Computed tomographic (CT) images are widely used in the diagnosis of stroke. In this paper, we present an automated method to detect and classify an abnormality into acute infarct, chronic infarct and hemorrhage at the slice level of non-contrast CT images. The proposed method consists of three main steps: image enhancement, detection of mid-line symmetry and classification of abnormal slices. A windowing operation is performed on the intensity distribution to enhance the region of interest. Domain knowledge about the anatomical structure of the skull and the brain is used to detect abnormalities in a rotation- and translation-invariant manner. A two-level classification scheme is used to detect abnormalities using features derived in the intensity and the wavelet domain. The proposed method has been evaluated on a dataset of 15 patients (347 image slices). The method gives 90% accuracy and 100% recall in detecting abnormality at patient level; and achieves an average precision of 91% and recall of 90% at the slice level.

A case of cerebral infarction after viper bite is described; the patient also had features of diffuse encephalopathy. Findings on MRI were suggestive of subacute hemorrhagic infarcts. Possible mechanisms for cerebral infarction in these circumstances were discussed. The mechanism of cerebral infarction in this case seemed to be vasospasm due to the action of the toxin, hemorrhagin, present in the venom.

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Quantum cellular automata (QCA) is a new technology in nanometer scale as one of the alternatives to nano cmos technology, QCA technology has large potential in terms of high space density and power dissipation with the development of faster computers with lower power consumption. This paper considers the problem of reliability analysis of Simple QCA circuits at layout level like QCA latches and NOT circuit. The tool used to tackle this problem is Bayesian networks (BN) that derive from convergence of statistics and Artificial Intelligence. QCA circuit is transformed in to Bayesian framework for getting the probability of correct output and Reliability analysis performed on the resulting circuits for finding the defective cells in QCA circuit. This will increase overall efficiency of circuit and hence speed of the circuit with lower power consumption.