Supervised color image segmentation, using LVQ networks and K-means. Application: cellular image

This paper proposes a new method for supervised color image classification by theKohonen map, based on LVQ algorithms. The sample of observations, constituted by image pixels with 3 color components in the color space, is at first projected into a Kohonen map. This map is represented in the 3-dimensional space, from the weight vectors resulting of the learning process . Image classification by kohonen is a low-level image processing task that aims at partitioning an image into homogeneous regions. How region homogeneity is defined depends on the application. In this paper color image quantisation by clustering is discussed. A clustering scheme, based on learning quantisation vector (LVQ), is constructed and compared to the K-means clustering algorithm. It is demonstrated that both perform equally well. However, the former performs better than the latter with respect to the known number of although class. Both depend on their initial conditions and may end up in local optima. Based on these findings, an LVQ scheme is constructed which is completely independent of initial conditions; this approach is a hybrid structure between competitive learning and splitting of the color space. For comparison, a K-means approach is applied; it is known to produce global optimal results, but with high computational load. The clustering scheme is shown to obtain near-global optimal results with low computational loadKeywords: color image, kohonen, LVQ, classification, K-mean

Green Tea Extract and (−)-Epigallocatechin-3-Gallate Inhibit Mast Cell-Stimulated Type I Collagen Expression in Keloid Fibroblasts via Blocking PI-3K/Akt Signaling Pathways

Keloid, a chronic fibro-proliferative disease, exhibits distinctive histological features characterized by an abundant extracellular matrix stroma, a local infiltration of inflammatory cells including mast cells (MCs), and a milieu of enriched cytokines. Previous studies have demonstrated that co-culture with MCs stimulate type I collagen synthesis in fibroblasts, but the signaling mechanisms remain largely unknown. In this study, we investigated the signaling pathways involved in MC-stimulated type I collagen synthesis and the effects of green tea extract (GTE) and its major catechin, (-)-epigallocatechin-3-gallate (EGCG), on collagen homeostasis in keloid fibroblasts. Our results showed that MCs significantly stimulated type I collagen expression in keloid fibroblasts, and the upregulation of type I collagen was significantly attenuated by blockade of phosphatidylinositol-3-kinase (PI-3K), mammalian target of rapamycin (mTOR), and p38 MAPK signaling pathways, but not by blockade of ERK1/2 pathway. Furthermore, GTE and EGCG dramatically inhibited type I collagen production possibly by interfering with the PI-3K/Akt/mTOR signaling pathway. Our findings suggest that interaction between MCs and keloid fibroblasts may contribute to excessive collagen accumulation in keloids and imply a therapeutic potential of green tea for the intervention and prevention of keloids and other fibrotic diseases. © 2006 The Society for Investigative Dermatology

Green Tea Extract and (−)-Epigallocatechin-3-Gallate Inhibit Mast Cell-Stimulated Type I Collagen Expression in Keloid Fibroblasts via Blocking PI-3K/Akt Signaling Pathways

Keloid, a chronic fibro-proliferative disease, exhibits distinctive histological features characterized by an abundant extracellular matrix stroma, a local infiltration of inflammatory cells including mast cells (MCs), and a milieu of enriched cytokines. Previous studies have demonstrated that co-culture with MCs stimulate type I collagen synthesis in fibroblasts, but the signaling mechanisms remain largely unknown. In this study, we investigated the signaling pathways involved in MC-stimulated type I collagen synthesis and the effects of green tea extract (GTE) and its major catechin, (-)-epigallocatechin-3-gallate (EGCG), on collagen homeostasis in keloid fibroblasts. Our results showed that MCs significantly stimulated type I collagen expression in keloid fibroblasts, and the upregulation of type I collagen was significantly attenuated by blockade of phosphatidylinositol-3-kinase (PI-3K), mammalian target of rapamycin (mTOR), and p38 MAPK signaling pathways, but not by blockade of ERK1/2 pathway. Furthermore, GTE and EGCG dramatically inhibited type I collagen production possibly by interfering with the PI-3K/Akt/mTOR signaling pathway. Our findings suggest that interaction between MCs and keloid fibroblasts may contribute to excessive collagen accumulation in keloids and imply a therapeutic potential of green tea for the intervention and prevention of keloids and other fibrotic diseases. © 2006 The Society for Investigative Dermatology

