Studies on the littoral processes in relation to stability of beaches around Cchin

The coastal and nearshore areas have played vital role in the trade and economic development of coastal nations since ancient times. In recent years, the demands for utilization of these areas have increased for purposes of navigation, setting up of offshore structures for oil industry, exploitation of the available fishery and mineral resources, and to provide recreational facilities along the coast as a part of the coastal zone management. It is in this context the studies on nearshore processes receive greater priorities. Stability of beaches is controlled by the interaction of various physical parameters such as winds, waves, currents, tides and the nature and constituents of the beaches. The results of studies carried out by the author on the dynamical effects of these environmental parameters on the shoreline processes along the beaches around Cochin are presented in this thesis. The section of the coast investigated is about 57 km of shore from Azhikode to Anthakaranazhi situated on the central Kerala coast. Four regions namely Narakkal, Malipuram, Fort Cochin and Anthakaranazhi were chosen for detailed study

Studies On Sediment Transport In The Surf Zone Along Certain Beaches Of Kerala

Sediment transport in the nearshore areas is an important process in deciding the coastline stability. The design and effective maintenance of navigable waterways, harbours and marine structures depend on the stability of the sediment substrate and the nature of sedimentation in the nearshore zone. The nearshore zone is a complex environment and the exact relationships existing between water motions and the resulting sediment transports are not well understood. During the rough weather season, when the sediment movement is considerable, processes occurring in the nearshore area are much less understood. Moreover, there is a general lack of field measurements, especially during the time of severe storm conditions. The increasing pressures and the concern on the preservation of the valuable coastal environment have led to the development of shore protection programmes. Conservation not only demands knowledge of what needs to be done, but also requires the basic processes to be fully understood. Considering the fragile nature of barrier beaches and intense occupancy of these areas by man, these coastal features have long been a subject of study by coastal oceanographers, geomorphologists and engineers. The present study is an attempt to understand the sediment movement in relation to beach dynamics, especially in the surf zone, along some part of Kerala coast and the response of the beaches to various forcing functions over different seasons

“Improved Feature Extraction and Classification Techniques for Multispectral Brain Magnetic Resonance Images”

Magnetic Resonance Imaging (MRI) is a multi sequence medical imaging technique in which stacks of images are acquired with different tissue contrasts. Simultaneous observation and quantitative analysis of normal brain tissues and small abnormalities from these large numbers of different sequences is a great challenge in clinical applications. Multispectral MRI analysis can simplify the job considerably by combining unlimited number of available co-registered sequences in a single suite. However, poor performance of the multispectral system with conventional image classification and segmentation methods makes it inappropriate for clinical analysis. Recent works in multispectral brain MRI analysis attempted to resolve this issue by improved feature extraction approaches, such as transform based methods, fuzzy approaches, algebraic techniques and so forth. Transform based feature extraction methods like Independent Component Analysis (ICA) and its extensions have been effectively used in recent studies to improve the performance of multispectral brain MRI analysis. However, these global transforms were found to be inefficient and inconsistent in identifying less frequently occurred features like small lesions, from large amount of MR data. The present thesis focuses on the improvement in ICA based feature extraction techniques to enhance the performance of multispectral brain MRI analysis. Methods using spectral clustering and wavelet transforms are proposed to resolve the inefficiency of ICA in identifying small abnormalities, and problems due to ICA over-completeness. Effectiveness of the new methods in brain tissue classification and segmentation is confirmed by a detailed quantitative and qualitative analysis with synthetic and clinical, normal and abnormal, data. In comparison to conventional classification techniques, proposed algorithms provide better performance in classification of normal brain tissues and significant small abnormalities.

classification and content based retrieval of digital mammograms and placental sonograms

Image processing has been a challenging and multidisciplinary research area since decades with continuing improvements in its various branches especially Medical Imaging. The healthcare industry was very much benefited with the advances in Image Processing techniques for the efficient management of large volumes of clinical data. The popularity and growth of Image Processing field attracts researchers from many disciplines including Computer Science and Medical Science due to its applicability to the real world. In the meantime, Computer Science is becoming an important driving force for the further development of Medical Sciences. The objective of this study is to make use of the basic concepts in Medical Image Processing and develop methods and tools for clinicians’ assistance. This work is motivated from clinical applications of digital mammograms and placental sonograms, and uses real medical images for proposing a method intended to assist radiologists in the diagnostic process. The study consists of two domains of Pattern recognition, Classification and Content Based Retrieval. Mammogram images of breast cancer patients and placental images are used for this study. Cancer is a disaster to human race. The accuracy in characterizing images using simplified user friendly Computer Aided Diagnosis techniques helps radiologists in detecting cancers at an early stage. Breast cancer which accounts for the major cause of cancer death in women can be fully cured if detected at an early stage. Studies relating to placental characteristics and abnormalities are important in foetal monitoring. The diagnostic variability in sonographic examination of placenta can be overlooked by detailed placental texture analysis by focusing on placental grading. The work aims on early breast cancer detection and placental maturity analysis. This dissertation is a stepping stone in combing various application domains of healthcare and technology.

