Tobacco abuse and alcoholism cause cancer, emphysema, and heart disease, which contribute to high death rates, globally. Society pays a significant cost for these habits whose first demonstration in many cases is in the oral cavity. Oral cavity disorders are highly curable if a screening procedure is available to diagnose them in the earliest stages. The aim of the study is to identify the severity of tobacco abuse, in oral cavity, as reflected by the emission from endogenous fluorophores and the chromophore hemoglobin. A group who had no tobacco habits and another with a history of tobacco abuse were included in this study. To compare the results with a pathological condition, a group of leukoplakia patients were also included. Emission from porphyrin and the spectral filtering modulation effect of hemoglobin were collected from different sites. Multivariate analysis strengthened the spectral features with a sensitivity of 60% to 100% and a specificity of 76% to 100% for the discrimination. Total hemoglobin and porphyrin levels of habitués and leukoplakia groups were comparable, indicating the alarming situation about the risk of tobacco abuse. Results prove that fluorescence spectroscopy along with multivariate analysis is an effective noninvasive tool for the early diagnosis of pathological changes due to tobacco abuse.
Fluorescence and diffuse reflectance spectroscopy are powerful tools to differentiate normal and malignant tissue based on the emissions from endogenous fluorophores and diffuse reflection of absorbers such as hemoglobin. However, separate analytical methods are used for the identification of fluorophores and hemoglobin. The estimation of fluorophores and hemoglobin simultaneously using a single technique of autofluorescence spectroscopy is reported, and its diagnostic potential on clinical tissue samples is potentially exploited. Surgically removed brain tissues from patients that are later identified pathologically as astrocytoma, glioma, meningioma, and schwannoma are studied. The emissions from prominent fluorophores collagen, flavin adenine dinucleotide, phospholipids, and porphyrin are analyzed at 320 and 410 nm excitations. The hemoglobin concentration is also calculated from the ratio of fluorescence emissions at 500 and 570 nm. A better classification of normal and tumor tissues is yielded for 410 nm excitation compared to 320 nm when diagnostic algorithm based on linear discriminant analysis is used. The potential of fluorescence spectroscopy as a single entity to evaluate the prominent fluorophores as well as the hemoglobin concentration within normal and tumor brain tissues is emphasized.
Segmentation in Magnetic resonance imaging (MRI) images is a widely studied problem, and techniques
(supervised and unsupervised) are discussed in the literature. The basic approaches to image segmentation are
based upon: (a) boundary representation, (b) regional characteristics and (c) a combination of boundary and
region-based features. In this paper, we report classification of brain tissue based objects employing one of
combination of boundary and region-based features as wavelet modified generic Fourier descriptor (WGFD)
technique. This technique have been applied to a database consisting of 3 different class's tissues, each class
consist of 50 shapes. The Euclidean distance has been calculated as a similarity measure parameter for tissue
shape classification. The classification results have been carried out and it is inferred that WGFD performs for
brain tissue classification. For brain tissue recognition, a simulation experiment employing hybrid correlator
architecture has been carried out. We have used Wavelet modified maximum average correlation hight
(MACH) filter for hybrid correlator. Mexican-hat wavelet has used to synthesize the wavelet MACH filter for
simulation experiment.
Detection of rotationally distorted targets is a challenging task in pattern recognition applications. Recently, we proposed
and implemented a wavelet-modified maximum average correlation height (MACH) filter for in-plane and out-of-plane
rotation invariance in hybrid digital-optical correlator architecture. Use of wavelet transform improved the performance
of the MACH filter by reducing the number of filters required for identifying a rotated target and enhancing the
correlation peak intensity significantly. The output of a hybrid digital-optical correlator contains two autocorrelation
peaks and a strong dc. To capture a desired single autocorrelation peak a chirp function with the wavelet-modified
MACH filter was used. The influence of perturbations in hybrid digital-optical correlator has also been studied.
Perturbations include, the effect of occlusion on input target, the effect of additive and multiplicative noise and their
combined effect on input target, and the effect of occlusion of product function to be optically processed for obtaining
the correlation outputs. The present paper reviews investigations on the hybrid digital-optical correlation scheme with
special reference to the work carried out at the Photonics Division, IRDE Dehradun.
In this paper, we implement a wavelet-modified fringe-adjusted joint transform correlator (JTC) for real-time target recognition applications. In real-time situation the input scene is captured using a CCD/thermal camera. The obtained joint power spectrum is multiplied with a pre-synthesized fringe-adjusted filter and the resultant function is processed with an appropriately scaled wavelet filter. The wavelet-modified fringe-adjusted JTC has been found to yield better results in comparison to the conventional fringe-adjusted JTC. To suppress the undesired strong dc, the resultant function is differentiated. Differential processing the wavelet-modified fringe-adjusted joint power spectrum removes the zero-order spectra and hence improves the detection efficiency. To focus the correlation terms in different planes in order to capture one of the desired autocorrelation peaks and discard the strong dc and another autocorrelation peak, chirp-encoding technique has also been applied. Computer simulation and experimental results are presented.
In this paper, we describe and implement a genetic algorithm based composite wavelet-matched filter for target recognition. The designed filter is invariant to 0-to-360° out-of-plane rotation ranges. The Mexican hat wavelet has been used for the design of the filter. Genetic algorithm has been used to optimize the weight factors for the input matched filters and the different scale wavelets. The designed filter has been implemented in the hybrid digital-optical correlator architecture. Simulation and experimental results in support of the proposed idea are presented.
We implement a binary differential joint-transform correlator for real-time single- and multiple-target recognition applications. In a real-time situation the input scene is captured using a CCD or thermal camera. The obtained joint power spectrum is first differentiated and then binarized. The subset median threshold method was used to get the threshold value for binarization. The binarized joint power spectrum is displayed over a ferroelectric-liquid-crystal spatial light modulator and is Fourier-transformed optically to obtain the correlation peaks. Differential processing of the joint power spectrum removes the zero-order spectra (dc) and hence improves the detection efficiency. Experiments taking single as well as multiple input images have been performed. The parameters for a performance measure have also been calculated. Single and multiple targets with added Gaussian noise have also been used to check the correlation outputs. Computer simulation and experimental results are presented.
The present paper discusses the design and development of diode laser based sensor, which measures the snow particle size,the number of drifting snow particles passing per unit time through sampling volume and the speed of drifting snow particle. These factors, in turn determine the mass flux of drifting snow over the range of wind speeds. The design of sensor is based on interruption of the laser beam as the snow particle traverse through it. The extinction is detected by a suitable bi-element Pin photo detector. The sensor is designed for measuring snow particle size in the range of 100 micrometers to 2000 micrometers diameter and can count up to 100,000 particles per minute. The sensor is capable of measuring particle speed up to 50 meters per second. The sampling volume of sensor is selected in such a way that a single particle crosses the sampling volume at a time.
Imaging characteristics of optical system can be modified to achieve improved performance by making use of apodisation technique. It can be achieved by modifying either by amplitude or the phase or both over the pupil of the optical system or by using linear polarisation masks placed at different orientations over the zones of the optical system . It has been found that the performance of apodised optical systems can be improved in any desired region of spatial frequencies by varying the relative size of the zones as well as amplitude transmittances in different zones. Similar results are also obtained in case of optical systems masked with linear polarisers.
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