Fractal based techniques for classification of mammograms and identification of microcalcifications

After skin cancer, breast cancer accounts for the second greatest number of cancer diagnoses in women. Currently the etiologies of breast cancer are unknown, and there is no generally accepted therapy for preventing it. Therefore, the best way to improve the prognosis for breast cancer is early detection and treatment. Computer aided detection systems (CAD) for detecting masses or micro-calcifications in mammograms have already been used and proven to be a potentially powerful tool , so the radiologists are attracted by the effectiveness of clinical application of CAD systems. Fractal geometry is well suited for describing the complex physiological structures that defy the traditional Euclidean geometry, which is based on smooth shapes. The major contribution of this research include the development of • A new fractal feature to accurately classify mammograms into normal and normal (i)With masses (benign or malignant) (ii) with microcalcifications (benign or malignant) • A novel fast fractal modeling method to identify the presence of microcalcifications by fractal modeling of mammograms and then subtracting the modeled image from the original mammogram. The performances of these methods were evaluated using different standard statistical analysis methods. The results obtained indicate that the developed methods are highly beneficial for assisting radiologists in making diagnostic decisions. The mammograms for the study were obtained from the two online databases namely, MIAS (Mammographic Image Analysis Society) and DDSM (Digital Database for Screening Mammography.

Statistical Machine Learning Techniques for the Prediction of Learning Disabilities in School-Age Children

Learning Disability (LD) is a general term that describes specific kinds of learning problems. It is a neurological condition that affects a child's brain and impairs his ability to carry out one or many specific tasks. The learning disabled children are neither slow nor mentally retarded. This disorder can make it problematic for a child to learn as quickly or in the same way as some child who isn't affected by a learning disability. An affected child can have normal or above average intelligence. They may have difficulty paying attention, with reading or letter recognition, or with mathematics. It does not mean that children who have learning disabilities are less intelligent. In fact, many children who have learning disabilities are more intelligent than an average child. Learning disabilities vary from child to child. One child with LD may not have the same kind of learning problems as another child with LD. There is no cure for learning disabilities and they are life-long. However, children with LD can be high achievers and can be taught ways to get around the learning disability. In this research work, data mining using machine learning techniques are used to analyze the symptoms of LD, establish interrelationships between them and evaluate the relative importance of these symptoms. To increase the diagnostic accuracy of learning disability prediction, a knowledge based tool based on statistical machine learning or data mining techniques, with high accuracy,according to the knowledge obtained from the clinical information, is proposed. The basic idea of the developed knowledge based tool is to increase the accuracy of the learning disability assessment and reduce the time used for the same. Different statistical machine learning techniques in data mining are used in the study. Identifying the important parameters of LD prediction using the data mining techniques, identifying the hidden relationship between the symptoms of LD and estimating the relative significance of each symptoms of LD are also the parts of the objectives of this research work. The developed tool has many advantages compared to the traditional methods of using check lists in determination of learning disabilities. For improving the performance of various classifiers, we developed some preprocessing methods for the LD prediction system. A new system based on fuzzy and rough set models are also developed for LD prediction. Here also the importance of pre-processing is studied. A Graphical User Interface (GUI) is designed for developing an integrated knowledge based tool for prediction of LD as well as its degree. The designed tool stores the details of the children in the student database and retrieves their LD report as and when required. The present study undoubtedly proves the effectiveness of the tool developed based on various machine learning techniques. It also identifies the important parameters of LD and accurately predicts the learning disability in school age children. This thesis makes several major contributions in technical, general and social areas. The results are found very beneficial to the parents, teachers and the institutions. They are able to diagnose the child’s problem at an early stage and can go for the proper treatments/counseling at the correct time so as to avoid the academic and social losses.

