Quantifying tumour-infiltrating lymphocyte subsets : a practical immuno-histochemical method

Background: Efficient histological quantification of tumour-infiltrating T and B lymphocyte (TIL) subsets in archival tissues would greatly facilitate investigations of the role of TIL in human cancer biology. We sought to develop such a method. Methods: Ten ×40 digital images of 4 μ sections of 16 ductal invasive breast carcinomas immunostained for CD3, CD4, CD8, and CD20 were acquired (a total of 640 images). The number of pixels in each image matching a partition of Lab colour space corresponding to immunostained cells were counted using the ‘Color range’ and ‘Histogram’ tools in Adobe Photoshop 7. These pixel counts were converted to cell counts per mm2 using a calibration factor derived from one, two, three or all 10 images of each case/antibody combination. Results: Variations in the number of labelled pixels per immunostained cell made individual calibration for each case/antibody combination necessary. Calibration based on two fields containing the most labelled pixels gave a cell count minimally higher (+ 5.3%) than the count based on 10-field calibration, with 95% confidence limits − 14.7 to + 25.3%. As TIL density could vary up to 100-fold between cases, this accuracy and precision are acceptable. Conclusion: The methodology described offers sufficient accuracy, precision and efficiency to quantify the density of TIL sub-populations in breast cancer using commonly available software, and could be adapted to batch processing of image files

A Neuromorphic Model for Achromatic and Chromatic Surface Representation of Natural Images

This study develops a neuromorphic model of human lightness perception that is inspired by how the mammalian visual system is designed for this function. It is known that biological visual representations can adapt to a billion-fold change in luminance. How such a system determines absolute lightness under varying illumination conditions to generate a consistent interpretation of surface lightness remains an unsolved problem. Such a process, called "anchoring" of lightness, has properties including articulation, insulation, configuration, and area effects. The model quantitatively simulates such psychophysical lightness data, as well as other data such as discounting the illuminant, the double brilliant illusion, and lightness constancy and contrast effects. The model retina embodies gain control at retinal photoreceptors, and spatial contrast adaptation at the negative feedback circuit between mechanisms that model the inner segment of photoreceptors and interacting horizontal cells. The model can thereby adjust its sensitivity to input intensities ranging from dim moonlight to dazzling sunlight. A new anchoring mechanism, called the Blurred-Highest-Luminance-As-White (BHLAW) rule, helps simulate how surface lightness becomes sensitive to the spatial scale of objects in a scene. The model is also able to process natural color images under variable lighting conditions, and is compared with the popular RETINEX model.Air Force Office of Scientific Research (F496201-01-1-0397); Defense Advanced Research Project and the Office of Naval Research (N00014-95-0409, N00014-01-1-0624

Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures

This paper summarizes the results of traditional image processing algorithms for detection of defects in concrete using images taken by Unmanned Aerial Systems (UASs). Such algorithms are useful for improving the accuracy of crack detection during autonomous inspection of bridges and other structures, and they have yet to be compared and evaluated on a dataset of concrete images taken by UAS. The authors created a generic image processing algorithm for crack detection, which included the major steps of filter design, edge detection, image enhancement, and segmentation, designed to uniformly compare dierent edge detectors. Edge detection was carried out by six filters in the spatial (Roberts, Prewitt, Sobel, and Laplacian of Gaussian) and frequency (Butterworth and Gaussian) domains. These algorithms were applied to fifty images each of defected and sound concrete. Performances of the six filters were compared in terms of accuracy, precision, minimum detectable crack width, computational time, and noise-to-signal ratio. In general, frequency domain techniques were slower than spatial domain methods because of the computational intensity of the Fourier and inverse Fourier transformations used to move between spatial and frequency domains. Frequency domain methods also produced noisier images than spatial domain methods. Crack detection in the spatial domain using the Laplacian of Gaussian filter proved to be the fastest, most accurate, and most precise method, and it resulted in the finest detectable crack width. The Laplacian of Gaussian filter in spatial domain is recommended for future applications of real-time crack detection using UAS