Mohammad-Reza Siadat

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We have designed and implemented a human brain multi-modality database system with content-based image management, navigation and retrieval support for epilepsy. The system consists of several modules including a database backbone, brain structure identification and localization, segmentation, registration, visual feature extraction,(More)
This paper presents our recent study to evaluate how effectively the image texture information within the hippocampus structure can help the physicians to determine the candidates for epilepsy surgery. First we segment the hippocampus from T1-weighted images using our newly developed knowledge-based segmentation method. To extract the texture features we(More)
Big data technologies are critical to the medical field which requires new frameworks to leverage them. Such frameworks would benefit medical experts to test hypotheses by querying huge volumes of unstructured medical data to provide better patient care. The objective of this work is to implement and examine the feasibility of having such a framework to(More)
Longitudinal data for studying urinary incontinence (UI) risk factors are rare. Data from one study, the hallmark Medical, Epidemiological, and Social Aspects of Aging (MESA), have been analyzed in the past; however, repeated measures analyses that are crucial for analyzing longitudinal data have not been applied. We tested a novel application of(More)
Essential information is often conveyed in illustrations in biomedical publications. A clinician's decision to access the full text when searching for evidence in support of clinical decision is frequently based solely on a short bibliographic reference. We seek to automatically augment these references with images from the article that may assist in(More)
This paper is focused on the human factors analysis comparing a standard neuronavigation system with an augmented reality system. We use a passive articulated arm (Microscribe, Immersion technology) to track a calibrated end-effector mounted video camera. In real time, we superimpose the live video view with the synchronized graphical view of CT-derived(More)
Gene expression profile classification is a pivotal research domain assisting in the transformation from traditional to personalized medicine. A major challenge associated with gene expression data classification is the small number of samples relative to the large number of genes. To address this problem, researchers have devised various feature selection(More)
OBJECTIVE This paper is focused on prototype development and accuracy evaluation of a medical Augmented Reality (AR) system. The accuracy of such a system is of critical importance for medical use, and is hence considered in detail. We analyze the individual error contributions and the system accuracy of the prototype. MATERIALS AND METHODS A passive(More)
We present a novel and efficient method for localization of human brain structures such as hippocampus. Landmark localization is important for segmentation and registration. This method follows a statistical roadmap, consisting of anatomical landmarks, to reach the desired structures. Using a set of desired and undesired landmarks, identified on a training(More)