Trace metal speciation in the Cochin Estuary

This thesis Entitled Trace metal speciation in the cochin estuary.Natural waters provide a favourable environment for speciation studies because of the prevailing variable chemical matrix and the variety of metal forms which may exist there.An estuary is a mixing zone of riverine and oceanic waters with widely varying compositions where end members interact both physically and chemically. The trace element chemistry in the estuarine environment has been an area of considerable research in the past decades. The trace metal distribution in the Cochin estuary is considerably influenced by the tropical features of the location and by human activities. The lower Periyar river and the Cochin estuary have been particularly selected for this investigation in view of the impact of trace metals on the estuarine ecosystem as well as in attempt quantify the phenomenon of metal speciation in the waters of a tropical coastal plain waterbody. If the concentration in the water media is very low, then, many of the fractions that could be estimated by speciation schemes for metals will fall below the detection limits, a factor which is undesirable.The study would also delineate the features of metal speciation which modify the chemical regime of ionic elements that traverse natural boundaries in aquatic environments, especally in those tropical areas prone to multivariate geographical settings.

Computer vision techniques in seafood quality control

The application of computer vision based quality control has been slowly but steadily gaining importance mainly due to its speed in achieving results and also greatly due to its non- destnictive nature of testing. Besides, in food applications it also does not contribute to contamination. However, computer vision applications in quality control needs the application of an appropriate software for image analysis. Eventhough computer vision based quality control has several advantages, its application has limitations as to the type of work to be done, particularly so in the food industries. Selective applications, however, can be highly advantageous and very accurate.Computer vision based image analysis could be used in morphometric measurements of fish with the same accuracy as the existing conventional method. The method is non-destructive and non-contaminating thus providing anadvantage in seafood processing.The images could be stored in archives and retrieved at anytime to carry out morphometric studies for biologists.Computer vision and subsequent image analysis could be used in measurements of various food products to assess uniformity of size. One product namely cutlet and product ingredients namely coating materials such as bread crumbs and rava were selected for the study. Computer vision based image analysis was used in the measurements of length, width and area of cutlets. Also the width of coating materials like bread crumbs was measured.Computer imaging and subsequent image analysis can be very effectively used in quality evaluations of product ingredients in food processing. Measurement of width of coating materials could establish uniformity of particles or the lack of it. The application of image analysis in bacteriological work was also done

Studies on the Intercompartmental Exchange of Trace Metals in an Estuarine System

The present study is an attempt at investigating the intercompartmental exchange of trace metals (copper, cadmium, zinc, lead and nickel) in the Cochin estuary. The nature and extent of distribution in the different compartments with special reference to the transport from environmental compartments to biological compartments have been dealt with in detail. The suitability of the shells of Villorita cyprinoides var cochinensis (Hanely) in pollution monitoring activities has been assessed. A mathematical model (SAAMPLE - Shells in the Assessment of Aquatic Metal Pollution Levels) based on kinetic laws that govern the intercompartmental exchange has been proposed.

Role of trace element of the growth and Physiology of selected microalgae

The main objective of the study is primarily to determine the magnitude of selected trace elements, the concentrations of which would possibly accelerate growth resulting in larger biomass and sustained period of exponential phase for economically viable harvest. The study on the effect of three trace elements namely Cu, Mn and Zn on two species of algae,ISOChrySiS galbana Parke and Synechocystib salina Wislouch under different conditions of salinity, PH and temperature involves several combinations for each metal, from which the relative set of conditions has been adduced. The scheme of the experiments was statistically designed for interpretation of data and factors were assessed and graded according to relative importance. The methodology adopted for data interpretation is analysis of variance by split-plot design method. The thesis has been divided into five chapters. The introductory chapter explains the relevance of the research work undertaken. Chapter 11 gives a review on the work pertaining to the above mentioned three trace elements in relation to nutrition as well as on the toxic aspects about which there is an abundance of literature. Chapter Ill presents a detailed description of the material and specialised methods followed for the study. The results and conclusions of the various experiments on effect of metals on growth and other physiological activities are discussed in Chapters IV and V.