FEATURE SELECTION AND COMPARISON OF TWO NAÏVE BAYES CLASSIFICATION METHODS IN THE CONTEXT OF SPAM FILTERING

Treating e-mail filtering as a binary text classification problem, researchers have applied several statistical learning algorithms to email corpora with promising results. This paper examines the performance of a Naive Bayes classifier using different approaches to feature selection and tokenization on different email corpora

Underwater Target Localization, Tracking and Classification

Underwater target localization and tracking attracts tremendous research interest due to various impediments to the estimation task caused by the noisy ocean environment. This thesis envisages the implementation of a prototype automated system for underwater target localization, tracking and classification using passive listening buoy systems and target identification techniques. An autonomous three buoy system has been developed and field trials have been conducted successfully. Inaccuracies in the localization results, due to changes in the environmental parameters, measurement errors and theoretical approximations are refined using the Kalman filter approach. Simulation studies have been conducted for the tracking of targets with different scenarios even under maneuvering situations. This system can as well be used for classifying the unknown targets by extracting the features of the noise emanations from the targets.

A Classification of Sandhi Rules for Suffix Separation in Malayalam

Suffix separation plays a vital role in improving the quality of training in the Statistical Machine Translation from English into Malayalam. The morphological richness and the agglutinative nature of Malayalam make it necessary to retrieve the root word from its inflected form in the training process. The suffix separation process accomplishes this task by scrutinizing the Malayalam words and by applying sandhi rules. In this paper, various handcrafted rules designed for the suffix separation process in the English Malayalam SMT are presented. A classification of these rules is done based on the Malayalam syllable preceding the suffix in the inflected form of the word (check_letter). The suffixes beginning with the vowel sounds like ആല, ഉെെ, ഇല etc are mainly considered in this process. By examining the check_letter in a word, the suffix separation rules can be directly applied to extract the root words. The quick look up table provided in this paper can be used as a guideline in implementing suffix separation in Malayalam language

Significance Of Classification Techniques In Prediction Of Learning Disabilities

The aim of this study is to show the importance of two classification techniques, viz. decision tree and clustering, in prediction of learning disabilities (LD) of school-age children. LDs affect about 10 percent of all children enrolled in schools. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Decision trees and clustering are powerful and popular tools used for classification and prediction in Data mining. Different rules extracted from the decision tree are used for prediction of learning disabilities. Clustering is the assignment of a set of observations into subsets, called clusters, which are useful in finding the different signs and symptoms (attributes) present in the LD affected child. In this paper, J48 algorithm is used for constructing the decision tree and K-means algorithm is used for creating the clusters. By applying these classification techniques, LD in any child can be identified

Spectral clustering independent component analysis for tissue classification from brain MRI

A spectral angle based feature extraction method, Spectral Clustering Independent Component Analysis (SC-ICA), is proposed in this work to improve the brain tissue classification from Magnetic Resonance Images (MRI). SC-ICA provides equal priority to global and local features; thereby it tries to resolve the inefficiency of conventional approaches in abnormal tissue extraction. First, input multispectral MRI is divided into different clusters by a spectral distance based clustering. Then, Independent Component Analysis (ICA) is applied on the clustered data, in conjunction with Support Vector Machines (SVM) for brain tissue analysis. Normal and abnormal datasets, consisting of real and synthetic T1-weighted, T2-weighted and proton density/fluid-attenuated inversion recovery images, were used to evaluate the performance of the new method. Comparative analysis with ICA based SVM and other conventional classifiers established the stability and efficiency of SC-ICA based classification, especially in reproduction of small abnormalities. Clinical abnormal case analysis demonstrated it through the highest Tanimoto Index/accuracy values, 0.75/98.8%, observed against ICA based SVM results, 0.17/96.1%, for reproduced lesions. Experimental results recommend the proposed method as a promising approach in clinical and pathological studies of brain diseases

Brain Tissue Classification from Multispectral MRI by Wavelet based Principal Component Analysis

