Journal of Medical Signals and Sensors
Volume:1 Issue: 3, Sep-Dec 2011

This paper introduces a novel methodology for the segmentation of brain MS lesions in MRI volumes using a new clustering algorithm named SCPFCM. SCPFCM uses membership, typicality and spatial information to cluster each voxel. The proposed method relies on an initial segmentation of MS lesions in T1-w and T2-w images by applying SCPFCM algorithm, and the T1 image is then used as a mask and is compared with T2 image. The proposed method was applied to 10 clinical MRI datasets. The results obtained on different types of lesions have been evaluated by comparison with manual segmentations.

Breast cancer is a major public health problem for women in the Iran and many other parts of the world. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a pivotal role in breast cancer care, including detection, diagnosis, and treatment monitoring. But segmentation of these images which is seriously affected by intensity inhomogeneities created by radio-frequency coils, is a challenging task. Markov Random Field (MRF) is used widely in medical image segmentation especially in MR images. It is because this method can model intensity inhomogeneities occurring in these images. But this method has two critical weaknesses: Computational complexity and sensitivity of the results to the models parameters. To overcome these problems, in this paper, we present Improved-Markov Random Field (I-MRF) method for breast lesion segmentation in MR images. Unlike the conventional MRF, in the proposed approach, we don’t use the Iterative Conditional Mode (ICM) method or Simulated Annealing (SA) for class membership estimation of each pixel (lesion and non-lesion). The prior distribution of the class membership is modeled as a ratio of two conditional probability distributions in a neighborhood which is defined for each pixel: probability distribution of similar pixels and non-similar ones. Since our proposed approach don’t use an iterative method for maximizing the posterior probability, above mentioned problems are solved. Experimental results show that performance of segmentation in this approach is higher than conventional MRF in terms of accuracy, precision, and Computational complexity.

Abstract Brian Computer interface (BCI) is a direct communication pathway between the brain and an external device. BCIs are often aimed at assisting, augmenting or repairing human cognitive or sensory-motor functions. In this work a new algorithm is introduced to enhancing EEG signals that have been concerned the P300 problem. Signal to noise ratio of EEG signals is very low and have much artifacts. We have proposed a new method based on multiresolution analysis via Independent Component Analysis Fundamentals. We have suggest combination of negentropy as a feature of signal and subband information from wavelet transform. The proposed method is finally tested with dataset from BCI Competition 2003 and gives results that compare favorably.

Medical image registration methods which use mutual information as similarity measure have been improved in recent decades. Mutual Information is a basic concept of Information theory which indicates the dependency of two random variables (or two images). In order to evaluate the mutual information of two images their joint probability distribution is required. Several interpolation methods, such as PV and bilinear, are used to estimate joint probability distribution. Both of these two methods yield some artifacts on mutual information function. PVH and GPV methods are introduced to remove such artifacts. In this paper we show that the acceptable performance of these methods is not due to their kernel function. It’s because of the number of pixels which incorporate in interpolation. Since using more pixels requires more complex and time consuming interpolation process, we propose a new interpolation method which uses only four pixels (the same as PV and bilinear interpolations) and removes most of the artifacts. Experimental results of the registration of CT images show superiority of the proposed scheme.

We propose a method for medical image denoising using calculus of variations and local variance estimation by shaped windows. This method reduces any additive noise and preserves small patterns and edges of images. A pyramid structure-texture decomposition of images is used to separate noise and texture components based on local variance measures. The experimental results show that the proposed method has visual improvement as well as a better SNR,RMSE and PSNR than common medical imgae denoising methods. Experimental results in MR denoising show that SNR,PSNR and RMSE have been improved by 19,9 and 21 percents respectively.

Computer-aided Diagnosis (CAD) systems can assist radiologists in several diagnostic tasks. Lung segmentation is one of the mandatory steps for initial detection of lung cancer in Posterior-Anterior chest radiographs. On the other hand, many CAD schemes in projection chest radiography may benefit from the suppression of the bony structures that overlay the lung fields, e.g. ribs. The original images are enhanced by an adaptive contrast equalization and non-linear filtering. Then an initial estimation of lung area is obtained based on morphological operations and then it is improved by growing this region to find the accurate final contour, then for rib suppression, we use oriented spatial Gabor filter. The proposed method was tested on a publicly available database of 247 chest radiographs. Results show that this method outperformed greatly with accuracy of 96.25% for lung segmentation, also we will show improving the conspicuity of lung nodules by rib suppression with local nodule contrast measures. Because there is no additional radiation exposure or specialized equipment required, it could also be applied to bedside portable chest x-rays. In addition to simplicity of these fully automatic methods, lung segmentation and rib suppression algorithms are performed accurately with low computation time and robustness to noise because of the suitable enhancement procedure

CASA (Computer assisted semen analysis) systems are designed to assist Andrologist labour. Most available CASA systems are not accurate or so expensive. Therefore labours use manual methods to provide parameters. Although some companies have achieved appropriate accuracy, they have not released their methods. So proposing methods in this area might be useful for groups who intend to design new CASA system. One of the parameters which these systems compute is sperm count. In this paper we introduce our algorithm which can count sperms with an acceptable accuracy. Sperm count or concentration is one determinant parameter in male fertility. Our program preprocesses the video frame or image of semen sample under the microscope recorded by camera, then use morphology and effective ellipse detection method techniques to segment sperms and then count appropriate sperms.

In this study, focuses on applying the hp-version for forward modeling. The EIT forward solver is normally based on the conventional finite element method (hFEM). In h-FEM, the polynomial order p of the element shape functions is constant and the element size h, is decreased. To accurately simulate the forward solution with the hFEM, a mesh with large number of nodes and elements is usually needed. To overcome this problem, we proposed the high order finite element method for EIT forward problem. In the p-version, the polynomial order is increased and the mesh size is constant. Combinations of refine the mesh adaptively and used higher order polynomials are called hp-extensions (hp-FEM). With the high order FEM, a smaller number of nodes and hence less computational time and memory are needed to achieve the same or better accuracy in the forward solution than the hFEM. Numerical results are presented that the performance of the hp-version is better than of the h-version.