Kayvan Najarian

Big Data Analytics in Healthcare.

The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.

Maximizing strength of digital watermarks using neural networks

Several discrete wavelet transform (DWT) based techniques are used for watermarking digital images. Although these techniques are robust to some attacks, none of them is robust when a different set of parameters is used or some other attacks (such as low pass filtering) are applied. In order to make the watermark stronger and less susceptible to different types of attacks, it is essential to find the maximum amount of watermark before the watermark becomes visible. In this paper, neural networks are used to implement an automated system of creating maximum-strength watermarks.

Breast cancer detection in gadolinium‐enhanced MR images by static region descriptors and neural networks

To automate the diagnosis of malignancy by classifying breast tissues as negative or positive for malignancy in gadolinium‐enhanced dynamic magnetic resonance (MR) images, using static region descriptors and a neural network classifier.

Automated ventricular systems segmentation in brain CT images by combining low-level segmentation and high-level template matching

BackgroundAccurate analysis of CT brain scans is vital for diagnosis and treatment of Traumatic Brain Injuries (TBI). Automatic processing of these CT brain scans could speed up the decision making process, lower the cost of healthcare, and reduce the chance of human error. In this paper, we focus on automatic processing of CT brain images to segment and identify the ventricular systems. The segmentation of ventricles provides quantitative measures on the changes of ventricles in the brain that form vital diagnosis information.MethodsFirst all CT slices are aligned by detecting the ideal midlines in all images. The initial estimation of the ideal midline of the brain is found based on skull symmetry and then the initial estimate is further refined using detected anatomical features. Then a two-step method is used for ventricle segmentation. First a low-level segmentation on each pixel is applied on the CT images. For this step, both Iterated Conditional Mode (ICM) and Maximum A Posteriori Spatial Probability (MASP) are evaluated and compared. The second step applies template matching algorithm to identify objects in the initial low-level segmentation as ventricles. Experiments for ventricle segmentation are conducted using a relatively large CT dataset containing mild and severe TBI cases.ResultsExperiments show that the acceptable rate of the ideal midline detection is over 95%. Two measurements are defined to evaluate ventricle recognition results. The first measure is a sensitivity-like measure and the second is a false positive-like measure. For the first measurement, the rate is 100% indicating that all ventricles are identified in all slices. The false positives-like measurement is 8.59%. We also point out the similarities and differences between ICM and MASP algorithms through both mathematically relationships and segmentation results on CT images.ConclusionThe experiments show the reliability of the proposed algorithms. The novelty of the proposed method lies in its incorporation of anatomical features for ideal midline detection and the two-step ventricle segmentation method. Our method offers the following improvements over existing approaches: accurate detection of the ideal midline and accurate recognition of ventricles using both anatomical features and spatial templates derived from Magnetic Resonance Images.

A comparative analysis of multi-level computer-assisted decision making systems for traumatic injuries

BackgroundThis paper focuses on the creation of a predictive computer-assisted decision making system for traumatic injury using machine learning algorithms. Trauma experts must make several difficult decisions based on a large number of patient attributes, usually in a short period of time. The aim is to compare the existing machine learning methods available for medical informatics, and develop reliable, rule-based computer-assisted decision-making systems that provide recommendations for the course of treatment for new patients, based on previously seen cases in trauma databases. Datasets of traumatic brain injury (TBI) patients are used to train and test the decision making algorithm. The work is also applicable to patients with traumatic pelvic injuries.MethodsDecision-making rules are created by processing patterns discovered in the datasets, using machine learning techniques. More specifically, CART and C4.5 are used, as they provide grammatical expressions of knowledge extracted by applying logical operations to the available features. The resulting rule sets are tested against other machine learning methods, including AdaBoost and SVM. The rule creation algorithm is applied to multiple datasets, both with and without prior filtering to discover significant variables. This filtering is performed via logistic regression prior to the rule discovery process.ResultsFor survival prediction using all variables, CART outperformed the other machine learning methods. When using only significant variables, neural networks performed best. A reliable rule-base was generated using combined C4.5/CART. The average predictive rule performance was 82% when using all variables, and approximately 84% when using significant variables only. The average performance of the combined C4.5 and CART system using significant variables was 89.7% in predicting the exact outcome (home or rehabilitation), and 93.1% in predicting the ICU length of stay for airlifted TBI patients.ConclusionThis study creates an efficient computer-aided rule-based system that can be employed in decision making in TBI cases. The rule-bases apply methods that combine CART and C4.5 with logistic regression to improve rule performance and quality. For final outcome prediction for TBI cases, the resulting rule-bases outperform systems that utilize all available variables.

