Segmentation of epidermal tissue with histopathological damage in images of haematoxylin and eosin stained human skin.

Background: Digital image analysis has the potential to address issues surrounding traditional histological techniques including a lack of objectivity and high variability, through the application of quantitative analysis. A key initial step in image analysis is the identification of regions of interest. A widely applied methodology is that of segmentation. This paper proposes the application of image analysis techniques to segment skin tissue with varying degrees of histopathological damage. The segmentation of human tissue is challenging as a consequence of the complexity of the tissue structures and inconsistencies in tissue preparation, hence there is a need for a new robust method with the capability to handle the additional challenges materialising from histopathological damage.Methods: A new algorithm has been developed which combines enhanced colour information, created following a transformation to the L*a*b* colourspace, with general image intensity information. A colour normalisation step is included to enhance the algorithm's robustness to variations in the lighting and staining of the input images. The resulting optimised image is subjected to thresholding and the segmentation is fine-tuned using a combination of morphological processing and object classification rules. The segmentation algorithm was tested on 40 digital images of haematoxylin & eosin (H&E) stained skin biopsies. Accuracy, sensitivity and specificity of the algorithmic procedure were assessed through the comparison of the proposed methodology against manual methods.Results: Experimental results show the proposed fully automated methodology segments the epidermis with a mean specificity of 97.7%, a mean sensitivity of 89.4% and a mean accuracy of 96.5%. When a simple user interaction step is included, the specificity increases to 98.0%, the sensitivity to 91.0% and the accuracy to 96.8%. The algorithm segments effectively for different severities of tissue damage.Conclusions: Epidermal segmentation is a crucial first step in a range of applications including melanoma detection and the assessment of histopathological damage in skin. The proposed methodology is able to segment the epidermis with different levels of histological damage. The basic method framework could be applied to segmentation of other epithelial tissues

Texture analysis of articular cartilage traumatic changes in the knee calculated from morphological 3.0 T MR imaging

Objectives: In the present work, we aim to identify changes in the cartilage texture of the injured knee in young, physically active, patients by computer analysis of MRI images based on 3.0 T morphological sequences. Methods: Fifty-three young patients with training injury or trauma in one knee underwent MRI and arthroscopy. Textural features were computed from the MRI images of the knee-cartilages and two classes were formed of 28 normal and 16 with pathology only in the medial femoral condyle (MFC) cartilage. Results: Textural features with statistically significant differences between the two classes were found only at the MFC and the medial tibial condyle (MTC) areas. Three features-combinations, at the MFC or the MTC, maximized the between classes separation, thus, rendering alterations in cartilage texture due to injury more evident. The MFC cartilage in the pathology class was found more inhomogeneous in the distribution of gray-levels and of lower texture anisotropy and the opposed MTC cartilage, though normal on MRI and arthroscopy, was found to have lower texture anisotropy than cartilage in the normal class. Conclusion: Texture analysis may be used as an adjunct to morphological MR imaging for improving the detection of subtle cartilage changes and contributes to early therapeutic approach. (C) 2013 Elsevier Ireland Ltd. All rights reserved

A modified Seeded Region Growing algorithm for vessel segmentation in breast MRI images for investigating the nature of potential lesions

The role of Magnetic Resonance Imaging (MRI) as an alternative protocol for screening of breast cancer has been intensively investigated during the past decade. Preliminary research results have indicated that gadolinium-agent administrative MRI scans may reveal the nature of breast lesions by analyzing the contrast-agent's uptake time. In this study, we attempt to deduce the same conclusion, however, from a different perspective by investigating, using image processing, the vascular network of the breast at two different time intervals following the administration of gadolinium. Twenty cases obtained from a 3.0-T MRI system (SIGNA HDx; GE Healthcare) were included in the study. A new modification of the Seeded Region Growing (SRG) algorithm was used to segment vessels from surrounding background. Delineated vessels were investigated by means of their topology, morphology and texture. Results have shown that it is possible to estimate the nature of the lesions with approximately 94.4% accuracy, thus, it may be claimed that the breast vascular network does encodes useful, patterned, information, which can be used for characterizing breast lesions. © Published under licence by IOP Publishing Ltd

HealthAgents: distributed multi-agent brain tumor diagnosis and prognosis

We present an agent-based distributed decision support system for the diagnosis and prognosis of brain tumors developed by the HEALTHAGENTS project. HEALTHAGENTS is a European Union funded research project, which aims to enhance the classification of brain tumours using such a decision support system based on intelligent agents to securely connect a network of clinical centres. The HEALTHAGENTS system is implementing novel pattern recognition discrimination methods, in order to analyse in vivo Magnetic Resonance Spectroscopy (MRS) and ex vivo/in vitro High Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HR-MAS) and DNA micro-array data. HEALTHAGENTS intends not only to apply forefront agent technology to the biomedical field, but also develop the HEALTHAGENTS network, a globally distributed information and knowledge repository for brain tumour diagnosis and prognosis