In this paper, we propose a multispectral analysis system using wavelet based Principal Component Analysis (PCA), to improve the brain tissue classification from MRI images. Global transforms like PCA often neglects significant small abnormality details, while dealing with a massive amount of multispectral data. In order to resolve this issue, input dataset is expanded by detail coefficients from multisignal wavelet analysis. Then, PCA is applied on the new dataset to perform feature analysis. Finally, an unsupervised classification with Fuzzy C-Means clustering algorithm is used to measure the improvement in reproducibility and accuracy of the results. A detailed comparative analysis of classified tissues with those from conventional PCA is also carried out. Proposed method yielded good improvement in classification of small abnormalities with high sensitivity/accuracy values, 98.9/98.3, for clinical analysis. Experimental results from synthetic and clinical data recommend the new method as a promising approach in brain tissue analysis.

Wavelet based Independent Component Analysis for Multispectral Brain Tissue Classification

Multispectral analysis is a promising approach in tissue classification and abnormality detection from Magnetic Resonance (MR) images. But instability in accuracy and reproducibility of the classification results from conventional techniques keeps it far from clinical applications. Recent studies proposed Independent Component Analysis (ICA) as an effective method for source signals separation from multispectral MR data. However, it often fails to extract the local features like small abnormalities, especially from dependent real data. A multisignal wavelet analysis prior to ICA is proposed in this work to resolve these issues. Best de-correlated detail coefficients are combined with input images to give better classification results. Performance improvement of the proposed method over conventional ICA is effectively demonstrated by segmentation and classification using k-means clustering. Experimental results from synthetic and real data strongly confirm the positive effect of the new method with an improved Tanimoto index/Sensitivity values, 0.884/93.605, for reproduced small white matter lesions

Towards the development of a new wavelet for ECG classification

In this paper an attempt has been made to determine the number of Premature Ventricular Contraction (PVC) cycles accurately from a given Electrocardiogram (ECG) using a wavelet constructed from multiple Gaussian functions. It is difficult to assess the ECGs of patients who are continuously monitored over a long period of time. Hence the proposed method of classification will be helpful to doctors to determine the severity of PVC in a patient. Principal Component Analysis (PCA) and a simple classifier have been used in addition to the specially developed wavelet transform. The proposed wavelet has been designed using multiple Gaussian functions which when summed up looks similar to that of a normal ECG. The number of Gaussians used depends on the number of peaks present in a normal ECG. The developed wavelet satisfied all the properties of a traditional continuous wavelet. The new wavelet was optimized using genetic algorithm (GA). ECG records from Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) database have been used for validation. Out of the 8694 ECG cycles used for evaluation, the classification algorithm responded with an accuracy of 97.77%. In order to compare the performance of the new wavelet, classification was also performed using the standard wavelets like morlet, meyer, bior3.9, db5, db3, sym3 and haar. The new wavelet outperforms the rest

Classification of Mammograms and DW T based Detection of Microcalcification

Cancer treatment is most effective when it is detected early and the progress in treatment will be closely related to the ability to reduce the proportion of misses in the cancer detection task. The effectiveness of algorithms for detecting cancers can be greatly increased if these algorithms work synergistically with those for characterizing normal mammograms. This research work combines computerized image analysis techniques and neural networks to separate out some fraction of the normal mammograms with extremely high reliability, based on normal tissue identification and removal. The presence of clustered microcalcifications is one of the most important and sometimes the only sign of cancer on a mammogram. 60% to 70% of non-palpable breast carcinoma demonstrates microcalcifications on mammograms [44], [45], [46].WT based techniques are applied on the remaining mammograms, those are obviously abnormal, to detect possible microcalcifications. The goal of this work is to improve the detection performance and throughput of screening-mammography, thus providing a ‘second opinion ‘ to the radiologists. The state-of- the- art DWT computation algorithms are not suitable for practical applications with memory and delay constraints, as it is not a block transfonn. Hence in this work, the development of a Block DWT (BDWT) computational structure having low processing memory requirement has also been taken up.

Automatic Detection and Classification of Glioma Tumors using Statistical Features

The characterization and grading of glioma tumors, via image derived features, for diagnosis, prognosis, and treatment response has been an active research area in medical image computing. This paper presents a novel method for automatic detection and classification of glioma from conventional T2 weighted MR images. Automatic detection of the tumor was established using newly developed method called Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA).Statistical Features were extracted from the detected tumor texture using first order statistics and gray level co-occurrence matrix (GLCM) based second order statistical methods. Statistical significance of the features was determined by t-test and its corresponding p-value. A decision system was developed for the grade detection of glioma using these selected features and its p-value. The detection performance of the decision system was validated using the receiver operating characteristic (ROC) curve. The diagnosis and grading of glioma using this non-invasive method can contribute promising results in medical image computing

