N. Shivakumar

Sujatha A, L Govindaraju, N Shivakumar

Fuzzy rule-based model is a powerful tool for imitating the human way of thinking and solving uncertainty-related problems as it allows for understandable and interpretable rule bases. The objective of this paper is to study the applicability of fuzzy rule-based modelling to quantify soil classification for engineering purposes by qualitatively considering soil index properties. The classification system of the Highway Research Board is considered to illustrate a fuzzy rule-based model. The soil's index properties are fuzzified using triangular functions, and the fuzzy membership values are calculated. Fuzzy arithmetical operators are then applied to the membership values obtained for classification. Fuzzy decision tree classification algorithm is used to derive fuzzy if-then rules to quantify qualitative soil classification. The proposed system is implemented in MATLAB. The results obtained are checked and the implementation of the proposed model is measured against the outcomes of the laboratory tests.

Anirudh Chandrashekar, Ashok Handa, Natesh Shivakumar et al.

Contrast-enhanced computed tomography angiograms (CTAs) are widely used in cardiovascular imaging to obtain a non-invasive view of arterial structures. However, contrast agents are associated with complications at the injection site as well as renal toxicity leading to contrast-induced nephropathy (CIN) and renal failure. We hypothesised that the raw data acquired from a non-contrast CT contains sufficient information to differentiate blood and other soft tissue components. We utilised deep learning methods to define the subtleties between soft tissue components in order to simulate contrast enhanced CTAs without contrast agents. Twenty-six patients with paired non-contrast and CTA images were randomly selected from an approved clinical study. Non-contrast axial slices within the AAA from 10 patients (n = 100) were sampled for the underlying Hounsfield unit (HU) distribution at the lumen, intra-luminal thrombus and interface locations. Sampling of HUs in these regions revealed significant differences between all regions (p<0.001 for all comparisons), confirming the intrinsic differences in the radiomic signatures between these regions. To generate a large training dataset, paired axial slices from the training set (n=13) were augmented to produce a total of 23,551 2-D images. We trained a 2-D Cycle Generative Adversarial Network (cycleGAN) for this non-contrast to contrast (NC2C) transformation task. The accuracy of the cycleGAN output was assessed by comparison to the contrast image. This pipeline is able to differentiate between visually incoherent soft tissue regions in non-contrast CT images. The CTAs generated from the non-contrast images bear strong resemblance to the ground truth. Here we describe a novel application of Generative Adversarial Network for CT image processing. This is poised to disrupt clinical pathways requiring contrast enhanced CT imaging.