Texture Mineralogy and Geochemistry Of Sediments From The Coastal Plains Between Kodungallur And Chellanam Central Kerala India

The present study addresses to understand the sedimentological properties of the coasts of kodungallur and chellanam, central Kerala to bring out the relationship between the textural, mineralogical and geochemical characters with that of the respective environment. The grain size study of the beach ridge sediments from different pits has been investigated at close intervals, which enables to understand the grain size variations with depth. The sediment samples from various pits of the beach ridges indicate that the sediments range primarily from medium to very fine sand, well to moderately sorted, fine to coarse skewed and leptokurtic to platykurtic. The study area is considered as a prograding coast. Variations in grain size down the pit give three phases of beach building activities i.e.; a coarsening upward sequence in the bottom layers, a fining upward in the middle and coarsening upward in the top. Beach ridges are formed by swash built sediments with cross bedding and setting lag type sediments with seaward dipping/horizontal units. Geochemical signatures in the study area have been brought out through the analysis of major and trace elements. Iron is significantly enriched and its control over many trace elements is evident. Copper, chromium, cobalt, lithium, lead and zinc show decreasing trend with depth, while sodium, potassium,strontium,nickel and organic carbon increases. The association of many trace elements with organic carbon has also been established. Dissolution of trace elements in anoxic environment, at depth and reprecipitation in the oxic layers, at near or subsurface, are the major mechanism that brought out the variation of certain environmentally sensitive elements

Texture, composition and provinance of innershelf sediments between Narakkal and Purakkad, Kerala with special reference to the formation of Mud Banks

The continental shelf of southwest coast of India (Kerala) is broader and . flatter compared to that of the east coast. The unique characteristic feature of the study area (innershelf between Narakkal and Purakkad) is the intermittent appearance of 'mud banks' at certain locations during southwest monsoon. The strong seasonality manifests significant changes in the wind, waves, currents, rainfall, drainage etc., along this area. Peculiar geomorphological variation with high, mid and lowlands in the narrow strip of the hinterland, the geological formations mainly consisting of rocks of metamorphic origin and the humid tropical weathering conditions play significant role in regulating the shelf sedimentation. A complementary pattern of distri bution is observed for clay that shows an abundance in the nearshore. Silt, to a major extent, depicts semblance with clay distribution . Summation of the total asymmetry of grain size distribution are inferred from the variation of skewness and kurtosis.Factor I implies a low energy regime where the transportation and deposition phases are controlled mostly by pelagic suspension process as the factor loadings are dominant on finer phi sizes. The second Factor is inferred to be the result of a high energy regime which gives higher loadings on coarser size fractions. The third Factor which might be a transition phase (medium energy regime) representing the resultant flux of coastal circulation of the re-suspension/deposition and an onshoreoffshore advection by reworking and co-deposition of relict and modern sediments. The spatial variations of the energy regime based on the three end-member factor model exhibits high energy zone in the seaward portion transcending to a low energy one towards the coast.From the combined analysis of granulometry and SEM studies, it is concluded that the sandy patches beyond 20 m depth are of relict nature. They are the resultant responses of beach activity during the lower stand of sea level in the Holocene. Re-crystallisation features on the quartz grains indicate that they were exposed to subaerial weathering process subsequent to thei r deposition

Influence of collagen content on the texture andstorage quality of fish mince basedproducts

Objectives of the present study are to find out the proximate composition of 20 commercially important tropical fish species on the west coast of India. To determine the collagen content in these commercially important fish species and fractionation of collagen into acid soluble collagen (ASC) and hot water soluble (insoluble) collagen (ISC). To classify fishes according to its collagen content and To study the different storage characteristics in the mince based product—surimi, from different species of fishes. The researcher tries to find out a suitable collagen source to incorporate in surimi. and studies the different storage qualities in the mince based product, surimi at different levels of collagen in different species of fishes. The optimum collagen level to get desirable texture and storage quality for mince based product. The researcher aims to develop some products from surimi with desirable level of collagen. And compare the products prepared from surimi of lesser collagen content fish containing desirable level of collagen with surimi prepared with high collagen content fish without collagen. This study gains in importance as there is littleinformation on the collagen content of different species of fishes in India. So far no attempt was made to classify fishes according to its collagen content.

