Sustainability of Medicinal Plants in Kerala Economic Considerations in Domestication and Conservation of Forest Resources

The present study on the sustainability of medicinal plants in Kerala economic considerations in domestication and conservation of forest resources. There is worldwide consensus on the fact that medicinal plants are important not only in the local health support systems but in rural income and foreign exchange earnings. Sustainability of medicinal plants is important for the survival of forest dwellers, the forest ecosystem, conserving a heritage of human knowledge and overall development through linkages. More equitable sharing of the benefits from commercial utilization of the medicinal plants was found essential for the sustainability of the plants. Cultivation is very crucial for the sustainability of the sector. Through a direct tie-up with the industry, the societies can earn more income and repatriate better collection charges to its members. Cultivation should be carried out in wastelands, tiger reserves and in plantation forests. In short, the various players in the in the sector could find solution to their specific problems through co-operation and networking among them. They should rely on self-help rather than urging the government to take care of their needs. As far as the government is concerned, the forest department through checking over- exploitation of wild plants and the Agriculture Dept. through encouraging cultivation could contribute to the sustainable development of the medicinal plant sector.

Use of E-Resources in a Networked Environment A Case Study of CUSAT

This paper studies the use of E•rescurces by the faculty and research scholars of Cochin University of Science and TechnologytI'he: use of E resources under INDEST.consortinm;_UGC.lnfonet :project,.and the databases.subscribed.to.in theCUSAT Library are.studied.in the.survey, The:survey•covers various aspects jike.awareness-of the .users, user satisfaction, use pattem of Eoresources,preferenee.for print or electronic version.etc. The problems-faced-are stressed :and possible solutions are suggested

E-Resources in the Engineering College Libraries of Kerala: Problems towards Sustainable Collection Development

Electronic resources have become a vital part of an academic library especially in universities and higher education institutions. The availability of electronic resources and the acceptance of the fonnat among the academics are rising day by day. As far as engineering students are concerned, they are much techno-savy and are more used to electronic resources. So it has become necessary for the libraries of engineering institutions to subscribe and provide access to electronic resources to satisfy its user community. Many studies have identified that academics are much preferring online journals and databases than their print counter-parts

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.

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