Sedimentology and Geochemistry of core sediments from the Ashtamudi estuary and the adjoining coastal plain,central Kerala,India

The evolution of coast through geological time scale is dependent on the transgression-regression event subsequent to the rise or fall of sea level. This event is accounted by investigation of the vertical sediment deposition patterns and their interrelationship for paleo-enviornmental reconstruction. Different methods like sedimentological (grain size and micro-morphological) and geochemical (elemental relationship) analyses as well as radiocarbon dating are generally used to decipher the sea level changes and paleoclimatic conditions of the Quaternary sediment sequence. For the Indian coast with a coastline length of about 7500 km, studies on geological and geomorphological signatures of sea level changes during the Quaternary were reported in general by researchers during the last two decades. However, for the southwest coast of India particularily Kerala which is famous for its coastal landforms comprising of estuaries, lagoons, backwaters, coastal plains, cliffs and barrier beaches, studies pertaining to the marine transgression-regression events in the southern region are limited. The Neendakara-Kayamkulam coastal stretch in central Kerala where the coast is manifested with shore parallel Kayamkulam Lagoon on one side and shore perpendicular Ashtamudi Estuary on the other side indicating existence of an uplifted prograded coastal margin followed by barrier beaches, backwater channels, ridge and runnel topography is an ideal site for studying such events. Hence the present study has been taken up in this context to address the gap area. The location for collection of core samples representing coastal plain, estuarylagoon and offshore regions have been identified based on published literature and available sedimentary records. The objectives of the research work are:  To study the lithological variations and depositional environments of sediment cores along the coastal plain, estuary-lagoon and offshore regions between Kollam and Kayamkulam in the central Kerala coast  To study the transportation and diagenetic history of sediments in the area  To investigate the geochemical characterization of sediments and to elucidate the source-sink relationship  To understand the marine transgression-regression events and to propose a conceptual model for the region The thesis comprises of 8 chapters. The first chapter embodies the preamble for the selection and significance of this research work. The study area is introduced with details on its physiographical, geological, geomorphological, rainfall and climate information. A review of literature, compiling the research on different aspects such as physico-chemical, geomorphological, tectonics, transgression-regression events are presented in the second chapter and they are broadly classified into three viz:- International, National and Kerala. The field data collection and laboratory analyses adopted in the research work are discussed in the third chapter. For collection of sediment core samples from the coastal plains, rotary drilling method was employed whereas for the estuary-lagoon and offshore locations the gravity/piston corer method was adopted. The collected subsurficial samples were analysed for texture, surface micro-texture, elemental analysis, XRD and radiocarbon dating techniques for age determination. The fourth chapter deals with the textural analysis of the core samples collected from various predefined locations of the study area. The result reveals that the Ashtamudi Estuary is composed of silty clay to clayey type of sediments whereas offshore cores are carpeted with silty clay to relict sand. Investigation of the source of sediments deposited in the coastal plain located on either side of the estuary indicates the dominance of terrigenous to marine origin in the southern region whereas it is predominantly of marine origin towards the north. Further the hydrodynamic conditions as well as the depositional enviornment of the sediment cores are elucidated based on statistical parameters that decipher the deposition pattern at various locations viz., coastal plain (open to closed basin), Ashtamudi Estuary (partially open to restricted estuary to closed basin) and offshore (open channel). The intensity of clay minerals is also discussed. From the results of radiocarbon dating the sediment depositional environments were deciphered.The results of the microtextural study of sediment samples (quartz grains) using Scanning Electron Microscope (SEM) are presented in the fifth chapter. These results throw light on the processes of transport and diagenetic history of the detrital sediments. Based on the lithological variations, selected quartz grains of different environments were also analysed. The study indicates that the southern coastal plain sediments were transported and deposited mechanically under fluvial environment followed by diagenesis under prolonged marine incursion. But in the case of the northern coastal plain, the sediments were transported and deposited under littoral environment indicating the dominance of marine incursion through mechanical as well as chemical processes. The quartz grains of the Ashtamudi Estuary indicate fluvial origin. The surface texture features of the offshore sediments suggest that the quartz grains are of littoral origin and represent the relict beach deposits. The geochemical characterisation of sediment cores based on geochemical classification, sediment maturity, palaeo-weathering and provenance in different environments are discussed in the sixth chapter. In the seventh chapter the integration of multiproxies data along with radiocarbon dates are presented and finally evolution and depositional history based on transgression–regression events is deciphered. The eighth chapter summarizes the major findings and conclusions of the study with recommendation for future work.