Mahdi Ramezani

Towards Genetic Feature Selection in Image Steganalysis

In this study, a new feature-based steganalytic method is presented and four classification methods: Fisher Linear Discriminant, Gaussian naive Bayes, Multilayer perceptron, and k nearest neighbor, are compared for steganalysis of suspicious images. The method exploits statistics of the histogram, wavelet statistics, amplitudes of local extrema from the 1D and 2D adjacency histograms, center of mass of the histogram characteristic function and co-occurrence matrices for feature extraction process. In order to reduce the proposed features dimension and select the best subset, genetic algorithm is used and the results are compared through principle component analysis and linear discriminant analysis. The results show that the proposed method achieves higher accuracy in discriminating between innocent and stego images, as compared to one of well-known image steganalysis schemes.

Joint Sparse Representation of Brain Activity Patterns in Multi-Task fMRI Data

A single-task functional magnetic resonance imaging (fMRI) experiment may only partially highlight alterations to functional brain networks affected by a particular disorder. Multivariate analysis across multiple fMRI tasks may increase the sensitivity of fMRI-based diagnosis. Prior research using multi-task analysis in fMRI, such as those that use joint independent component analysis (jICA), has mainly assumed that brain activity patterns evoked by different tasks are independent. This may not be valid in practice. Here, we use sparsity, which is a natural characteristic of fMRI data in the spatial domain, and propose a joint sparse representation analysis (jSRA) method to identify common information across different functional subtraction (contrast) images in data from a multi-task fMRI experiment. Sparse representation methods do not require independence, or that the brain activity patterns be nonoverlapping. We use functional subtraction images within the joint sparse representation analysis to generate joint activation sources and their corresponding sparse modulation profiles. We evaluate the use of sparse representation analysis to capture individual differences with simulated fMRI data and with experimental fMRI data. The experimental fMRI data was acquired from 16 young (age: 19-26) and 16 older (age: 57-73) adults obtained from multiple speech comprehension tasks within subjects, where an independent measure (namely, age in years) can be used to differentiate between groups. Simulation results show that this method yields greater sensitivity, precision, and higher Jaccard indexes (which measures similarity and diversity of the true and estimated brain activation sources) than does the jICA method. Moreover, superiority of the jSRA method in capturing individual differences was successfully demonstrated using experimental fMRI data.

Learning-Based Multi-Label Segmentation of Transrectal Ultrasound Images for Prostate Brachytherapy

Low-dose-rate prostate brachytherapy treatment takes place by implantation of small radioactive seeds in and sometimes adjacent to the prostate gland. A patient specific target anatomy for seed placement is usually determined by contouring a set of collected transrectal ultrasound images prior to implantation. Standard-of-care in prostate brachytherapy is to delineate the clinical target anatomy, which closely follows the real prostate boundary. Subsequently, the boundary is dilated with respect to the clinical guidelines to determine a planning target volume. Manual contouring of these two anatomical targets is a tedious task with relatively high observer variability. In this work, we aim to reduce the segmentation variability and planning time by proposing an efficient learning-based multi-label segmentation algorithm. We incorporate a sparse representation approach in our methodology to learn a dictionary of sparse joint elements consisting of images, and clinical and planning target volume segmentation. The generated dictionary inherently captures the relationships among elements, which also incorporates the institutional clinical guidelines. The proposed multi-label segmentation method is evaluated on a dataset of 590 brachytherapy treatment records by 5-fold cross validation. We show clinically acceptable instantaneous segmentation results for both target volumes.

Ultrasound-Based Characterization of Prostate Cancer Using Joint Independent Component Analysis

Objective: This paper presents the results of a new approach for selection of RF time series features based on joint independent component analysis for in vivo characterization of prostate cancer. Methods: We project three sets of RF time series features extracted from the spectrum, fractal dimension, and the wavelet transform of the ultrasound RF data on a space spanned by five joint independent components. Then, we demonstrate that the obtained mixing coefficients from a group of patients can be used to train a classifier, which can be applied to characterize cancerous regions of a test patient. Results: In a leave-one-patient-out cross validation, an area under receiver operating characteristic curve of 0.93 and classification accuracy of 84% are achieved. Conclusion: Ultrasound RF time series can be used to accurately characterize prostate cancer, in vivo without the need for exhaustive search in the feature space. Significance: We use joint independent component analysis for systematic fusion of multiple sets of RF time series features, within a machine learning framework, to characterize PCa in an in vivo study.

