Medical image encryption techniques: a technical survey and potential challenges

Among the most sensitive and important data in telemedicine systems are medical images. It is necessary to use a robust encryption method that is resistant to cryptographic assaults while transferring medical images over the internet. Confidentiality is the most crucial of the three security goals for protecting information systems, along with availability, integrity, and compliance. Encryption and watermarking of medical images address problems with confidentiality and integrity in telemedicine applications. The need to prioritize security issues in telemedicine applications makes the choice of a trustworthy and efficient strategy or framework all the more crucial. The paper examines various security issues and cutting-edge methods to secure medical images for use with telemedicine systems

Machine Learning towards General Medical Image Segmentation

The quality of patient care associated with diagnostic radiology is proportionate to a physician\u27s workload. Segmentation is a fundamental limiting precursor to diagnostic and therapeutic procedures. Advances in machine learning aims to increase diagnostic efficiency to replace single applications with generalized algorithms. We approached segmentation as a multitask shape regression problem, simultaneously predicting coordinates on an object\u27s contour while jointly capturing global shape information. Shape regression models inherent point correlations to recover ambiguous boundaries not supported by clear edges and region homogeneity. Its capabilities was investigated using multi-output support vector regression (MSVR) on head and neck (HaN) CT images. Subsequently, we incorporated multiplane and multimodality spinal images and presented the first deep learning multiapplication framework for shape regression, the holistic multitask regression network (HMR-Net). MSVR and HMR-Net\u27s performance were comparable or superior to state-of-the-art algorithms. Multiapplication frameworks bridges any technical knowledge gaps and increases workflow efficiency

Discriminative Representations for Heterogeneous Images and Multimodal Data

Histology images of tumor tissue are an important diagnostic and prognostic tool for pathologists. Recently developed molecular methods group tumors into subtypes to further guide treatment decisions, but they are not routinely performed on all patients. A lower cost and repeatable method to predict tumor subtypes from histology could bring benefits to more cancer patients. Further, combining imaging and genomic data types provides a more complete view of the tumor and may improve prognostication and treatment decisions. While molecular and genomic methods capture the state of a small sample of tumor, histological image analysis provides a spatial view and can identify multiple subtypes in a single tumor. This intra-tumor heterogeneity has yet to be fully understood and its quantification may lead to future insights into tumor progression. In this work, I develop methods to learn appropriate features directly from images using dictionary learning or deep learning. I use multiple instance learning to account for intra-tumor variations in subtype during training, improving subtype predictions and providing insights into tumor heterogeneity. I also integrate image and genomic features to learn a projection to a shared space that is also discriminative. This method can be used for cross-modal classification or to improve predictions from images by also learning from genomic data during training, even if only image data is available at test time.Doctor of Philosoph

Statistical shape analysis for bio-structures : local shape modelling, techniques and applications

A Statistical Shape Model (SSM) is a statistical representation of a shape obtained from data to study variation in shapes. Work on shape modelling is constrained by many unsolved problems, for instance, difficulties in modelling local versus global variation. SSM have been successfully applied in medical image applications such as the analysis of brain anatomy. Since brain structure is so complex and varies across subjects, methods to identify morphological variability can be useful for diagnosis and treatment. The main objective of this research is to generate and develop a statistical shape model to analyse local variation in shapes. Within this particular context, this work addresses the question of what are the local elements that need to be identified for effective shape analysis. Here, the proposed method is based on a Point Distribution Model and uses a combination of other well known techniques: Fractal analysis; Markov Chain Monte Carlo methods; and the Curvature Scale Space representation for the problem of contour localisation. Similarly, Diffusion Maps are employed as a spectral shape clustering tool to identify sets of local partitions useful in the shape analysis. Additionally, a novel Hierarchical Shape Analysis method based on the Gaussian and Laplacian pyramids is explained and used to compare the featured Local Shape Model. Experimental results on a number of real contours such as animal, leaf and brain white matter outlines have been shown to demonstrate the effectiveness of the proposed model. These results show that local shape models are efficient in modelling the statistical variation of shape of biological structures. Particularly, the development of this model provides an approach to the analysis of brain images and brain morphometrics. Likewise, the model can be adapted to the problem of content based image retrieval, where global and local shape similarity needs to be measured

Developing and Applying Hybrid Deep Learning Models for Computer-Aided Diagnosis of Medical Image Data

The dissertation discusses three methods to address the challenges of applying deep learning models to medical imaging. The first method involves the development of a new joint deep learning model, J-Net, to achieve lesion segmentation and classification simultaneously. The J-Net model outperforms the individual models in accuracy with small datasets. The second method performs automatic image detection using a two-stage deep learning model to produce clean data. The third method involves developing multi-stage deep learning algorithms to generate synthetic medical image data, which can be used to overcome the lack of large, diverse datasets. These methods demonstrate that building enhanced training datasets can play a vital role in improving the performance of deep-learning models in medical imaging applications

MRI measures of brain integrity and their relation to processing speed in the elderly

A significant percentage of the elderly population experiences at least one geriatric disability. Previous studies have shown that geriatric disabilities are preceded by sub-clinical cognitive changes of aging and brain changes seen on magnetic resonance imaging (MRI). Decreased information processing speed has been identified as a common factor associated with age-related disabilities in gait, cognition, and mood. However, the current neurocognitive model of aging is incomplete; there remains uncertainty about the relationships between the different components of brain integrity and cognitive function. The goals of this dissertation are to characterize the relationships between different functional and structural MRI markers (for example: macro-structural, micro-structural, physiologic) with respect to cognitive aging and to improve the neuroimaging toolset for oldest old.We studied the relationship between functional MRI markers, structural MRI markers, and information processing speed in a sample of twenty-five healthy elderly subjects. We found that recruitment of fronto-parietal brain areas was associated with higher performance. Also, greater structural damage (white matter integrity) was associated with lower activation in prefrontal and anterior cingulate regions. In the presence of underlying brain connectivity structural abnormalities, additional posterior parietal activation was found to be important for maintaining higher task performance.MRI MEASURES OF BRAIN INTEGRITY AND THEIR RELATION TO PROCESSING SPEED IN THE ELDERLYVijay Krishna Venkatraman, Ph.D.University of Pittsburgh, 2010vWe also studied MRI measures of brain structure in a sample of 277 community-dwelling older adults free from neurological diseases. This study used a set of neuroimage analysis pathways optimized for the MRI images and examined the macro- and micro-structural indices. The results indicate that both the macro- and micro-structural MRI indices may provide complementary information on neuroanatomical correlates of information processing speed. The micro-structural MRI indices of white matter integrity were found to be the strongest correlate of information processing speed in this sample.While developing the image analysis pipelines for this dataset, we noticed that the diffusion tensor-imaging pathway was particularly sensitive to the approach of localizing the white matter tracts. We used both empirical and simulated datasets to confirm our hypothesis that the mean fractional anisotropy of the white matter tract is more sensitive to individual differences in the elderly when compared to a skeleton based approach