Mechanisms of Hypoxic Regulation of Plasminogen Activator Inhibitor-1 Gene Expression in Keloid Fibroblasts

Keloids are an excessive accumulation of extracellular matrix. Although numerous studies have shown elevated plasminogen activator inhibitor-1 (PAI-1) levels in keloid fibroblasts compared with those of normal skin. Their specific mechanisms involved in the differential expression of PAI-1 in these cell types. In this study, the upregulation of PAI-1 expression is demonstrated in keloid tissues and their derived dermal fibroblasts, attesting to the persistence, if any, of fundamental differences between in vivo and in vitro paradigms. We further examined the mechanisms involved in hypoxia-induced regulation of PAI-1 gene in dermal fibroblast derived from keloid lesions and associated clinically normal peripheral skins from the same patient. Primary cultures were exposed to an environmental hypoxia or desferroxamine. We found that the hypoxia-induced elevation of PAI-1 gene appears to be regulated at both transcriptional and post-transcriptional levels in keloid fibroblasts. Furthermore, our results showed a consistent elevation of HIF-1α protein level in keloid tissues compared with their normal peripheral skin controls, implying a potential role as a biomarker for local skin hypoxia. Treatment with antisense oligonucleotides against hypoxia-inducible factor 1α (HIF-1α) led to the downregulation of steady-state levels of PAI-1 mRNA under both normoxic and hypoxic conditions. Conceivably, our results suggest that HIF-1α may be a novel therapeutic target to modulate the scar fibrosis process

Mechanisms of Hypoxic Regulation of Plasminogen Activator Inhibitor-1 Gene Expression in Keloid Fibroblasts

Keloids are an excessive accumulation of extracellular matrix. Although numerous studies have shown elevated plasminogen activator inhibitor-1 (PAI-1) levels in keloid fibroblasts compared with those of normal skin. Their specific mechanisms involved in the differential expression of PAI-1 in these cell types. In this study, the upregulation of PAI-1 expression is demonstrated in keloid tissues and their derived dermal fibroblasts, attesting to the persistence, if any, of fundamental differences between in vivo and in vitro paradigms. We further examined the mechanisms involved in hypoxia-induced regulation of PAI-1 gene in dermal fibroblast derived from keloid lesions and associated clinically normal peripheral skins from the same patient. Primary cultures were exposed to an environmental hypoxia or desferroxamine. We found that the hypoxia-induced elevation of PAI-1 gene appears to be regulated at both transcriptional and post-transcriptional levels in keloid fibroblasts. Furthermore, our results showed a consistent elevation of HIF-1α protein level in keloid tissues compared with their normal peripheral skin controls, implying a potential role as a biomarker for local skin hypoxia. Treatment with antisense oligonucleotides against hypoxia-inducible factor 1α (HIF-1α) led to the downregulation of steady-state levels of PAI-1 mRNA under both normoxic and hypoxic conditions. Conceivably, our results suggest that HIF-1α may be a novel therapeutic target to modulate the scar fibrosis process

Extraction of specific parameters for skin tumour classification

In this paper, a methodological approach to the classification of tumour skin lesions in dermoscopy images is presented. Melanomas are the most malignant skin tumours. They grow in melanocytes, the cells responsible for pigmentation. This type of cancer is increasing rapidly; its related mortality rate is increasing more modestly, and inversely proportional to the thickness of the tumour. The mortality rate can be decreased by earlier detection of suspicious lesions and better prevention. Using skin tumour features such as colour, symmetry and border regularity, an attempt is made to determine if the skin tumour is a melanoma or a benign tumour. In this work, we are interested in extracting specific attributes which can be used for computer-aided diagnosis of melanoma, especially among general practitioners. In the first step, we eliminate surrounding hair in order to eliminate the residual noise. In the second step, an automatic segmentation is applied to the image of the skin tumour. This method reduces a colour image into an intensity image and approximately segments the image by intensity thresholding. Then, it refines the segmentation using the image edges, which are used to localize the boundary in that area of the skin. This step is essential to characterize the shape of the lesion and also to locate the tumour for analysis. Then, a sequences of transformations is applied to the image to measure a set of attributes (A: asymmetry, B: border, C: colour and D: diameter) which contain sufficient information to differentiate a melanoma from benign lesions. Finally, the various signs of specific lesion (ABCD) are provided to an artificial neural network to differentiate between malignant tumours and benign lesions