Adaptive Image Steganography with Mod-4 Embedding Using Image Contrast

A new adaptive steganography method based on image contrast to improve the embedding capacity and imperceptibility of the stego images is presented. The method exploits the average difference between the gray level values of the pixels in 2×2 blocks of non-overlapping spatially and their mean gray level in order to select valid blocks for embedding. The method was tested on different gray scale images. Results show that our proposed approach provides larger embedding capacity, while being less detectable by steganalysis methods such as χ2 attack and machine learning steganalysis systems, as compared to some well-known adaptive and non-adaptive steganography algorithms.

Fusion analysis of functional MRI data for classification of individuals based on patterns of activation

Classification of individuals based on patterns of brain activity observed in functional MRI contrasts may be helpful for diagnosis of neurological disorders. Prior work for classification based on these patterns have primarily focused on using a single contrast, which does not take advantage of complementary information that may be available in multiple contrasts. Where multiple contrasts are used, the objective has been only to identify the joint, distinct brain activity patterns that differ between groups of subjects; not to use the information to classify individuals. Here, we use joint Independent Component Analysis (jICA) within a Support Vector Machine (SVM) classification method, and take advantage of the relative contribution of activation patterns generated from multiple fMRI contrasts to improve classification accuracy. Young (age: 19–26) and older (age: 57–73) adults (16 each) were scanned while listening to noise alone and to speech degraded with noise, half of which contained meaningful context that could be used to enhance intelligibility. Functional contrasts based on these conditions (and a silent baseline condition) were used within jICA to generate spatially independent joint activation sources and their corresponding modulation profiles. Modulation profiles were used within a non-linear SVM framework to classify individuals as young or older. Results demonstrate that a combination of activation maps across the multiple contrasts yielded an area under ROC curve of 0.86, superior to classification resulting from individual contrasts. Moreover, class separability, measured by a divergence criterion, was substantially higher when using the combination of activation maps.

Temporal-lobe morphology differs between healthy adolescents and those with early-onset of depression

Major depressive disorder (MDD) has previously been linked to structural changes in several brain regions, particularly in the medial temporal lobes (Bellani, Baiano, Brambilla, 2010; Bellani, Baiano, Brambilla, 2011). This has been determined using voxel-based morphometry, segmentation algorithms, and analysis of shape deformations (Bell-McGinty et al., 2002; Bergouignan et al., 2009; Posener et al., 2003; Vasic et al., 2008; Zhao et al., 2008): these are methods in which information related to the shape and the pose (the size, and anatomical position and orientation) of structures is lost. Here, we incorporate information about shape and pose to measure structural deformation in adolescents and young adults with and without depression (as measured using the Beck Depression Inventory and Diagnostic and Statistical Manual of Mental Disorders criteria). As a hypothesis-generating study, a significance level of p < 0.05, uncorrected for multiple comparisons, was used, so that subtle morphological differences in brain structures between adolescent depressed individuals and control participants could be identified. We focus on changes in cortical and subcortical temporal structures, and use a multi-object statistical pose and shape model to analyze imaging data from 16 females (aged 16–21) and 3 males (aged 18) with early-onset MDD, and 25 female and 1 male normal control participants, drawn from the same age range. The hippocampus, parahippocampal gyrus, putamen, and superior, inferior and middle temporal gyri in both hemispheres of the brain were automatically segmented using the LONI Probabilistic Brain Atlas (Shattuck et al., 2008) in MNI space. Points on the surface of each structure in the atlas were extracted and warped to each participants structural MRI. These surface points were analyzed to extract the pose and shape features. Pose differences were detected between the two groups, particularly in the left and right putamina, right hippocampus, and left and right inferior temporal gyri. Shape differences were detected between the two groups, particularly in the left hippocampus and in the left and right parahippocampal gyri. Furthermore, pose measures were significantly correlated with BDI score across the whole (clinical and control) sample. Since the clinical participants were experiencing their very first episodes of MDD, morphological alteration in the medial temporal lobe appears to be an early sign of MDD, and is unlikely to result from treatment with antidepressants. Pose and shape measures of morphology, which are not usually analyzed in neuromorphometric studies, appear to be sensitive to depressive symptomatology.

