A Novel Two-Stage Approach For Automatic Detection of Brain Tumor

Brain tumor is one of the most life-threatening diseases, and it is the most common type of cancer that occurs among those in the age group belonging to 0-19. It is also a major cause of cancer-related deaths in children (males and females) under age 20 hence its detection should be fast and accurate. Manual detection of brain tumors using MRI scan images is effective but time-consuming. Many automation techniques and algorithms for detection of brain tumors are being proposed recently. In this paper, we propose an integrated two-step approach combining modified K-means clustering algorithm and Hierarchical Centroid Shape Descriptor (HCSD). The images are clustered using modified K-means based on pixel intensity, and then HCSD helps to select those having a specific shape thus making this approach more effective and reliable. Simulation of the proposed work is done in MATLAB R2013a. Tests are carried out on T1 weighted MRI scan images

Brain extraction using the watershed transform from markers

Isolation of the brain from other tissue types in magnetic resonance (MR) images is an important step in many types of neuro-imaging research using both humans and animal subjects. The importance of brain extraction is well appreciated—numerous approaches have been published and the benefits of good extraction methods to subsequent processing are well known. We describe a tool—the marker based watershed scalper (MBWSS)—for isolating the brain in T1-weighted MR images built using filtering and segmentation components from the Insight Toolkit (ITK) framework. The key elements of MBWSS—the watershed transform from markers and aggressive filtering with large kernels—are techniques that have rarely been used in neuroimaging segmentation applications. MBWSS is able to reliably isolate the brain without expensive preprocessing steps, such as registration to an atlas, and is therefore useful as the first stage of processing pipelines. It is an informative example of the level of accuracy achievable without using priors in the form of atlases, shape models or libraries of examples. We validate the MBWSS using a publicly available dataset, a paediatric cohort, an adolescent cohort, intra-surgical scans and demonstrate flexibility of the approach by modifying the method to extract macaque brains

A Novel Approach for Manual Segmentation of the Amygdala and Hippocampus in Neonate MRI

The gross anatomy of the infant brain at term is fairly similar to that of the adult brain, but structures are immature, and the brain undergoes rapid growth during the first 2 years of life. Neonate magnetic resonance (MR) images have different contrasts compared to adult images, and automated segmentation of brain magnetic resonance imaging (MRI) can thus be considered challenging as less software options are available. Despite this, most anatomical regions are identifiable and thus amenable to manual segmentation. In the current study, we developed a protocol for segmenting the amygdala and hippocampus in T2-weighted neonatal MR images. The participants were 31 healthy infants between 2 and 5 weeks of age. Intra-rater reliability was measured in 12 randomly selected MR images, where 6 MR images were segmented at 1-month intervals between the delineations, and another 6 MR images at 6-month intervals. The protocol was also tested by two independent raters in 20 randomly selected T2-weighted images, and finally with T1 images. Intraclass correlation coefficient (ICC) and Dice similarity coefficient (DSC) for intra-rater, inter-rater, and T1 vs. T2 comparisons were computed. Moreover, manual segmentations were compared to automated segmentations performed by iBEAT toolbox in 10 T2-weighted MR images. The intra-rater reliability was high ICC >= 0.91, DSC >= 0.89, the inter-rater reliabilities were satisfactory ICC >= 0.90, DSC >= 0.75 for hippocampus and DSC >= 0.52 for amygdalae. Segmentations for T1 vs. T2-weighted images showed high consistency ICC >= 0.90, DSC >= 0.74. The manual and iBEAT segmentations showed no agreement, DSC >= 0.39. In conclusion, there is a clear need to improve and develop the procedures for automated segmentation of infant brain MR images

Knowledge-Guided Robust MRI Brain Extraction for Diverse Large-Scale Neuroimaging Studies on Humans and Non-Human Primates

Accurate and robust brain extraction is a critical step in most neuroimaging analysis pipelines. In particular, for the large-scale multi-site neuroimaging studies involving a significant number of subjects with diverse age and diagnostic groups, accurate and robust extraction of the brain automatically and consistently is highly desirable. In this paper, we introduce population-specific probability maps to guide the brain extraction of diverse subject groups, including both healthy and diseased adult human populations, both developing and aging human populations, as well as non-human primates. Specifically, the proposed method combines an atlas-based approach, for coarse skull-stripping, with a deformable-surface-based approach that is guided by local intensity information and population-specific prior information learned from a set of real brain images for more localized refinement. Comprehensive quantitative evaluations were performed on the diverse large-scale populations of ADNI dataset with over 800 subjects (55 approximately 90 years of age, multi-site, various diagnosis groups), OASIS dataset with over 400 subjects (18 approximately 96 years of age, wide age range, various diagnosis groups), and NIH pediatrics dataset with 150 subjects (5 approximately 18 years of age, multi-site, wide age range as a complementary age group to the adult dataset). The results demonstrate that our method consistently yields the best overall results across almost the entire human life span, with only a single set of parameters. To demonstrate its capability to work on non-human primates, the proposed method is further evaluated using a rhesus macaque dataset with 20 subjects. Quantitative comparisons with popularly used state-of-the-art methods, including BET, Two-pass BET, BET-B, BSE, HWA, ROBEX and AFNI, demonstrate that the proposed method performs favorably with superior performance on all testing datasets, indicating its robustness and effectiveness.published_or_final_versio

Segmentación automática de tejido cerebral en imagen preclínica

En estudios preclínicos neurológicos de imagen de resonancia magnética (MRI) en pequeños animales es común el uso de la segmentación cerebral para su posterior análisis volumétrico y/o registro con otras modalidades de imagen. Este proceso suele realizarse de forma manual, por lo que a menudo se emplea una gran cantidad de tiempo dependiendo del estudio. En este trabajo se propone un nuevo método de segmentación automática basado en registro para facilitar dicho proceso. La propuesta se ha comparado con dos métodos: segmentación manual, que se emplea como referencia, y una segmentación basada en PCNN (Pulse Couple Neural Network) propuesta específicamente para imágenes de rata. El método propuesto consigue buenos resultados en índice de solapamiento y volumen cerebral comparado con el manual, y ofrece además una reducción considerable en el tiempo de ejecución comparado con PCNN.Ingeniería Técnica en Sistemas de Telecomunicació

Сегментация изображений анатомической структуры сердца

Изучение методов сегментации медицинских изображений, формирование последовательности алгоритмов предобработки медицинских изображений, позволяющей на основе реализации предложенных методов сегментации повысить точность обработки изображений, и проведение статистического анализа для проверки достоверности результатов.Studying the medical images segmentation methods, the formation of algorithms sequence for the medical images preprocessing, which allows to increase the accuracy of image processing based on the implementation of the proposed segmentation methods, and statistical analysis to verify the reliability of the results

Probabilistic partial volume modelling of biomedical tomographic image data

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