Market Segmentation by Financial lnstitution

The research problem selected for this study is one of the important issues in the field of financial market and its marketing dimensions on which researchers and academicians encourage more research studies. This research study may be relevant considering its significance in terms of some possible findings which may be useful to Fls in framing successful market segmentation approach to turn their dissatisfied and ‘merely' satisfied customers into ‘delighted’ customers, which in turn can result in better savings mobilisation. The household segments may also be benefited from the research findings if they bring about an attitudinal change in their savings behaviour. The importance of the study may be briefly highlighted in the following points. The research study examines existing theories on market segmentation by Fls and the findings might supplement the existing theories on this topic. The study brings to light certain clues to strengthen market segmentation approach of Fls.The study throws light on the existing beliefs and perceptions on customer behaviour which may be useful in effecting some positive changes in market segmentation approach by Fls. The study suggests certain relationship between market segmentation variables and customer behaviour in the context of marketing of financial products by Fls. The study supplements the existing knowledge on different dimension of market segmentation in the financial market which might encourage future research in the field.

Document Image Segmentation and Compression using Artificial Neural Networks and Evolutionary Methods

The work is intended to study the following important aspects of document image processing and develop new methods. (1) Segmentation ofdocument images using adaptive interval valued neuro-fuzzy method. (2) Improving the segmentation procedure using Simulated Annealing technique. (3) Development of optimized compression algorithms using Genetic Algorithm and parallel Genetic Algorithm (4) Feature extraction of document images (5) Development of IV fuzzy rules. This work also helps for feature extraction and foreground and background identification. The proposed work incorporates Evolutionary and hybrid methods for segmentation and compression of document images. A study of different neural networks used in image processing, the study of developments in the area of fuzzy logic etc is carried out in this work

Development of Techniques for the Automatic Extraction and Grade Detection of Glioma Tumors from Conventional Brain Magnetic Resonant Images

Cerebral glioma is the most prevalent primary brain tumor, which are classified broadly into low and high grades according to the degree of malignancy. High grade gliomas are highly malignant which possess a poor prognosis, and the patients survive less than eighteen months after diagnosis. Low grade gliomas are slow growing, least malignant and has better response to therapy. To date, histological grading is used as the standard technique for diagnosis, treatment planning and survival prediction. The main objective of this thesis is to propose novel methods for automatic extraction of low and high grade glioma and other brain tissues, grade detection techniques for glioma using conventional magnetic resonance imaging (MRI) modalities and 3D modelling of glioma from segmented tumor slices in order to assess the growth rate of tumors. Two new methods are developed for extracting tumor regions, of which the second method, named as Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA) can also extract white matter and grey matter from T1 FLAIR an T2 weighted images. The methods were validated with manual Ground truth images, which showed promising results. The developed methods were compared with widely used Fuzzy c-means clustering technique and the robustness of the algorithm with respect to noise is also checked for different noise levels. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and detection. Based on the thresholds of discriminant first order and gray level cooccurrence matrix based second order statistical features three feature sets were formulated and a decision system was developed for grade detection of glioma from conventional T2 weighted MRI modality.The quantitative performance analysis using ROC curve showed 99.03% accuracy for distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant glioma. The developed brain texture analysis techniques can improve the physician’s ability to detect and analyse pathologies leading to a more reliable diagnosis and treatment of disease. The segmented tumors were also used for volumetric modelling of tumors which can provide an idea of the growth rate of tumor; this can be used for assessing response to therapy and patient prognosis.

