Shabana Urooj

A Robust Polynomial Filtering Framework for Mammographic Image Enhancement From Biomedical Sensors

This paper presents a non-linear framework employing a robust polynomial filter for accomplishing enhancement of mammographic abnormalities outcoming from biomedical instrumentation, i.e., X-rays instrumentation. The approach proposed in this paper uses a linear combination of Type-0 and Type-II polynomial filters as a generalized filtering solution to achieve enhancement of mammographic masses and calcifications irrespective of the nature of background tissues. A Type-0 filter provides contrast enhancement, suppressing the ill-effects of background noise. On the other hand, Type-II filter performs edge enhancement leading to preservation of finer details. Contrast improvement index is used as a performance measure to quantify the degree of improvement in contrast of the region-of interest. In addition, estimation of signal-to-noise ratio (in terms of PSNR and ASNR) is carried out to account for the suppression in background noise levels and over-enhancements of the processed mammograms. These measures are used as a mechanism to optimally select the filter parameters and also serve as a quantifying platform to compare the performance of the proposed filter with other non-linear enhancement techniques to be used for diverse biomedical image sensors.

A Polynomial filtering model for enhancement of mammogram lesions

This paper presents a preliminary analysis of a class of non-linear filters for enhancement of mammogram lesions. A non-linear filtering approach employing polynomial model of non-linearity is designed by second order truncation of Volterra series expansion. The proposed filter response is a linear combination of Type-0 and Type-II Volterra filters. Type-0 filter provides contrast enhancement, suppressing the ill-effects of background noise. On the other hand, Type-II filter employs edge enhancement. The objective analysis of the proposed filter is carried out by estimating values of quality parameters like CEM and PSNR on mammograms from MIAS and DDSM databases.

Entropy Index in Quantitative EEG Measurement for Diagnosis Accuracy

Electroencephalogram (EEG) remains the most immediate, simple, and rich source of information for understanding phenomena related to brain electrical activities. It is certainly a source of basic and interesting information to be extracted using specific and appropriate techniques. The most important aspect in processing EEG signals is to use less co-lateral assets and instrumentation in order to carried out a possible diagnosis; this is the approach of early diagnosis. Advanced estimate spectral analysis can reveal new information encompassed in EEG signals by means of specific parameters or indices. The research proposes a multidimensional approach with a combined use of decimated signal diagonalization (DSD) as basis from which it is possible to work by finding appropriate signal windows for revealing expected information and overcoming signal processing limitations encountered in quantitative EEG. Important information, about the state of the patient under observation, must be extracted from calculated DSD bispectrum. For this aim, it is useful to define an assessment index about the dynamic process associated with the analyzed signal. This information is measured by means of entropy, since the degree of order/disorder of the recorded EEG signal will be reflected in the obtained DSD bispectrum. The general advantage of multidimensional approach is to reveal eventual stealth frequencies “in space and in time” giving a topological vision to be correlated to physical areas which these frequencies emerge from. Long term and sleeping EEG recorded are analyzed, and the results obtained are of interest for an accurate diagnosis of the patients clinical condition.

A Composite Wavelets and Morphology Approach for ECG Noise Filtering

Noisy ECG signals contain variations in the amplitudes or in the time intervals which represents the abnormalities associated with the heart; thereby making visual diagnosis difficult for cardiovascular diseases. Hence, to facilitate proper analysis of ECG; this paper presents a combination of wavelets analysis and morphological filtering as an approach for noise removal in ECG signals. The proposed algorithm involves sub-band decomposition of ECG signal using bi-orthogonal wavelet family. The wavelet detail coefficients containing the noisy components are then processed by morphological operators using linear structuring elements. The morphological filter processes only the corrupted bands without affecting the signal parameters. Simulation results of the proposed algorithm show noteworthy suppression of noise in terms of higher signal-to-noise ratio preserving the ST segment and R wave of ECG.

Mutidimensional analysis of EEG features using advanced spectral estimates for diagnosis accuracy

Electroencephalogram (EEG) is a source of interesting information if one is able to extract them according to appropriate techniques. The conditions of individual under EEG test is a key issue. In general, EEG feature extraction can be associated to other information like Electrocardiogram (ECG), ergospirometry and electromyogram (EMG). However, in some cases, a multidimensional representation is used; bispectrum is an example of such a representation. HOS (high order statistics), for instance, include the bispectrum and the trispectrum (third and fourth order statistics, respectively). Advanced estimate spectral analysis can reveal new information encompassed in EEG signals. That is the reason the author propose an algorithm based on DSD (Decimated Signal Diagonalization) that is able of processing exponentially dumped signals like those that regard EEG features. The version proposed here is a multidimensional one.