Detection of sandal spike phytoplasma using immunological and molecular techniques

Spike disease in sandal is generally diagnosed by the manifestation of external symptoms. Attempts have been made to detect the diseased plants by determining the length/breadth ratio of leaves (lyengar, 1961) and histochemical tests using Mann's stain (Parthasarathi et al., 1966), Dienes' stain (Ananthapadmanabha et a/., 1973) aniline blue and Hoechst 33258 (Ghosh et a/., 1985, Rangaswamy, 1995). But most of these techniques are insensitive, indirect detection methods leading to misinterpretation of results. Moreover, to identify disease resistant sandal trees, highly sensitive techniques are needed to detect the presence of the pathogen. In sandal forests, several host plants of sandal like Zizyphus oenop/ea (Fig. 1.3) also exhibit the yellows type disease symptoms. Immunological and molecular assays have to be developed to confirm the presence of sandal spike phytoplasma in such hosts. The major objectives of the present work includes:In situ detection of sandal spike phytoplasma by epifluorescence microscopy and scanning electron microscopy.,Purification of sandal spike phytoplasma and production of polyclonal antibodies.,Amino acid and total protein estimation of sandal spike phytoplasma.,Immunological detection of sandal spike phytoplasma., Molecular detection of sandal spike phytoplasma.,Screening for phytoplasma in host plants of spike disease affected sandal using immunological and molecular techniques.

Chemical Evaluation of Selected Organics and Trace Metals in the Mangrove Macroflora Of Cochin

Mangroves are considered to play a significant role in global carbon cycling. Themangrove forests would fix CO2 by photosynthesis into mangrove lumber and thus decrease the possibility of a catastrophic series of events - global warming by atmospheric CO2, melting of the polar ice caps, and inundation of the great coastal cities of the world. The leaf litter and roots are the main contributors to mangrove sediments, though algal production and allochthonous detritus can also be trapped (Kristensen et al, 2008) by mangroves due to their high organic matter content and reducing nature are excellent metal retainers. Environmental pollution due to metals is of major concern. This is due to the basic fact that metals are not biodegradable or perishable the way most organic pollutants are. While most organic toxicants can be destroyed by combustion and converted into compounds such as C0, C02, SOX, NOX, metals can't be destroyed. At the most the valance and physical form of metals may change. Concentration of metals present naturally in air, water and soil is very low. Metals released into the environment through anthropogenic activities such as burning of fossils fuels, discharge of industrial effluents, mining, dumping of sewage etc leads to the development of higher than tolerable or toxic levels of metals in the environment leading to metal pollution. Of course, a large number of heavy metals such as Fe, Mn, Cu, Ni, Zn, Co, Cr, Mo, and V are essential to plants and animals and deficiency of these metals may lead to diseases, but at higher levels, it would lead to metal toxicity. Almost all industrial processes and urban activities involve release of at least trace quantities of half a dozen metals in different forms. Heavy metal pollution in the environment can remain dormant for a long time and surface with a vengeance. Once an area gets toxified with metals, it is almost impossible to detoxify it. The symptoms of metal toxicity are often quite similar to the symptoms of other common diseases such as respiratory problems, digestive disorders, skin diseases, hypertension, diabetes, jaundice etc making it all the more difficult to diagnose metal poisoning. For example the Minamata disease caused by mercury pollution in addition to affecting the nervous system can disturb liver function and cause diabetes and hypertension. The damage caused by heavy metals does not end up with the affected person. The harmful effects can be transferred to the person's progenies. Ironically heavy metal pollution is a direct offshoot of our increasing ability to mass produce metals and use them in all spheres of existence. Along with conventional physico- chemical methods, biosystem approachment is also being constantly used for combating metal pollution

Phase Transitions Of Trace Metals In The Aquatic Environment Of Kuttanad, Kerala

Environmental persistence, fate and interactive effects with living organisms - beneficial or toxic - of trace elements are directly related to the physico-chemical forms in which they occur. Knowledge on the association of trace metals with different environmental compartments in an aquatic system are, therefore, essential for monitoring the trace metal pollution as well as transport, fate and bio-geochemical cycles of trace metals. This thesis is a modest attempt in assessing the trace metal levels and their behaviour in the aquatic environment of Kuttanad, an aquatic system that is severely affected by man's intervention on natural processes, by seriously evaluating the levels of trace metals in dissolved and particulate phases and also in the different chemical fractions of the sediments.

Investlgations On The Chemical Constituents And Trace Metal Interactions In Some Bivalve Moll.USCS Of The Cochin Backwaters

The subsequent chapters of the Thesis deal with the toxic effects of mercury, copper, zinc und~1ead on these bivalve molluecs, their accumulation and distribution among various organs of the animals and also the motel retention winstica by the three species. Static biousauy tests have been conducted in these studies. It was found that the concentrations of the various metals studied in these organism are well below the permitted level given far ease ahellfienes (crab and ehrimgi and that these maliuscs are very good integrators ef trace metals from their environment and may be used as an indicator organism sf metal pallutaute. The present investigutionsemphaeie the need for a clean coastal water and gives a serious warning regarding the possiblc route of heavy metals in ta human body thraugh marine food chain.

