Brain Tumor Segmentation Based on Random Forest (original) (raw)
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Automatic Detection and Segmentation of Brain Tumor Using Random Forest Approach
Lecture Notes in Computer Science, 2016
Stereotactic treatments are today the reference techniques for the irradiation of brain metastases in radiotherapy. The dose per fraction is very high, and delivered in small volumes (diameter <1 cm). As part of these treatments, effective detection and precise segmentation of lesions are imperative. Many methods based on deep-learning approaches have been developed for the automatic segmentation of gliomas, but very little for that of brain metastases. We adapted an existing 3D convolutional neural network (DeepMedic) to detect and segment brain metastases on MRI. At first, we sought to adapt the network parameters to brain metastases. We then explored the single or combined use of different MRI modalities, by evaluating network performance in terms of detection and segmentation. We also studied the interest of increasing the database with virtual patients or of using an additional database in which the active parts of the metastases are separated from the necrotic parts. Our results indicated that a deep network approach is promising for the detection and the segmentation of brain metastases on multimodal MRI.
Now-a-days, most of the people suffered from Brain tumor. In the whole nervous system, human brain is the one of the most important organ. By these brain tumors most of the people lost their life. There are extraordinary cells inside the brain leads to brain tumors. The brain tumors are of different like malignant tumors or cancerous tumors and benign tumors. In this proposed, a technique is used consists of preprocessing, segmentation, Feature extraction and Classification. Here, we are segments the tumors and detects and classified the tumors based on improved RFDT approach. The main thing we focus to investigate the tumors in early stages which help for the health practitioners.
Extremely randomized trees based brain tumor segmentation
2014
Random Decision Forest-based approaches have previously shown promising performance in the domain of brain tumor segmenta-<br> tion. We extend this idea by using an ExtraTree-classi er. Several features are calculated based on normalized T1, T2, T1 with contrast agent and T2 Flair MR-images. With these features an ExtraTree-classi er is trained and used to predict di erent tissue classes on voxel level. The results are compared to other state-of-the-art approaches by participating at the BraTS 2013 challenge.
An Optimized Segmentation Framework Applied to Glioma Delimitation
Studies in Informatics and Control
In this article we describe our segmentation framework applied to glioma delimitation in multimodal magnetic resonance images. Statistical pattern recognition strategies are applied to create a discriminative function. The discriminative classifier is the result of an automatic learning process based on random forest (RF) algorithm. This algorithm is used for two different purposes as well as in the construction of segmentation classifiers, as in the variable importance evaluation task. In the training phase the most important local image features are selected and the most adequate optimal parameters of the RF classifier are determined. The goal is to find the discriminative model that allows us to obtain the best possible segmentation performances. The segmentation framework obtained was evaluated online using the brain tumor segmentation benchmark system, and the performances were compared to the best ones reported in the literature.
Patient Specific Brain Tumor Segmentation using Context Sensitive Feature Extraction in MR Images
International Journal of Computing and Digital Systems, 2020
Brain tumor is a serious problem when it is not diagnosed. Different levels of tumors are identified this decade. The severity of tumor can be reduced if it is identified in its early stage. The most important challenge of identifying tumor is its shape and location in the brain tissue. This paper proposes different technique for extracting features for identifying the tumor by reducing the computation time. The identified features are classified using Random Forest classifier. Our proposed framework is experimented on a challenging BRATS 2015 dataset. The investigational results obtained by the proposed method shows better in terms of qualitative metrics such as Dice Score, Positive Predictive Value (PPV) and Sensitivity with a little reduction in computation time when compared to other recent methods.
Automatic Brain Tissue Segmentation in MR Images Using Random Forests and Conditional Random Fields
Journal of neuroscience methods, 2016
The segmentation of brain tissue into cerebrospinal fluid, gray matter, and white matter in Magnetic Resonance Imaging scans is an important procedure to extract regions of interest for quantitative analysis and disease assessment. Manual segmentation requires skilled experts, being a laborious and time-consuming task; therefore, reliable and robust automatic segmentation methods are necessary. We propose a segmentation framework based on a Conditional Random Field for brain tissue segmentation, with a Random Forest encoding the likelihood function. The features include intensities, gradients, probability maps, and locations. Additionally, skull stripping is critical for achieving an accurate segmentation; thus, after extracting the brain we propose to refine its boundary during segmentation. The proposed framework was evaluated on the MR Brain Image Segmentation Challenge and the Internet Brain Segmentation Repository databases. The segmentations of brain tissues obtained with the ...
Two-phase MRI brain tumor segmentation using Random Forests and Level Set Methods
CSRN
Magnetic resonance images (MRI) in various modalities contain valuable information usable in medical diagnosis. Accurate delimitation of the brain tumor and its internal tissue structures is very important for the evaluation of disease progression, for studying the effects of a chosen treatment strategy and for surgical planning as well. At the same time early detection of brain tumors and the determination of their nature have long been desirable in preventive medicine. The goal of this study is to develop an intelligent software tool for quick detection and accurate segmentation of brain tumors from MR images. In this paper we describe the developed two-staged image segmentation framework. The first stage is a voxelwise classifier based on random forest (RF) algorithm. The second acquires the accurate boundaries by evolving active contours based on the level set method (LSM). The intelligent combination of two powerful segmentation algorithms ensures performances that cannot be achieved by either of these methods alone. In our work we used the MRI database created for the BraTS '14-'16 challenges, considered a gold standard in brain tumor segmentation. The segmentation results are compared with the winning state of the art methods presented at the Brain Tumor Segmentation Grand Challenge and Workshop (BratsTS).
Predicting a multi-parametric probability map of active tumor extent using random forests
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013
Glioblastoma Mulitforme is highly infiltrative, making precise delineation of tumor margin difficult. Multimodality or multi-parametric MR imaging sequences promise an advantage over anatomic sequences such as post contrast enhancement as methods for determining the spatial extent of tumor involvement. In considering multi-parametric imaging sequences however, manual image segmentation and classification is time-consuming and prone to error. As a preliminary step toward integration of multi-parametric imaging into clinical assessments of primary brain tumors, we propose a machine-learning based multi-parametric approach that uses radiologist generated labels to train a classifier that is able to classify tissue on a voxel-wise basis and automatically generate a tumor segmentation. A random forests classifier was trained using a leave-one-out experimental paradigm. A simple linear classifier was also trained for comparison. The random forests classifier accurately predicted radiologist generated segmentations and tumor extent.