766 research outputs found

    Brain Tumor Detection and Segmentation in Multisequence MRI

    Get PDF
    Tato práce se zabývá detekcí a segmentací mozkového nádoru v multisekvenčních MR obrazech se zaměřením na gliomy vysokého a nízkého stupně malignity. Jsou zde pro tento účel navrženy tři metody. První metoda se zabývá detekcí prezence částí mozkového nádoru v axiálních a koronárních řezech. Jedná se o algoritmus založený na analýze symetrie při různých rozlišeních obrazu, který byl otestován na T1, T2, T1C a FLAIR obrazech. Druhá metoda se zabývá extrakcí oblasti celého mozkového nádoru, zahrnující oblast jádra tumoru a edému, ve FLAIR a T2 obrazech. Metoda je schopna extrahovat mozkový nádor z 2D i 3D obrazů. Je zde opět využita analýza symetrie, která je následována automatickým stanovením intenzitního prahu z nejvíce asymetrických částí. Třetí metoda je založena na predikci lokální struktury a je schopna segmentovat celou oblast nádoru, jeho jádro i jeho aktivní část. Metoda využívá faktu, že většina lékařských obrazů vykazuje vysokou podobnost intenzit sousedních pixelů a silnou korelaci mezi intenzitami v různých obrazových modalitách. Jedním ze způsobů, jak s touto korelací pracovat a používat ji, je využití lokálních obrazových polí. Podobná korelace existuje také mezi sousedními pixely v anotaci obrazu. Tento příznak byl využit v predikci lokální struktury při lokální anotaci polí. Jako klasifikační algoritmus je v této metodě použita konvoluční neuronová síť vzhledem k její známe schopnosti zacházet s korelací mezi příznaky. Všechny tři metody byly otestovány na veřejné databázi 254 multisekvenčních MR obrazech a byla dosáhnuta přesnost srovnatelná s nejmodernějšími metodami v mnohem kratším výpočetním čase (v řádu sekund při použitý CPU), což poskytuje možnost manuálních úprav při interaktivní segmetaci.This work deals with the brain tumor detection and segmentation in multisequence MR images with particular focus on high- and low-grade gliomas. Three methods are propose for this purpose. The first method deals with the presence detection of brain tumor structures in axial and coronal slices. This method is based on multi-resolution symmetry analysis and it was tested for T1, T2, T1C and FLAIR images. The second method deals with extraction of the whole brain tumor region, including tumor core and edema, in FLAIR and T2 images and is suitable to extract the whole brain tumor region from both 2D and 3D. It also uses the symmetry analysis approach which is followed by automatic determination of the intensity threshold from the most asymmetric parts. The third method is based on local structure prediction and it is able to segment the whole tumor region as well as tumor core and active tumor. This method takes the advantage of a fact that most medical images feature a high similarity in intensities of nearby pixels and a strong correlation of intensity profiles across different image modalities. One way of dealing with -- and even exploiting -- this correlation is the use of local image patches. In the same way, there is a high correlation between nearby labels in image annotation, a feature that has been used in the ``local structure prediction'' of local label patches. Convolutional neural network is chosen as a learning algorithm, as it is known to be suited for dealing with correlation between features. All three methods were evaluated on a public data set of 254 multisequence MR volumes being able to reach comparable results to state-of-the-art methods in much shorter computing time (order of seconds running on CPU) providing means, for example, to do online updates when aiming at an interactive segmentation.

