Automated detection of brain abnormalities in neonatal hypoxia ischemic injury from MR images.

We compared the efficacy of three automated brain injury detection methods, namely symmetry-integrated region growing (SIRG), hierarchical region splitting (HRS) and modified watershed segmentation (MWS) in human and animal magnetic resonance imaging (MRI) datasets for the detection of hypoxic ischemic injuries (HIIs). Diffusion weighted imaging (DWI, 1.5T) data from neonatal arterial ischemic stroke (AIS) patients, as well as T2-weighted imaging (T2WI, 11.7T, 4.7T) at seven different time-points (1, 4, 7, 10, 17, 24 and 31 days post HII) in rat-pup model of hypoxic ischemic injury were used to assess the temporal efficacy of our computational approaches. Sensitivity, specificity, and similarity were used as performance metrics based on manual ('gold standard') injury detection to quantify comparisons. When compared to the manual gold standard, automated injury location results from SIRG performed the best in 62% of the data, while 29% for HRS and 9% for MWS. Injury severity detection revealed that SIRG performed the best in 67% cases while 33% for HRS. Prior information is required by HRS and MWS, but not by SIRG. However, SIRG is sensitive to parameter-tuning, while HRS and MWS are not. Among these methods, SIRG performs the best in detecting lesion volumes; HRS is the most robust, while MWS lags behind in both respects

Advanced Brain Tumour Segmentation from MRI Images

Magnetic resonance imaging (MRI) is widely used medical technology for diagnosis of various tissue abnormalities, detection of tumors. The active development in the computerized medical image segmentation has played a vital role in scientific research. This helps the doctors to take necessary treatment in an easy manner with fast decision making. Brain tumor segmentation is a hot point in the research field of Information technology with biomedical engineering. The brain tumor segmentation is motivated by assessing tumor growth, treatment responses, computer-based surgery, treatment of radiation therapy, and developing tumor growth models. Therefore, computer-aided diagnostic system is meaningful in medical treatments to reducing the workload of doctors and giving the accurate results. This chapter explains the causes, awareness of brain tumor segmentation and its classification, MRI scanning process and its operation, brain tumor classifications, and different segmentation methodologies

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain–behavior relationships after stroke

The goal of the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well‐powered meta‐ and mega‐analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large‐scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided

An Information Theoretic Approach For Feature Selection And Segmentation In Posterior Fossa Tumors

Posterior Fossa (PF) is a type of brain tumor located in or near brain stem and cerebellum. About 55% - 70 % pediatric brain tumors arise in the posterior fossa, compared with only 15% - 20% of adult tumors. For segmenting PF tumors we should have features to study the characteristics of tumors. In literature, different types of texture features such as Fractal Dimension (FD) and Multifractional Brownian Motion (mBm) have been exploited for measuring randomness associated with brain and tumor tissues structures, and the varying appearance of tissues in magnetic resonance images (MRI). For selecting best features techniques such as neural network and boosting methods have been exploited. However, neural network cannot descirbe about the properties of texture features. We explore methods such as information theroetic methods which can perform feature selection based on properties of texture features. The primary contribution of this dissertation is investigating efficacy of different image features such as intensity, fractal texture, and level - set shape in segmentation of PF tumor for pediatric patients. We explore effectiveness of using four different feature selection and three different segmentation techniques respectively to discriminate tumor regions from normal tissue in multimodal brain MRI. Our research suggest that Kullback - Leibler Divergence (KLD) measure for feature ranking and selection and Expectation Maximization (EM) algorithm for feature fusion and tumor segmentation offer the best performance for the patient data in this study. To improve segmentation accuracy, we need to consider abnormalities such as cyst, edema and necrosis which surround tumors. In this work, we exploit features which describe properties of cyst and technique which can be used to segment it. To achieve this goal, we extend the two class KLD techniques to multiclass feature selection techniques, so that we can effectively select features for tumor, cyst and non tumor tissues. We compute segemntation accuracy by computing number of pixels segemented to total number of pixels for the best features. For automated process we integrate the inhomoheneity correction, feature selection using KLD and segmentation in an integrated EM framework. To validate results we have used similarity coefficients for computing the robustness of segmented tumor and cyst