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

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

Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders

Lesion detection in brain Magnetic Resonance Images (MRI) remains a challenging task. State-of-the-art approaches are mostly based on supervised learning making use of large annotated datasets. Human beings, on the other hand, even non-experts, can detect most abnormal lesions after seeing a handful of healthy brain images. Replicating this capability of using prior information on the appearance of healthy brain structure to detect lesions can help computers achieve human level abnormality detection, specifically reducing the need for numerous labeled examples and bettering generalization of previously unseen lesions. To this end, we study detection of lesion regions in an unsupervised manner by learning data distribution of brain MRI of healthy subjects using auto-encoder based methods. We hypothesize that one of the main limitations of the current models is the lack of consistency in latent representation. We propose a simple yet effective constraint that helps mapping of an image bearing lesion close to its corresponding healthy image in the latent space. We use the Human Connectome Project dataset to learn distribution of healthy-appearing brain MRI and report improved detection, in terms of AUC, of the lesions in the BRATS challenge dataset.Comment: 9 pages, 5 figures, accepted at MIDL 201

Augmented MRI Images for Classification of Normal and Tumors Brain through Transfer Learning Techniques

A brain tumor is a severe malignant condition caused by uncontrolled and abnormal cell division. Recent advances in deep learning have aided the health business in Medical Imaging for the diagnosis of numerous disorders. The most frequent and widely used deep learning algorithm for visual learning and image recognition. This research seeks to multi-classification tumors in the brain from images attained by Magnetic Resonance Imaging (MRI) using deep learning models that have been pre-trained for transfer learning. As per the publicly available MRI brain tumor dataset, brain tumors identified as glioma, meningioma, and pituitary, are accounting for most brain tumors. To ensure the robustness of the suggested method, data acquisition, and preprocessing are performed in the first step followed by data augmentation. Finally, Transfer Learning algorithms including DenseNet, ResNetV2, and InceptionResNetv2 have been applied to find out the optimum algorithm based on various parameters including accuracy, precision, and recall, and are under the curve (AUC). The experimental outcomes show that the model’s validation accuracy is high for DenseNet (about 97%), while ResNetv2 and InceptionResNetv2 achieved 77% and 80% only

Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing

<p>Abstract</p> <p>Background</p> <p>In recent years, magnetic resonance imaging (MRI) has become important in brain tumor diagnosis. Using this modality, physicians can locate specific pathologies by analyzing differences in tissue character presented in different types of MR images.</p> <p>This paper uses an algorithm integrating fuzzy-c-mean (FCM) and region growing techniques for automated tumor image segmentation from patients with menigioma. Only non-contrasted T1 and T2 -weighted MR images are included in the analysis. The study's aims are to correctly locate tumors in the images, and to detect those situated in the midline position of the brain.</p> <p>Methods</p> <p>The study used non-contrasted T1- and T2-weighted MR images from 29 patients with menigioma. After FCM clustering, 32 groups of images from each patient group were put through the region-growing procedure for pixels aggregation. Later, using knowledge-based information, the system selected tumor-containing images from these groups and merged them into one tumor image. An alternative semi-supervised method was added at this stage for comparison with the automatic method. Finally, the tumor image was optimized by a morphology operator. Results from automatic segmentation were compared to the "ground truth" (GT) on a pixel level. Overall data were then evaluated using a quantified system.</p> <p>Results</p> <p>The quantified parameters, including the "percent match" (PM) and "correlation ratio" (CR), suggested a high match between GT and the present study's system, as well as a fair level of correspondence. The results were compatible with those from other related studies. The system successfully detected all of the tumors situated at the midline of brain.</p> <p>Six cases failed in the automatic group. One also failed in the semi-supervised alternative. The remaining five cases presented noticeable edema inside the brain. In the 23 successful cases, the PM and CR values in the two groups were highly related.</p> <p>Conclusions</p> <p>Results indicated that, even when using only two sets of non-contrasted MR images, the system is a reliable and efficient method of brain-tumor detection. With further development the system demonstrates high potential for practical clinical use.</p

Functional and structural MRI image analysis for brain glial tumors treatment

This Ph.D Thesis is the outcome of a close collaboration between the Center for Research in Image Analysis and Medical Informatics (CRAIIM) of the Insubria University and the Operative Unit of Neurosurgery, Neuroradiology and Health Physics of the University Hospital ”Circolo Fondazione Macchi”, Varese. The project aim is to investigate new methodologies by means of whose, develop an integrated framework able to enhance the use of Magnetic Resonance Images, in order to support clinical experts in the treatment of patients with brain Glial tumor. Both the most common uses of MRI technology for non-invasive brain inspection were analyzed. From the Functional point of view, the goal has been to provide tools for an objective reliable and non-presumptive assessment of the brain’s areas locations, to preserve them as much as possible at surgery. From the Structural point of view, methodologies for fully automatic brain segmentation and recognition of the tumoral areas, for evaluating the tumor volume, the spatial distribution and to be able to infer correlation with other clinical data or trace growth trend, have been studied. Each of the proposed methods has been thoroughly assessed both qualitatively and quantitatively. All the Medical Imaging and Pattern Recognition algorithmic solutions studied for this Ph.D. Thesis have been integrated in GliCInE: Glioma Computerized Inspection Environment, which is a MATLAB prototype of an integrated analysis environment that offers, in addition to all the functionality specifically described in this Thesis, a set of tools needed to manage Functional and Structural Magnetic Resonance Volumes and ancillary data related to the acquisition and the patient

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.