Now showing 1 - 2 of 2
  • Publication
    An underwater lighting and turbidity image repository for analysing the performance of image-based non-destructive techniques
    Image processing-based methods, capable of detecting and quantifying cracks, surface defects or recovering 3D shape information are increasingly being recognised as a valuable tool for inspecting underwater structures. It is of great practical importance for inspectors to know the effectiveness of such techniques when applied in conditions. This paper considers an underwater environment characterised by poor visibility chiefly governed by the lighting and turbidity levels, along with a range of geometry and damage conditions of calibrated specimens. The paper addresses the relationship between underwater visibility and the performance of image-based methods through the development and calibration of a first open-source underwater lighting and turbidity image repository (ULTIR). ULTIR contains a large collection of images of submerged specimens that have been photographed under controlled lighting and turbidity levels featuring various forms of geometry and damage. ULTIR aims to facilitate inspectors when rationalising the use of image processing methods as part of an underwater inspection campaign and to enable researchers to efficiently evaluate the performance of image-based methods under realistic operating conditions. Stakeholders in underwater infrastructure can benefit through this first large, standardised, well-annotated, and freely available database of images and associated metadata.
      804Scopus© Citations 31
  • Publication
    Automated segmentation of nuclei in breast cancer histopathology images
    The process of Nuclei detection in high-grade breast cancer images is quite challenging in the case of image processing techniques due to certain heterogeneous characteristics of cancer nuclei such as enlarged and irregularly shaped nuclei, highly coarse chromatin marginalized to the nuclei periphery and visible nucleoli. Recent reviews state that existing techniques show appreciable segmentation accuracy on breast histopathology images whose nuclei are dispersed and regular in texture and shape; however, typical cancer nuclei are often clustered and have irregular texture and shape properties. This paper proposes a novel segmentation algorithm for detecting individual nuclei from Hematoxylin and Eosin (H&E) stained breast histopathology images. This detection framework estimates a nuclei saliency map using tensor voting followed by boundary extraction of the nuclei on the saliency map using a Loopy Back Propagation (LBP) algorithm on a Markov Random Field (MRF). The method was tested on both whole-slide images and frames of breast cancer histopathology images. Experimental results demonstrate high segmentation performance with efficient precision, recall and dice-coefficient rates, upon testing high-grade breast cancer images containing several thousand nuclei. In addition to the optimal performance on the highly complex images presented in this paper, this method also gave appreciable results in comparison with two recently published methods-Wienert et al. (2012) and Veta et al. (2013), which were tested using their own datasets.
      266Scopus© Citations 45