Sharif Digital Repository

This paper proposes a semi-fragile, robust-to-JPEG2000 compression watermarking method which is based on the Human Visual System (HVS). This method is designed to verify the content integrity of Region of Interest (ROI) in tele-radiology images. The design of watermarking systems based on HVS leads to the possibility of embedding watermarks in places that are not obvious to the human eye. In this way, notwithstanding increased capacity and robustness, it becomes possible to hide more watermarks. Based on perceptual model of HVS, we propose a new watermarking scheme that embeds the watermarks using a replacement method. Thus, the proposed method not only detects the watermarks but also... 

Fetal electrocardiogram (fECG) monitoring is a beneficial method for assessing fetal health and diagnosing the fetal cardiac condition during pregnancy. In this study, an algorithm is proposed to extract fECG from maternal abdominal signals based on doubly constrained block-term (DoCoBT) tensor decomposition. This tensor decomposition method is constrained by quasi-periodicity constraints of fetal and maternal ECG signals. Tensor decompositions are more powerful tools than matrix decomposition, due to employing more information for source separation. Tensorizing abdominal signals and using periodicity constraints of fetal and maternal ECG, appropriately separates subspaces of the mother, the... 

in this paper we present an integrated classifier that is used in mammogram MR-image for classification of breast cancers and abnormalities using a Multi-stage classifier, the method developed here first classifies mammograms into normal and abnormal and then for abnormal cases determines that if the case cancer is benign or malignant and also determine the type of breast cancer. In this paper there are two main topics that must be considered. First one is selection of good features and second is designing a good structure for classifier. In this study, the features are a combination of some features that are extracted from Spatial Grey Level Dependency matrix and some statistical descriptor... 

Objective. Sleep apnea is a serious respiratory disorder, which is associated with increased risk factors for cardiovascular disease. Many studies in recent years have been focused on automatic detection of sleep apnea from polysomnography (PSG) recordings, however, detection of subtle respiratory events named Respiratory Event Related Arousals (RERAs) that do not meet the criteria for apnea or hypopnea is still challenging. The objective of this study was to develop automatic detection of sleep apnea based on Hidden Markov Models (HMMs) which are probabilistic models with the ability to learn different dynamics of the real time-series such as clinical recordings. Approach. In this study, a... 

Purpose: Cardiac arrhythmia is one of the most common heart diseases that can have serious consequences. Thus, heartbeat arrhythmias classification is very important to help diagnose and treat. To develop the automatic classification of heartbeats, recent advances in signal processing can be employed. The Hidden Markov Model (HMM) is a powerful statistical tool with the ability to learn different dynamics of the real time-series such as cardiac signals. Materials and Methods: In this study, a hierarchy of HMMs named Layered HMM (LHMM) was presented to classify heartbeats from the two-channel electrocardiograms. For training in the first layer, the morphology of the heartbeats was used as... 

In this paper, we present an efficient fractal method for detection and diagnosis of mass lesion in mammogram which is one of the abnormalities in mammographic images. We used 110 images that were carefully selected by a radiologist, and their abnormalities were also confirmed by biopsy. These images included circumscribed benign, ill-defined, and spiculated malignant masses. Firstly, we discriminated lesions automatically using new fractal dimensions. The results which were examined by different types of breast density showed that the proposed method was able to yield quite satisfactory detection results. Secondly, noting that contours of masses playing the most important role in diagnosis... 

Finger-Knuckle-Print (FKP) is one of the newest biometrics. In this paper, a novel approach has been proposed to segment the Region of Interest (ROI) of a FKP image using the global intensity. This method upgrades the speed and accuracy of segmentation stage, as well as the pace of other steps of the procedure. This has been achieved by employing the area with maximum intensity in ROI extraction, instead of using the creases of the knuckle image. To confirm this improvement, lots of experiments have been performed and the method has been compared with the only existing schemes for ROI extraction suggested by Zhang and Kekre. At the end, the captured ROI images obtained by three methods have... 

Image segmentation with clustering approach is widely used in biomedical application. Fuzzy c-means (FCM) clustering is able to preserve the information between tissues in image, but not taking spatial information into account, makes segmentation results of the standard FCM sensitive to noise. To overcome the above shortcoming, a modified FCM algorithm for MRI brain image segmentation is presented in this paper. The algorithm is realized by incorporating the spatial neighborhood information into the standard FCM algorithm and modifying the membership weighting of each cluster by smoothing it by Total Variation (TV) denoising. The proposed algorithm is evaluated with accuracy index in... 