    AUTOMATIC BRAIN TUMOR SEGMENTATION WITH K-MEANS, FUZZY C-MEANS, SELF-ORGANIZING MAP AND OTSU METHODS

    Get PDF
    AutomatIc BraIn Tumor SegmentatIon wIth K-Means, Fuzzy C-Means, Self-Organizing Map and Otsu MethodsAbstractThe human brain is an amazing organ of the human nervous system and controls all functions of our body. Brain tumors emerge from a mass of abnormal cells in the brain, and catching tumors early often allows for more treatment options. For diagnosing brain tumors, it has been benefited mostly from magnetic resonance images. In this study, we have developed the segmentation systems using the methods as K-Means, Fuzzy C-Means, Self-Organizing Map, Otsu, and the hybrid method of them, and evaluated the methods according to their success rates of segmentation. The developed systems, which take the brain image of MRI as input, perform skull stripping, preprocessing, and segmentation is performed using the clustering algorithms as K-Means, Fuzzy C-Means, Self-Organizing Map and Otsu Methods. Before preprocessing, the skull region is removed from the images in the MRI brain image data set. In preprocessing, the quality of the brain images is enhanced and the noise of the images is removed by some various filtering and morphological techniques. Finally, with the clustering and thresholding techniques, the tumor area of the brain is detected, and then the systems of the segmentation have been evaluated and compared with each other according to accuracy, true positive rate, and true negative rate.Keywords: Brain Tumor Segmentation, Medical Imaging, Fuzzy C-Means, K-Means, Self-Organizing Map, Otsu MethodBulanık C-Ortalamalar, K-Ortalamalar, Özdüzenlemelİ Ağ VE Otsu Metot İLE BEYİN TÜMÖRÜ SEGMENTASYONU Özetİnsan beyni, insan sinir sisteminin en önemli organıdır ve vücudumuzun tamamını kontrol eder. Beyin tümörleri beyindeki normal olmayan hücrelerden oluşur ve tümörleri erken tespit etmek birçok tedavi seçeneklerinin uygulanmasına olanak sağlar. Beyin tümörlerinin teşhisi için çoğunlukla manyetik rezonans görüntülerinden yararlanılmıştır. Bu çalışmada, Bulanık C-Ortalamalar, K-Ortalamalar, Özdüzenlemeli Ağ, Otsu Metot ve bu metotların birleşiminden oluşan hibrid metotlar kullanılarak beyin tümör segmentasyon sistemleri geliştirilmiştir. Bu metotların segmentasyon başarı oranları tespit edilmiş ve birbirleriyle karşılaştırılmıştır. Geliştirilen sistemlerde, ilk olarak MRI beyin görüntülerini girdi olarak alınır, sonra kafatası bölgesinin görüntüden ayrılması, önişleme ve Bulanık C-Ortalamalar, K-Ortalamalar, Özdüzenlemeli Ağ, Otsu metot gibi algoritmalarla segmentasyon işlemleri uygulanır. Önişlemden önce, kafatası bölgesi, MRI beyin görüntüsü veri setindeki görüntülerden çıkarılır. Ön işlemede, beyin görüntülerinin kalitesi iyileştirilir ve görüntülerin gürültüsü, çeşitli filtreleme ve morfolojik tekniklerle kaldırılır. Son olarak, kümeleme ve eşikleme teknikleri ile beynin tümör bölgesi tespit edildi. Daha sonra, segmentasyon sistemleri değerlendirildi ve doğruluk, gerçek pozitif oranı ve gerçek negatif oranına göre birbirleriyle karşılaştırıldı.Anahtar Kelimeler: Beyin Tümörü Segmentasyonu, Tıbbi Görüntüleme, Bulanık C-Ortalamalar, K-Ortalamalar, Özdüzenlemeli Ağ, Otsu Meto

    Advanced Brain Tumour Segmentation from MRI Images

    Get PDF
    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

    Glioblastomas brain tumour segmentation based on convolutional neural networks

    Get PDF
    Brain tumour segmentation can improve diagnostics efficiency, rise the prediction rate and treatment planning. This will help the doctors and experts in their work. Where many types of brain tumour may be classified easily, the gliomas tumour is challenging to be segmented because of the diffusion between the tumour and the surrounding edema. Another important challenge with this type of brain tumour is that the tumour may grow anywhere in the brain with different shape and size. Brain cancer presents one of the most famous diseases over the world, which encourage the researchers to find a high-throughput system for tumour detection and classification. Several approaches have been proposed to design automatic detection and classification systems. This paper presents an integrated framework to segment the gliomas brain tumour automatically using pixel clustering for the MRI images foreground and background and classify its type based on deep learning mechanism, which is the convolutional neural network. In this work, a novel segmentation and classification system is proposed to detect the tumour cells and classify the brain image if it is healthy or not. After collecting data for healthy and non-healthy brain images, satisfactory results are found and registered using computer vision approaches. This approach can be used as a part of a bigger diagnosis system for breast tumour detection and manipulation

    A generative approach for image-based modeling of tumor growth

    Get PDF
    22nd International Conference, IPMI 2011, Kloster Irsee, Germany, July 3-8, 2011. ProceedingsExtensive imaging is routinely used in brain tumor patients to monitor the state of the disease and to evaluate therapeutic options. A large number of multi-modal and multi-temporal image volumes is acquired in standard clinical cases, requiring new approaches for comprehensive integration of information from different image sources and different time points. In this work we propose a joint generative model of tumor growth and of image observation that naturally handles multi-modal and longitudinal data. We use the model for analyzing imaging data in patients with glioma. The tumor growth model is based on a reaction-diffusion framework. Model personalization relies only on a forward model for the growth process and on image likelihood. We take advantage of an adaptive sparse grid approximation for efficient inference via Markov Chain Monte Carlo sampling. The approach can be used for integrating information from different multi-modal imaging protocols and can easily be adapted to other tumor growth models.German Academy of Sciences Leopoldina (Fellowship Programme LPDS 2009-10)Academy of Finland (133611)National Institutes of Health (U.S.) (NIBIB NAMIC U54-EB005149)National Institutes of Health (U.S.) (NCRR NAC P41- RR13218)National Institutes of Health (U.S.) (NINDS R01-NS051826)National Institutes of Health (U.S.) (NIH R01-NS052585)National Institutes of Health (U.S.) (NIH R01-EB006758)National Institutes of Health (U.S.) (NIH R01-EB009051)National Institutes of Health (U.S.) (NIH P41-RR014075)National Science Foundation (U.S.) (CAREER Award 0642971
    • …
    corecore