Biomedical Informatics for Computer-Aided Decision Support Systems: A Survey

The volumes of current patient data as well as their complexity make clinical decision making more challenging than ever for physicians and other care givers. This situation calls for the use of biomedical informatics methods to process data and form recommendations and/or predictions to assist such decision makers. The design, implementation, and use of biomedical informatics systems in the form of computer-aided decision support have become essential and widely used over the last two decades. This paper provides a brief review of such systems, their application protocols and methodologies, and the future challenges and directions they suggest.

Development and three-dimensional modelling of a biological-tissue grasper tool equipped with a tactile sensor

New surgical procedures, such as minimally invasive surgeries, separate the surgeons hands from the site of an operation. As a result, the surgeons perception of touch is limited to his/her visual abilities fed back from a video camera located at the end of an endoscope. To compensate for this restriction, this study investigates the design, fabrication, testing, and mathematical modelling of an endoscopic grasper tool in which a polyvinylidene fluoride (PVDF) tactile sensor is incorporated. The entire surface of the grasper jaw is active and can be utilized as a means of detecting the magnitude and application point of the applied force. This is in contrast to array-type tactile sensors, in which the areas located between the adjacent sensing elements are inactive. The sensor assembly consists of three distinct layers. The lower layer is made of Plexiglas, while the upper layer is made from micromachined silicon. PVDF film is placed between the upper and lower layers and forms the middle layer. Three-dimensional finite element modelling is used to analyze the performance of the designed system. Good correspondence is obtained between the experimental data and the modelling predictions.

Efficient segmentation framework of cell images in noise environments

We propose an efficient segmentation method that exploits local information for automated cell segmentation. This method introduces a new criterion function based on statistical structure of the objects in cell image. Each pixel is initially assigned to the most probable region and then the pixel assignment process is iteratively updated by a new criterion function until steady state is reached. We apply the proposed method to cervical cell images as well as the corresponding noisy images that are contaminated by Gaussian noise. The performance of the proposed method is evaluated based on the results from both normal and noisy cell images.

Fracture detection in traumatic pelvic CT images

Fracture detection in pelvic bones is vital for patient diagnostic decisions and treatment planning in traumatic pelvic injuries. Manual detection of bone fracture from computed tomography (CT) images is very challenging due to low resolution of the images and the complex pelvic structures. Automated fracture detection from segmented bones can significantly help physicians analyze pelvic CT images and detect the severity of injuries in a very short period. This paper presents an automated hierarchical algorithm for bone fracture detection in pelvic CT scans using adaptive windowing, boundary tracing, and wavelet transform while incorporating anatomical information. Fracture detection is performed on the basis of the results of prior pelvic bone segmentation via our registered active shape model (RASM). The results are promising and show that the method is capable of detecting fractures accurately.

Detection of P, QRS, and T Components of ECG using wavelet transformation

Electrocardiogram (ECG) signals are composed of five important waves: P, Q, R, S, and T. Sometimes, a sixth wave (U) may follow T. Q, R, and S are grouped together to form the QRS-complex. Detection of these waves is a vital step in ECG signal analysis to extract hidden patterns. Many prior studies have focused only on detection of the QRS-complex, because P and T waves are sparse and harder to isolate from the signal. In this paper, we develop an algorithm to detect all five waves - P, Q, R, S, and T in ECG signals using wavelet transformation. The accuracy for P wave detection is 99.5%, 99.8% for QRS complex, and 99.2% for T waves.