An Evaluation of Edge Detection Algorithms for Mammographic Calcifications

Edge detection is an important module in medical imaging for diagnostic detection and extraction of features. The main limitation of the existing evaluation measures for edge detection algorithms is the requirement of a reference image for comparison. Thus, it becomes difficult to assess the performance of edge detection algorithms in case of mammographic features. This paper presents a new version of reconstruction estimation function for objective evaluation of edge enhanced mammograms containing microcalcifications. It is a non-reference approach helpful in selection of most appropriate algorithm for edge enhancement of microcalcifications and also plays a key role in selecting parameters for performance optimization of these algorithms. Simulations are performed on mammograms from MIAS database with different category of background tissues; the obtained results validate the efficiency of the proposed measure in precise assessment of mammograms (edge-maps) in accordance with the subjectivity of human evaluation.

A Non-Linear Approach to ECG Signal Processing using Morphological Filters

Analysis of the Electrocardiogram ECG signals is the pre-requisite for the clinical diagnosis of cardiovascular diseases. ECG signal is degraded by artifacts such as baseline drift and noises which appear during the acquisition phase. The effect of impulse and Gaussian noises is randomly distributed whereas baseline drift generally affects the baseline of the ECG signal; these artifacts induce interference in the diagnosis of cardio diseases. The influence of these artifacts on the ECG signals needs to be removed by suitable ECG signal processing scheme. This paper proposes combination of non linear morphological operators for the noise and baseline drift removal. Non flat structuring elements of varying dimensions are employed with morphological filtering to achieve low distortion as well as good noise removal. Simulation outcomes illustrate noteworthy improvement in baseline drift yielding lower values of MSE and PRD; on the other hand high signal to noise ratios depicts suppression of impulse and Gaussian noises.

A novel approach for suppression of powerline interference and impulse noise in ECG signals

One of the major problems encountered in recording ECG is the appearance of unwanted distortions induced by power line interference in the electrocardiogram. In addition, infections due to impulse noise leads to variations in the amplitudes which represent the abnormalities associated with the heart. This paper proposes a novel approach for addressing both the aforementioned issues in ECG signals employing sub-band decomposition using wavelets analysis. Morphological filtering is applied to the detail sub-bands for removal of impulse noise using one-dimensional structuring element. Further, the powerline interference is removed using IIR Butterworth filter providing significant reduction in power spectral density levels between 50 to 60 Hz. The finally reconstructed ECG signal yields reasonably good impulse noise as well as powerline suppression using the proposed approach.

Human visual system based unsharp masking for enhancement of mammographic images

Abstract Non-Linear Polynomial Filters (NPF) consists of a schema of linear and quadratic filter components operating as a fusion of low-and high pass filters. NPF has shown distinguished performance when applied for mammogram enhancement. The role has been multifaceted, as there is visual contrast improvement of Region-of-Interest (ROI), i.e. the tumor region as well as those of the surrounding diagnostic features. This paper presents the usage of NPF in design of Non-Linear Unsharp Masking (UM) framework for the enhancement of X-ray mammograms (digital mammographic images). The UM approach presented consists of operational modules namely: edge preserving and contrast enhancement algorithms which are realized using different variants of NPF. Application of Human Visual System (HVS) based adaptive thresholding during contrast enhancement provides for an effective minimization of background noises. The responses of the different modules are then combined using non-linear fusion operators based on an improved logarithmic model of perception and human vision. The obtained enhancement results demonstrate noteworthy improvement in contrast of lesion region together with better visualization of lesion margins and fine details. It has been subjectively as well as objectively shown that the enhancement of the contrast and edges do not introduces unwanted overshoots in the ROI.

Prediction of quantitative intrathoracic fluid volume to diagnose pulmonary oedema using LabVIEW

Pulmonary oedema is a life-threatening disease that requires special attention in the area of research and clinical diagnosis. Computer-based techniques are rarely used to quantify the intrathoracic fluid volume (IFV) for diagnostic purposes. This paper discusses a software program developed to detect and diagnose pulmonary oedema using LabVIEW. The software runs on anthropometric dimensions and physiological parameters, mainly transthoracic electrical impedance (TEI). This technique is accurate and faster than existing manual techniques. The LabVIEW software was used to compute the parameters required to quantify IFV. An equation relating per cent control and IFV was obtained. The results of predicted TEI and measured TEI were compared with previously reported data to validate the developed program. It was found that the predicted values of TEI obtained from the computer-based technique were much closer to the measured values of TEI. Six new subjects were enrolled to measure and predict transthoracic impedance and hence to quantify IFV. A similar difference was also observed in the measured and predicted values of TEI for the new subjects.