Computer-aided segmentation and estimation of indices in brain CT scans

The importance of neuro-imaging as one of the biomarkers for diagnosis and prognosis of pathologies and traumatic cases is well established. Doctors routinely perform linear measurements on neuro-images to ascertain severity and extent of the pathology or trauma from significant anatomical changes. However, it is a tedious and time consuming process and manually assessing and reporting on large volume of data is fraught with errors and variation. In this paper we present a novel technique for segmentation of significant anatomical landmarks using artificial neural networks and estimation of various ratios and indices performed on brain CT scans. The proposed method is efficient and robust in detecting and measuring sizes of anatomical structures on non-contrast CT scans and has been evaluated on images from subjects with ages between 5 to 85 years. Results show that our method has average ICC of ≥0.97 and, hence, can be used in processing data for further use in research and clinical environment

Computer aided assessment of CT scans of traumatic brain injury patients

A thesis submitted in partial fulfilment for the degree of Doctor of PhilosophyOne of the serious public health problems is the Traumatic Brain Injury, also known as silent epidemic, affecting millions every year. Management of these patients essentially involves neuroimaging and noncontrast CT scans are the first choice amongst doctors. Significant anatomical changes identified on the neuroimages and volumetric assessment of haemorrhages and haematomas are of critical importance for assessing the patients’ condition for targeted therapeutic and/or surgical interventions. Manual demarcation and annotation by experts is still considered gold standard, however, the interpretation of neuroimages is fraught with inter-observer variability and is considered ’Achilles heel’ amongst radiologists. Errors and variability can be attributed to factors such as poor perception, inaccurate deduction, incomplete knowledge or the quality of the image and only a third of doctors confidently report the findings. The applicability of computer aided dianosis in segmenting the apposite regions and giving ’second opinion’ has been positively appraised to assist the radiologists, however, results of the approaches vary due to parameters of algorithms and manual intervention required from doctors and this presents a gap for automated segmentation and estimation of measurements of noncontrast brain CT scans. The Pattern Driven, Content Aware Active Contours (PDCAAC) Framework developed in this thesis provides robust and efficient segmentation of significant anatomical landmarks, estimations of their sizes and correlation to CT rating to assist the radiologists in establishing the diagnosis and prognosis more confidently. The integration of clinical profile of the patient into image segmentation algorithms has significantly improved their performance by highlighting characteristics of the region of interest. The modified active contour method in the PDCAAC framework achieves Jaccard Similarity Index (JI) of 0.87, which is a significant improvement over the existing methods of active contours achieving JI of 0.807 with Simple Linear Iterative Clustering and Distance Regularized Level Set Evolution. The Intraclass Correlation Coefficient of intracranial measurements is >0.97 compared with radiologists. Automatic seeding of the initial seed curve within the region of interest is incorporated into the method which is a novel approach and alleviates limitation of existing methods. The proposed PDCAAC framework can be construed as a contribution towards research to formulate correlations between image features and clinical variables encompassing normal development, ageing, pathological and traumatic cases propitious to improve management of such patients. Establishing prognosis usually entails survival but the focus can also be extended to functional outcomes, residual disability and quality of life issues

Cognitive Storage Model And Mapping With Classical Data Structures

Memories are the internal mental records that we maintain .Human mind is a very complex organ.. Processing depends on how we memorize information, events and how we recall things and use them efficiently in situations when required. It can be related that for Storage in mind we use different data structures for storing variety of information. We remember the names of known persons, and the people we met more frequently.The Topics in book, Months of the year, our CNIC Number, the way we learn words of a new language etc. Recently invented data structures e.g skiplist [1] show much similarity of how the brain store the information. So we can say Careful study of how the cognitive storage works could lead to the discovery of the new data structures In this paper we have attempted to relate the existing data structures with how we store information in mind

Evaluation of Classification Algorithms for Intrusion Detection System: A Review

Intrusion detection is one of the most critical network security problems in the technology world. Machine learning techniques are being implemented to improve the Intrusion Detection System (IDS). In order to enhance the performance of IDS, different classification algorithms are applied to detect various types of attacks. Choosing a suitable classification algorithm for building IDS is not an easy task. The best method is to test the performance of the different classification algorithms. This paper aims to present the result of evaluating different classification algorithms to build an IDS model in terms of confusion matrix, accuracy, recall, precision, f-score, specificity and sensitivity. Nevertheless, most researchers have focused on the confusion matrix and accuracy metric as measurements of classification performance. It also provides a detailed comparison with the dataset, data preprocessing, number of features selected, feature selection technique, classification algorithms, and evaluation performance of algorithms described in the intrusion detection system

