Development of Free Space Method for Material Characterisation and GPR Study & A Case Study on Biological Samples

The thesis mainly focuses on material characterization in different environments: freely available samples taken in planar fonn, biological samples available in small quantities and buried objects.Free space method, finds many applications in the fields of industry, medicine and communication. As it is a non-contact method, it can be employed for monitoring the electrical properties of materials moving through a conveyor belt in real time. Also, measurement on such systems at high temperature is possible. NID theory can be applied to the characterization of thin films. Dielectric properties of thin films deposited on any dielectric substrate can be determined. ln chemical industry, the stages of a chemical reaction can be monitored online. Online monitoring will be more efficient as it saves time and avoids risk of sample collection.Dielectric contrast is one of the main factors, which decides the detectability of a system. lt could be noted that the two dielectric objects of same dielectric constant 3.2 (s, of plastic mine) placed in a medium of dielectric constant 2.56 (er of sand) could even be detected employing the time domain analysis of the reflected signal. This type of detection finds strategic importance as it provides solution to the problem of clearance of non-metallic mines. The demining of these mines using the conventional techniques had been proved futile. The studies on the detection of voids and leakage in pipes find many applications.The determined electrical properties of tissues can be used for numerical modeling of cells, microwave imaging, SAR test etc. All these techniques need the accurate determination of dielectric constant. ln the modem world, the use of cellular and other wireless communication systems is booming up. At the same time people are concemed about the hazardous effects of microwaves on living cells. The effect is usually studied on human phantom models. The construction of the models requires the knowledge of the dielectric parameters of the various body tissues. lt is in this context that the present study gains significance. The case study on biological samples shows that the properties of normal and infected body tissues are different. Even though the change in the dielectric properties of infected samples from that of normal one may not be a clear evidence of an ailment, it is an indication of some disorder.ln medical field, the free space method may be adapted for imaging the biological samples. This method can also be used in wireless technology. Evaluation of electrical properties and attenuation of obstacles in the path of RF waves can be done using free waves. An intelligent system for controlling the power output or frequency depending on the feed back values of the attenuation may be developed.The simulation employed in GPR can be extended for the exploration of the effects due to the factors such as the different proportion of water content in the soil, the level and roughness of the soil etc on the reflected signal. This may find applications in geological explorations. ln the detection of mines, a state-of-the art technique for scanning and imaging an active mine field can be developed using GPR. The probing antenna can be attached to a robotic arm capable of three degrees of rotation and the whole detecting system can be housed in a military vehicle. In industry, a system based on the GPR principle can be developed for monitoring liquid or gas through a pipe, as pipe with and without the sample gives different reflection responses. lt may also be implemented for the online monitoring of different stages of extraction and purification of crude petroleum in a plant.Since biological samples show fluctuation in the dielectric nature with time and other physiological conditions, more investigation in this direction should be done. The infected cells at various stages of advancement and the normal cells should be analysed. The results from these comparative studies can be utilized for the detection of the onset of such diseases. Studying the properties of infected tissues at different stages, the threshold of detectability of infected cells can be determined.

Content Based Image Retrieval Using Low Level Features of Automatically Extracted Regions of Interest

This paper proposes a region based image retrieval system using the local colour and texture features of image sub regions. The regions of interest (ROI) are roughly identified by segmenting the image into fixed partitions, finding the edge map and applying morphological dilation. The colour and texture features of the ROIs are computed from the histograms of the quantized HSV colour space and Gray Level co- occurrence matrix (GLCM) respectively. Each ROI of the query image is compared with same number of ROIs of the target image that are arranged in the descending order of white pixel density in the regions, using Euclidean distance measure for similarity computation. Preliminary experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods.

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.

Content Based Video Retrieval using SURF Descriptor

This paper presents a Robust Content Based Video Retrieval (CBVR) system. This system retrieves similar videos based on a local feature descriptor called SURF (Speeded Up Robust Feature). The higher dimensionality of SURF like feature descriptors causes huge storage consumption during indexing of video information. To achieve a dimensionality reduction on the SURF feature descriptor, this system employs a stochastic dimensionality reduction method and thus provides a model data for the videos. On retrieval, the model data of the test clip is classified to its similar videos using a minimum distance classifier. The performance of this system is evaluated using two different minimum distance classifiers during the retrieval stage. The experimental analyses performed on the system shows that the system has a retrieval performance of 78%. This system also analyses the performance efficiency of the low dimensional SURF descriptor.