Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Discriminating small wooded elements in rural landscape from aerial photography: a hybrid pixel/object-based analysis approach

While small, fragmented wooded elements do not represent a large surface area in agricultural landscape, their role in the sustainability of ecological processes is recognized widely. Unfortunately, landscape ecology studies suffer from the lack of methods for automatic detection of these elements. We propose a hybrid approach using both aerial photographs and ancillary data of coarser resolution to automatically discriminate small wooded elements. First, a spectral and textural analysis is performed to identify all the planted-tree areas in the digital photograph. Secondly, an object-orientated spatial analysis using the two data sources and including a multi-resolution segmentation is applied to distinguish between large and small woods, copses, hedgerows and scattered trees. The results show the usefulness of the hybrid approach and the prospects for future ecological applications

Multilayer Complex Network Descriptors for Color-Texture Characterization

A new method based on complex networks is proposed for color-texture analysis. The proposal consists on modeling the image as a multilayer complex network where each color channel is a layer, and each pixel (in each color channel) is represented as a network vertex. The network dynamic evolution is accessed using a set of modeling parameters (radii and thresholds), and new characterization techniques are introduced to capt information regarding within and between color channel spatial interaction. An automatic and adaptive approach for threshold selection is also proposed. We conduct classification experiments on 5 well-known datasets: Vistex, Usptex, Outex13, CURet and MBT. Results among various literature methods are compared, including deep convolutional neural networks with pre-trained architectures. The proposed method presented the highest overall performance over the 5 datasets, with 97.7 of mean accuracy against 97.0 achieved by the ResNet convolutional neural network with 50 layers.Comment: 20 pages, 7 figures and 4 table

Geodesic Distance Histogram Feature for Video Segmentation

This paper proposes a geodesic-distance-based feature that encodes global information for improved video segmentation algorithms. The feature is a joint histogram of intensity and geodesic distances, where the geodesic distances are computed as the shortest paths between superpixels via their boundaries. We also incorporate adaptive voting weights and spatial pyramid configurations to include spatial information into the geodesic histogram feature and show that this further improves results. The feature is generic and can be used as part of various algorithms. In experiments, we test the geodesic histogram feature by incorporating it into two existing video segmentation frameworks. This leads to significantly better performance in 3D video segmentation benchmarks on two datasets

Integration of feature distributions for colour texture segmentation

This paper proposes a new framework for colour texture segmentation and determines the contribution of colour and texture. The distributions of colour and texture features provides the discrimination between different colour textured regions in an image. The proposed method was tested using different mosaic and natural images. From the results, it is evident that the incorporation of colour information enhanced the colour texture segmentation and the developed framework is effective

Fine-scale mapping of vector habitats using very high resolution satellite imagery : a liver fluke case-study

The visualization of vector occurrence in space and time is an important aspect of studying vector-borne diseases. Detailed maps of possible vector habitats provide valuable information for the prediction of infection risk zones but are currently lacking for most parts of the world. Nonetheless, monitoring vector habitats from the finest scales up to farm level is of key importance to refine currently existing broad-scale infection risk models. Using Fasciola hepatica, a parasite liver fluke as a case in point, this study illustrates the potential of very high resolution (VHR) optical satellite imagery to efficiently and semi-automatically detect detailed vector habitats. A WorldView2 satellite image capable of <5m resolution was acquired in the spring of 2013 for the area around Bruges, Belgium, a region where dairy farms suffer from liver fluke infections transmitted by freshwater snails. The vector thrives in small water bodies (SWBs), such as ponds, ditches and other humid areas consisting of open water, aquatic vegetation and/or inundated grass. These water bodies can be as small as a few m(2) and are most often not present on existing land cover maps because of their small size. We present a classification procedure based on object-based image analysis (OBIA) that proved valuable to detect SWBs at a fine scale in an operational and semi-automated way. The classification results were compared to field and other reference data such as existing broad-scale maps and expert knowledge. Overall, the SWB detection accuracy reached up to 87%. The resulting fine-scale SWB map can be used as input for spatial distribution modelling of the liver fluke snail vector to enable development of improved infection risk mapping and management advice adapted to specific, local farm situations