Abstract The research attempts to know the salinity level and how much agricultural land was affected by the 2004 Indian Ocean tsunami in the study area. Nagapattinam province of Tamilnadu, which was strongly hit by the 2004 Indian Ocean tsunami, is selected as the demonstration site. IKONOS and QuickBird image for the periods 2003 (pre-tsunami, IKONOS), 2004 (immediate tsunami, IKONOS) and 2006 (post-tsunami, QuickBird) have been used in this study. The purpose of this research is to detect the effects of the tsunami in the study area. Multispectral images were obtained in order to detect the salt-affected soils and compare the data with images after the tsunami. The near-infrared reflectance spectra contain significant information related to soil components. The development of remote sensing techniques can support the monitoring and efficient mapping of salinity level in soil for environmental assessment. In this research, we have implemented the techniques in four levels: image preprocessing, extracting land region, feature extraction and image classification. Image preprocessing is the first step in the image processing chain and is usually necessary prior to image classification and analysis. In the second level, land regions are extracted from the study area using the Soil-Adjusted Vegetation Indices. In the feature extraction level, we have extracted 15 salinity features from the image data and performed the feature selection method to select the best five features of soil. In the classification stage, the salt-affected land region is classified into three classes (C1, highly saline; C2, saline; and C3, non-saline) using the fuzzy logic method. In the immediate tsunami and after-tsunami data, 95.15- and 121.4-hectare area, respectively, were detected as a salt-affected area.

Abstract Understanding the statistical relations among the remote sensing data and observed water quality parameters is the necessary task to develop an estimator model. In order to estimate the exact water quality parameter distribution map of the study area from remote sensing data, this present study proposed an empirical mathematical relational and signature (EMRS) model. Through this EMRS model, an environmental pollution level analysis has been carried out for coastal area of Gulf of Mannar. EMRS highly facilitates to map the distribution of the water quality parameters. The insitu samples were involved with ICP‐OES and Physicochemical analysis and the test results were used for convergence of EMRS. The heavy metals such as Fe, Cu, Cd, Hg, and physicochemical parameters such as pH, dissolved oxygen (DO), biological oxygen demand (BOD), alkalinity, total suspended solids (TSS), turbidity and hardness are the experimented water quality parameters. The Landsat, Sentinal remote sensing data were used by the model to estimate the values and distribution and pollution level maps. The experimental analysis shows that the extracted element concentration using the proposed EMRS has highly correlated with the observed values based on the different measures such as R2 and root mean square error (RMSE).

Abstract In the study, a supervised learning framework is focused to identify the bipolar disorder (BD) using structural magnetic resonance imaging is focused. The work is based on the newly developed 3D SIFT and 3D SURF feature vectors with pattern recognition technique. The overall hypothesis is to deduct BD results from dysfunctional cellular metabolism within specific brain systems (i.e., anterior limbic brain network) as reflected in abnormalities in brain activation patterns and in specific neurochemical measures. The proposed method is used to integrate neuroimaging in exploring the biomarkers of BD to reveal the mechanism. In the method, two newly developed feature vectors and kernel PCA are combined or connected to project the feature vectors. Diagnosis process is done by random forest. The results reveal that the method has high potential to identify the BD than earlier works, and an average accuracy of 77.77% is reached. This research reveals that neuroimaging studies will help to differentiate BD from healthy controls.

Vocal fold, a significant body structure, is accountable for phonation, which regulates air motion within and out of the lungs. The disorders in the vocal fold influence the quality of life. Thus, diagnosis of vocal fold disorders has a significant need, and CT of the neck is employed for an effective imaging scheme. Accordingly, this paper proposes an advanced multi-resolution algorithm (MRA) that optimally identifies and classifies pathologies. The vocal regions are acquired using the genetic k-means algorithm. The pathology features are generated using the local directional pattern (LDP) fed to pathology classification using moth search-rider optimization-based deep convolutional neural networks (MRA-based DCNN). The hybrid optimization (MRA), integrates the standard rider optimization algorithm (ROA) and moth search algorithm (MS) that trains deep learning classifier (DCNN). The analysis using the real databases regarding the performance metrics divulge that the proposed pathology detection module obtained the accuracy, specificity, and sensitivity of 97.020%, 91.698%, and 96.624%.