Automatic Localization of the da Vinci Surgical Instrument Tips in 3-D Transrectal Ultrasound

Robot-assisted laparoscopic radical prostatectomy (RALRP) using the da Vinci surgical system is the current state-of-the-art treatment option for clinically confined prostate cancer. Given the limited field of view of the surgical site in RALRP, several groups have proposed the integration of transrectal ultrasound (TRUS) imaging in the surgical workflow to assist with accurate resection of the prostate and the sparing of the neurovascular bundles (NVBs). We previously introduced a robotic TRUS manipulator and a method for automatically tracking da Vinci surgical instruments with the TRUS imaging plane, in order to facilitate the integration of intraoperative TRUS in RALRP. Rapid and automatic registration of the kinematic frames of the da Vinci surgical system and the robotic TRUS probe manipulator is a critical component of the instrument tracking system. In this paper, we propose a fully automatic registration technique based on automatic 3-D TRUS localization of robot instrument tips pressed against the air-tissue boundary anterior to the prostate. The detection approach uses a multiscale filtering technique to identify and localize surgical instrument tips in the TRUS volume, and could also be used to detect other surface fiducials in 3-D ultrasound. Experiments have been performed using a tissue phantom and two ex vivo tissue samples to show the feasibility of the proposed methods. Also, an initial in vivo evaluation of the system has been carried out on a live anaesthetized dog with a da Vinci Si surgical system and a target registration error (defined as the root mean square distance of corresponding points after registration) of 2.68 mm has been achieved. Results show this methods accuracy and consistency for automatic registration of TRUS images to the da Vinci surgical system.

Joint Sparse Representation of Brain Activity Patterns Related to Perceptual and Cognitive Components of a Speech Comprehension Task

Neurological disorders that affect brain structure, function and networks would substantially benefit from developing new techniques that combine multi-modal and/or multi-task information. Here, we propose a Joint Sparse Representation Analysis method to identify common information across different functional contrasts in data from an fMRI experiment. We evaluate the use of a sparse representation analysis method within a Fisher Linear Discriminant (FLD) classification to classify individuals as young or older, based only on functional activation patterns in a speech listening task. Sixteen young (age: 19-26) and 16 older (age: 57-73) adults were scanned while listening to noise and to sentences degraded with noise, half of which contained meaningful context which is known to enhance intelligibility. Functional contrast images representing different perceptual and cognitive components of speech perception (i.e., auditory perception; speech perception, use of context to enhance perception) were used within the joint sparse representation analysis to generate basis activation sources and their corresponding sparse modulation profiles. Sparse modulation profiles were used to classify individuals into the young and older categories. Results demonstrate that a combination of functional contrast images yielded excellent classification performance.

Automatic detection and localization of da vinci tool tips in 3d ultrasound

Radical prostatectomy (RP) is viewed by many as the gold standard treatment for clinically localized prostate cancer. State of the art radical prostatectomy involves the da Vinci surgical system, a laparoscopic robot which provides the surgeon with excellent 3D visualization of the surgical site and improved dexterity over standard laparoscopic instruments. Given the limited field of view of the surgical site in Robot-Assisted Laparoscopic Radical Prostatectomy (RALRP), several groups have proposed the integration of Transrectal Ultrasound (TRUS) imaging in the surgical work flow to assist with the resection of prostate and sparing the Neuro-Vascular Bundle (NVB). Rapid and automatic registration of TRUS imaging coordinates to the da Vinci tools or camera is a critical component of this integration. We propose a fully automatic registration technique based on accurate and automatic localization of robot tool tips pressed against the air-tissue boundary of the prostate, in 3D TRUS. The detection approach uses a multi-scale filtering technique to uniquely identify and localize the tool tip in the ultrasound volume and could also be used to detect other surface fiducials in 3D ultrasound. Feasibility experiments using a phantom and two ex vivo tissue samples yield promising results with target registration error (defined as the root mean square distance of corresponding points after registration) of (