Motion Segmentation And Meanshift Assisted Contour Refinement For Airborne Video

This paper presents methods for moving object detection in airborne video surveillance. The motion segmentation in the above scenario is usually difficult because of small size of the object, motion of camera, and inconsistency in detected object shape etc. Here we present a motion segmentation system for moving camera video, based on background subtraction. An adaptive background building is used to take advantage of creation of background based on most recent frame. Our proposed system suggests CPU efficient alternative for conventional batch processing based background subtraction systems. We further refine the segmented motion by meanshift based mode association.

Automatic Segmentation Framework for Primary Tumors From Brain MRIs Using Morphological Filtering Techniques

This paper describes a novel framework for automatic segmentation of primary tumors and its boundary from brain MRIs using morphological filtering techniques. This method uses T2 weighted and T1 FLAIR images. This approach is very simple, more accurate and less time consuming than existing methods. This method is tested by fifty patients of different tumor types, shapes, image intensities, sizes and produced better results. The results were validated with ground truth images by the radiologist. Segmentation of the tumor and boundary detection is important because it can be used for surgical planning, treatment planning, textural analysis, 3-Dimensional modeling and volumetric analysis

A semi-automatic method for segmentation and 3D modeling of glioma tumors from brain MRI

This work presents an efficient method for volume rendering of glioma tumors from segmented 2D MRI Datasets with user interactive control, by replacing manual segmentation required in the state of art methods. The most common primary brain tumors are gliomas, evolving from the cerebral supportive cells. For clinical follow-up, the evaluation of the pre- operative tumor volume is essential. Tumor portions were automatically segmented from 2D MR images using morphological filtering techniques. These seg- mented tumor slices were propagated and modeled with the software package. The 3D modeled tumor consists of gray level values of the original image with exact tumor boundary. Axial slices of FLAIR and T2 weighted images were used for extracting tumors. Volumetric assessment of tumor volume with manual segmentation of its outlines is a time-consuming proc- ess and is prone to error. These defects are overcome in this method. Authors verified the performance of our method on several sets of MRI scans. The 3D modeling was also done using segmented 2D slices with the help of a medical software package called 3D DOCTOR for verification purposes. The results were validated with the ground truth models by the Radi- ologist.

Texture Description of low grade and high grade Glioma using Statistical features in Brain MRIs

Low grade and High grade Gliomas are tumors that originate in the glial cells. The main challenge in brain tumor diagnosis is whether a tumor is benign or malignant, primary or metastatic and low or high grade. Based on the patient's MRI, a radiologist could not differentiate whether it is a low grade Glioma or a high grade Glioma. Because both of these are almost visually similar, autopsy confirms the diagnosis of low grade with high-grade and infiltrative features. In this paper, textural description of Grade I and grade III Glioma are extracted using First order statistics and Gray Level Co-occurance Matrix Method (GLCM). Textural features are extracted from 16X16 sub image of the segmented Region of Interest(ROI) .In the proposed method, first order statistical features such as contrast, Intensity , Entropy, Kurtosis and spectral energy and GLCM features extracted were showed promising results. The ranges of these first order statistics and GLCM based features extracted are highly discriminant between grade I and Grade III. In this study which gives statistical textural information of grade I and grade III Glioma which is very useful for further classification and analysis and thus assisting Radiologist in greater extent.

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

Tourist Profiles and Characteristics vis-à-vis Market Segmentation of Ecotourism Destinations in Kerala

Kerala, a classic ecotourism destination in India, provides significant opportunities for livelihood options to the people who depend on the resources from the forest and those who live in difficult terrains. This article analyses the socio-demographic, psychographic and travel behavior patterns and its sub-characteristics in the background of foreign and domestic tourists. The data source for the article has been obtained from a primary survey of 350 randomly chosen tourists, 175 each from domestic and foreign tourists, visiting Kerala’s ecotourists destinations during August-December 2010-11. Several socio-demographic, psychographic and life style factors have been identified based on the inference from field survey. There is considerable divergence in most of the factors identified in the case of domestic and international tourists. Post-trip attributes like satisfaction and intentions to return show that the ecotourism destinations in Kerala have significant potential that can help communities in the region.