Studies on the effects of selected trace metals on Matapenaeus Dobsoni (MIERS)

This thesis is an attempt by the author to assess the suitability of Metapenaeus dobsoni (Miers), an economically important crustacean species as a sentinel organism of trace metal pollution. The results of detailed investigations on seasonal variation, bioassay, accumulation and depuration of three metals viz., mercury, copper and zinc are presented and discussed. The importance of trace metals in the aquatic environment and their present status in the study area - Cochin backwaters, the significance of crustacean fisheries, the species M. dobsoni and the objectives of the present studies are described in Chapter 1. The methodology adopted during the investigation is given in Chapter 2. Chapter 3 delineates the seasonal variation of Hg, Cu and Zn in the edible and non-edible parts of M. dobsoni collected from Cochin backwaters for a period of one year (June 1984-May 1985). The results of bioassay experiments are given in Chapter 4. Kinetics of accumulation ,retention and depuration of trace metals, their biological half-life, the influence of size group and environmental factors are given in Chapter 5. The effect of these metals on the physiological response of M. dobsoni viz. oxygen consumption is included in Chapter 6. A summary and list of references are also appended.

“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.

‘Biochemical Responses to Heavy Metals in Oreochromis mossambicus with special reference to Metal Detoxifying Mechanism

The present work is focused on the organelle and biochemical responses to heavy metal exposure in the fish Oreochromis mossambicus giving particular importance to the metal detoxifying machinery of the organism. The thesis is an outcome of the effort aimed at developing practicable monitoring techniques to deliver guidelines for biological effect monitoring and the need for specific biochemical methods to detect biological effects of heavy metals that can be interpreted in terms of the health status of the individual organism and eventually alterations in vital processes as growth and reproduction. The efficiency of the metal detoxifying metallothioneins which is an attractive tool for biological monitoring, their role as scavengers of trace metal ions and thus in relieving the biological machinery from their toxicity effects are important themes of this study. Efforts have also been made to test the reliability of the spill over hypothesis of the action of metallothioneins (Winge et a1.,1973) and their use as a biological barometer of heavy metal stress.

“Reconstruction of Gene Regulatory Network from Expression Profile of Plasma RNA Data of Colorectal Cancer Patients using Soft Computing Techniques”

Microarray data analysis is one of data mining tool which is used to extract meaningful information hidden in biological data. One of the major focuses on microarray data analysis is the reconstruction of gene regulatory network that may be used to provide a broader understanding on the functioning of complex cellular systems. Since cancer is a genetic disease arising from the abnormal gene function, the identification of cancerous genes and the regulatory pathways they control will provide a better platform for understanding the tumor formation and development. The major focus of this thesis is to understand the regulation of genes responsible for the development of cancer, particularly colorectal cancer by analyzing the microarray expression data. In this thesis, four computational algorithms namely fuzzy logic algorithm, modified genetic algorithm, dynamic neural fuzzy network and Takagi Sugeno Kang-type recurrent neural fuzzy network are used to extract cancer specific gene regulatory network from plasma RNA dataset of colorectal cancer patients. Plasma RNA is highly attractive for cancer analysis since it requires a collection of small amount of blood and it can be obtained at any time in repetitive fashion allowing the analysis of disease progression and treatment response.

Cooperative caching techniques for mobile ad hoc networks

Data caching can remarkably improve the efficiency of information access in a wireless ad hoc network by reducing the access latency and bandwidth usage. The cache placement problem minimizes total data access cost in ad hoc networks with multiple data items. The ad hoc networks are multi hop networks without a central base station and are resource constrained in terms of channel bandwidth and battery power. By data caching the communication cost can be reduced in terms of bandwidth as well as battery energy. As the network node has limited memory the problem of cache placement is a vital issue. This paper attempts to study the existing cooperative caching techniques and their suitability in mobile ad hoc networks.