An information retrieval system is proposed as an assistance tool for diagnosing the skin lesion using Content-Based Image Retrieval approach. Efficiency of the retrieval system is deliberated in terms of the most relevant retrieval of images from database. The proposed diagnostic assistive model retrieves the skin lesion images and its disease category, case history, symptoms and treatment plan. This retrieval process is made from a dermatology database by the way of visual features in the input image such as shape, texture and colour. The author’s proposed principal component analysis (PCA) feature projection technique is to discriminate the features by projecting them onto a feature subspace. While projecting the features onto a feature subspace features are normalised orthogonally. So the proposed methodology is used to improve the classification by the way of discriminate the features, in-turn it focus the retrieval of comprehensive reference sources, so that the diagnosis accuracy of the dermatologists are also improved. Receiver-operating characteristic curve is used to analyse the proposed computer-aided diagnosis (CAD) method, while analysis we attained high contribution to detect the skin lesions. Totally 1450 images are experimented and the system produced the 99.09% specificity, 96.69% sensitivity and 98.3% accuracy. When compared with other works this system of assessment shows high retrieval and diagnosis concert.

This paper deals with the identification of fractures in CT scan images. A large amount of significant and critical information are normally stored in medical data. Highly efficient and automated computational methods are needed to process and analyze all available data in order to help the physician in diagnosis, decisions and treatment planning. Each CT scan includes a large number of slices. In this paper, a new hierarchical segmentation algorithm is applied to all slices; it automatically extracts the bone structures using HMRF_EM segmentation method. The template-matching technique is employed to extract the affected portion. This new approach is experimented with eight patients' data and validated by radiologists. The performance of the work is analyzed and compared with recent works using sensitivity, specificity and accuracy.

Detecting brain structural changes from magnetic resonance (MR) images can facilitate early diagnosis and treatment of neurological and psychiatric diseases. Alzheimer Disease (AD) is a progressive neurodegenerative disorder that causes structural changes in patient’s brain. As such, it is essential to develop an algorithm for identifying the biomarkers of this disease stage. We developed a novel volumetric analysis of anatomical components of brain with multiclass particle swam optimisation technique (MPSO) approach to detect the stages of AD as potential biomarkers. To avoid image distortion bias correction is applied. We have used anatomical structures i.e. tissue and ventricle volume are used as criteria to categorise image features into four classes such as Alzheimer Mild cognitive decline, Alzheimer Moderate Cognitive decline and Alzheimer Severe Cognitive decline and healthy subject. This work was experimented with 30 AD and 10 normal cases. We observed that grey matter content was reduced from 4 to 20% of normal brain and volume of ventricle is increasing gradually from mild to severe cognitive decline. The statistical performance measures are calculated for proposed and existing work. The value shows that our empirical evaluation has superior diagnosis performance. We found that AD patient’s brain has reduced volume in grey matter and subsequently shrunk the volume of brain. The size of ventricle is also the major concern to predict the severity of AD disease. Therefore, the volumes of grey matter and ventricle size more discriminately classify the AD patient with severity from normal subject.

This study proposes a content‐based image retrieval system for skin lesion images as a diagnostic aid. Effectiveness is measured by the rate of correct retrieval of images from skin lesions. The proposed architecture is used to retrieve digital images and the name of the disease category from an image data repository by the contents in the image, such as shape, texture and colour that is extracted from the image. The author's proposed algorithm used feature vector, classification and regression tree to retrieve comprehensive reference sources for diagnostic purpose. The results proved using a receiver operating characteristic curve that the proposed architecture has high contribute to computer‐aided diagnosis of skin lesions. Experiments on a set of 1210 images yielded a specificity of 97.25% and a sensitivity of 91.24%. Their empirical evaluation has a superior retrieval and diagnosis performance when compared to the performance of other works. The authors present explicit combinations of feature vectors corresponding to healthy and lesion skin.

Nucleus segmentation is one of important steps in the automatic white blood cell differential counting. In this paper, we proposed a technique to segment images of the nucleus. We analyze a set of white-blood-cell-nucleus-based features using color fuzzy texture spectrum (Base 5). We applied artificial neural network for classification. We compared the results with moment based features. The classification performances are evaluated by class wise classification rates. The results show that the features using nucleus alone could be utilized to achieve a classification rate of 99.05% on the test sets.