In this paper we use fractal method for detection and diagnosis of abnormalities in mammograms. We have used 168 images that were carefully selected by a radiologist and their abnormalities were also confirmed by biopsy. These images included asymmetric lesions, architectural distortion, normal tissue and mass lesion where in case of mass lesion they included circumscribed benign, ill-defined and spiculated malignant masses. At first, by using wavelet transform and piecewise linear coefficient mapping, image enhancement were done. Secondly detection of lesions was done by fractal method as a ROI. Since in investigation of breast cancer, it is important that fibroglandular tissues in both... 

In this paper a multi-scale image enhancement strategy using wavelets as applied to digital mammograms is presented. For multiresolution wavelet analysis redundant dyadic discrete wavelet transform is utilized to allow translation invariance and low resolution enhancement capability. Two alternative nonlinear gain adjustments, piecewise linear and Gaussian form of gain adjustment are used for coefficient modification for the enhancement of subtle details such as microcalcification and low resolution edges of different lesion types. The results of comparing these methods of gain adjustment are presented. This comparing has done by defining new parameters for measuring quality of image based... 

Independent Component Analysis (ICA) can be used to decompose functional Magnetic Resonance Imaging (fMRI) data into a set of statistically independent images which are likely to be the sources of fMRI data. After applying ICA, a set of independent components are produced, and then, a "meaningful" subset from these components must be identified, because a large majority of components are non-interesting. So, interpreting the components is an important and also difficult task. In this paper, we propose a criterion based on the entropy of time courses to automatically select the components of interest. This method does not require to know the stimulus pattern of the experiment  

In this paper we will address two major problems in mammogram analysis for breast cancer in MR-images. The first is classification between normal and abnormal cases and then, discrimination between benign and malignant in cancerous cases. Our proposed method extracts textural and statistical descriptive features that are fed to a learning engine based on the use of Support Vector Machine learning framework to categorize them. The obtained results show excellent accuracy in both classification problems, that proves the appropriate combination of our features and selecting powerful classifier i.e. Support Vector Machine leads us to a brilliant outcome  

This paper proposes a new approach to watermarking multimedia products based on the combination of redundant discrete wavelet transform and independent component analysis. The original image is decomposed by RDWT, and watermark is embedded in to LL sub-band frequency according mixing model of the ICA, after that for enhancing the robustness of the watermark, the perceptual model is applied via stochastic approach for watermark adapting. This is based on computation of a noise visibility function (NVF) which has local image properties so the strength of watermarking is controllable. Principal component analysis (PCA) whitening process and FastICA techniques are introduced to ensure a blind... 

Due to the large volume required for medical images for transmission and archiving purposes, the compression of medical images is known as one of the main concepts of medical image processing. Lossless compression methods have the drawback of a low compression ratio. In contrast, lossy methods have a higher compression ratio and suffer from lower quality of the reconstructed images in the receiver. Recently, some selective compression methods have been proposed in which the main image is divided into two separate regions: Region of Interest (ROI), which should be compressed in a lossless manner, and Region of Background (ROB), which is compressed in a lossy manner with a lower quality. In... 

Prediction of the amino acids that have a catalytic effect on the enzymes is a major stage in appointing the activity of the enzymes and classification. The biological activity of a protein usually depends on the existence of a small number of amino acids. Recently, many algorithms have been proposed in the literature for finding these amino acids which are complex and time consuming. In this paper, we will introduce a new method for predicting the active sites that will use the spatial coordinates and the type of amino acids that contain the active sites. In order to increase the speed we use an approximate graph isomorphism algorithm. Furthermore, this algorithm allows us to find several... 

In this study, optimized cephalexin-loaded niosomal formulations based on span 60 and tween 60 were prepared as a promising drug carrier system. The niosomal formulations were characterized using a series of techniques such as scanning electron microscopy, Fourier transformed infrared spectroscopy, dynamic light scattering, and zeta potential measurement. The size and drug encapsulation efficiency are determined by the type and composition of surfactant. The developed niosomal formulations showed great storage stability up to 30 days with low change in size and drug entrapment during the storage, making them potential candidates for real applications. Moreover, the prepared niosomes showed...