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

Graph-theoretic analysis of epileptic seizures on scalp EEG recordings

This work presents a novel modeling of neuronal activity of the brain by capturing the synchronization of EEG signals along the scalp. The pair-wise correspondence between elec-trodes recording EEG signals are used to establish edges be-tween these electrodes which then become the nodes of a syn-chronization graph. As EEG video signals are recorded over time, we discretize the time axis into overlapping epochs, and build a series of time-evolving synchronization graphs for each epoch and for each traditional frequency band. We show that graph theory provides a rich set of graph features that can be used for mining and learning from the EEG signals to determine temporal and spatial localization of epileptic seizures. We present several techniques to cap-ture the pair-wise synchronization and apply unsupervised learning algorithms, such as k-means clustering and multi-way modeling of third-order tensors, to analyze the labeled clinical data in the feature domain to detect the onset and origin location of the seizure. We use k-means clustering on two-way feature matrices for detection of seizures, and Tucker3 tensor decomposition for localization of seizures. We conduct an extensive parametric search to determine the best configuration of the model parameters including epoch length, synchronization metrics, and frequency bands, to achieve the highest accuracy. Our results are promising: we are able to detect the onset of seizure with an accuracy of 88.24%, and localize the onset of the seizure with an accuracy of 76.47%