Tropical Indian Ocean Mediates ENSO Influence Over Central Southwest Asia During the Wet Season

AbstractEl Niño–Southern Oscillation (ENSO) modulates wet season (November–April) precipitation over Central Southwest Asia (CSWA), however, intraseasonal characteristics of its influence are largely unknown, which can be important for its subseasonal to seasonal hydroclimate predictability. Here we show that the ENSO‐CSWA teleconnection varies intraseasonally and is a combination of direct and indirect positive influences. The direct influence is through a Rossby wave‐like pattern in the tail months. The indirect influence is through an atmospheric dipole of diabatic heating anomalies in the tropical Indian Ocean (TIO) as a result of ENSO‐forced response, which also generates a Rossby wave‐like forcing and persists throughout the wet season. ENSO exerts its strongest influence when both direct and indirect modes are in phase, while the relationship breaks down when the two modes are out of phase. The atmospheric teleconnection through the atmospheric diabatic heating anomalies in the TIO is reproducible in numerical simulations

A review on the application of remote sensing and geographic information system in flood crisis management

Flood is considered as one of the most devastating hazards around the globe and emerged as an important issue among all the stakeholder to manage. Every year when the flood occurs, it has a terrible impact on human lives and demolishes billions of dollars property and infrastructure as well. The flood catastrophe and its losses can be reduced and prevented by flood inundation maps which provides a reliable and accurate information to the public. The principle objective of this paper is to review the application of Geographical information system (GIS) and technology of Remote sensing (RS) in geospatial skills and expertise in sciences, the integration and utilization of spatial and information technology effectively and more prominence is on using non-structure approaches based on remote sensing and geographic information system in flood crisis management. The advantages of solving complex logistics operations, accuracy with high speed which provides a reliable change, improved communication, monitoring capability, modeling, estimation of flood risk, promoted a cost saving mechanism with greater efficiency/friendly adaptability with the environment of theses digitize systems purposes to using more and more spatial application in flood crisis management. Geospatial information and remote sensing utilization serves as bridge between the flooding security measures and early prediction system. The paper encompasses the advantages of RS & GIS which acts as a tool in monitoring and improving before, during and after the flood crisis management in Malaysia

Agriculture Value Added and Poverty Reduction in Pakistan: An Econometric Analysis

Agriculture plays an important role to reduce poverty in developing countries. This study was conducted with the core objective to examine the role of agriculture in poverty reduction in Pakistan using time series data for the period 1972-2013. This study also analyses the role of services and industrial sectors to mitigate poverty in Pakistan. The study has applied Augmented Dickey-Fuller test to examine the data for stationary. On the basis of ADF test all the variables are stationary at first difference i.e. I (1). Johansen Co-integration test was also applied to assess the long-run relation between the variables. There are two co-integrating vectors. So the results show that all the sectors---agriculture, services and industrial---have long-run relation with poverty reduction. Results of error correction model confirm the long-run relation of agriculture, services and industrial sectors with poverty reduction. Keywords: Agriculture, Poverty Reduction, co-integration, VECM, Pakistan

Image steganography using least significant bit and secret map techniques

In steganography, secret data are invisible in cover media, such as text, audio, video and image. Hence, attackers have no knowledge of the original message contained in the media or which algorithm is used to embed or extract such message. Image steganography is a branch of steganography in which secret data are hidden in host images. In this study, image steganography using least significant bit and secret map techniques is performed by applying 3D chaotic maps, namely, 3D Chebyshev and 3D logistic maps, to obtain high security. This technique is based on the concept of performing random insertion and selecting a pixel from a host image. The proposed algorithm is comprehensively evaluated on the basis of different criteria, such as correlation coefficient, information entropy, homogeneity, contrast, image, histogram, key sensitivity, hiding capacity, quality index, mean square error (MSE), peak signal-to-noise ratio (PSNR) and image fidelity. Results show that the proposed algorithm satisfies all the aforementioned criteria and is superior to other previous methods. Hence, it is efficient in hiding secret data and preserving the good visual quality of stego images. The proposed algorithm is resistant to different attacks, such as differential and statistical attacks, and yields good results in terms of key sensitivity, hiding capacity, quality index, MSE, PSNR and image fidelity