuvant insult suggests a selective effect of the gene deletion on release of IL-1beta and IL-10, with systemic reductions in adjuvant-induced increases in IL-6 and MCP-1. In addition, we show that this receptor is upregulated in human dorsal root ganglia and injured nerves obtained from chronic neuropathic pain patients. We hypothesise that the P2X(7) receptor, via regulation of mature IL-1beta production, plays a common upstream transductional role in the development of pain of neuropathic and inflammatory origin. Drugs which block this target may have the potential to deliver broad-spectrum analgesia

Shortest Paths in HSI Space for Color Texture Classification

Part 5: Perceptual IntelligenceInternational audienceColor texture representation is an important step in the task of texture classification. Shortest paths was used to extract color texture features from RGB and HSV color spaces. In this paper, we propose to use shortest paths in the HSI space to build a texture representation for classification. In particular, two undirected graphs are used to model the H channel and the S and I channels respectively in order to represent a color texture image. Moreover, the shortest paths is constructed by using four pairs of pixels according to different scales and directions of the texture image. Experimental results on colored Brodatz and USPTex databases reveal that our proposed method is effective, and the highest classification accuracy rate is 96.93$$\%$$ in the Brodatz database