A Study On The Utilisation Of Plastic Wastes In Stabilised Masonry Blocks

At this era of energy crisis and resource depletion, availability of conventional materials throughout the year in quantity and quality, pose a hectic problem for the builders. Adding fuel to the fire, the demand of these materials increases day by day, since the housing and habitat requirements exponentially increase time to time. There is an international concern over this crisis and researchers are reorienting themselves, so as to evolve appropriate masonry units, using locally available cheap materials and technology. The concept of green material and construction has been well conceived in the research so that marginal materials and unskilled labour can be employed for the mass production of building blocks. In this context, considering earth as a sustainable material, there is a growing interest in the use of it, as a modern construction material. Solid waste management is one of the current major environmental concerns in our country. Our country is left with millions of cubic metre of waste plastics. One of the methods to satisfactorily address this solid waste management and the environmental issues is to suitably accommodate the waste in some form (as fibres). Their employability in block making in the form of fibres (plastic fibre- mud blocks) can be investigated through a fundamental research. Also, the review of the existing literature shows that most studies on natural fibres are focussed on cellulose based/ vegetable fibres obtained from renewable plant resources except in very few cases, where animal fibre, plastic fibre and polystyrene fabric were used. At this context, for the plastic fibre-mud blocks to be more widely applicable, a systematic quantification of the relevant physical and mechanical properties of the fibre masonry units is crucial, to enable an objective evaluation of the composite material’s response to actual field condition. This research highlights the salient observations from the detailed investigation of a systematic study on the effect of embedded fibres, made of plastic wastes on the performance of stabilised mud blocks.

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.