Brain Tumor Segmentation Based on Random Forest (original) (raw)

2016

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Abstract

LÁSZLÓ LEFKOVITS, SZIDÓNIA LEFKOVITS and MIRCEA-FLORIN VAIDA Department of Electrical Engineering, Faculty of Technical and Human Sciences, Sapientia University, Tg. Mureş, Romania Department of Informatics, Faculty of Science and Letters “Petru Maior” University, Tg. Mureş, Romania Department of Communications, Technical University of Cluj-Napoca, Romania Corresponding author: lefkolaci@ms.sapientia.ro

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.

Multimodal MRI brain tumor segmentation using random forests with features learned from fully convolutional neural network

arXiv (Cornell University), 2017

In this paper, we propose a novel learning based method for automated segmentation of brain tumor in multimodal MRI images. The machine learned features from fully convolutional neural network (FCN) and hand-designed texton features are used to classify the MRI image voxels. The score map with pixelwise predictions is used as a feature map which is learned from multimodal MRI training dataset using the FCN. The learned features are then applied to random forests to classify each MRI image voxel into normal brain tissues and different parts of tumor. The method was evaluated on BRATS 2013 challenge dataset. The results show that the application of the random forest classifier to multimodal MRI images using machine-learned features based on FCN and hand-designed features based on textons provides promising segmentations. The Dice overlap measure for automatic brain tumor segmentation against ground truth is 0.88, 080 and 0.73 for complete tumor, core and enhancing tumor, respectively.

MRI Brain Tumor Segmentation using Random Forests and Fully Convolutional Networks

ArXiv, 2019

In this paper, we propose a novel learning based method for automated segmentation of brain tumor in multimodal MRI images, which incorporates two sets of machine -learned and hand crafted features. Fully convolutional networks (FCN) forms the machine learned features and texton based features are considered as hand-crafted features. Random forest (RF) is used to classify the MRI image voxels into normal brain tissues and different parts of tumors, i.e. edema, necrosis and enhancing tumor. The method was evaluated on BRATS 2017 challenge dataset. The results show that the proposed method provides promising segmentations. The mean Dice overlap measure for automatic brain tumor segmentation against ground truth is 0.86, 0.78 and 0.66 for whole tumor, core and enhancing tumor, respectively.

Statistical classification of magnetic resonance images of brain employing random forest classifier

Data mining in brain imaging is an emerging field of high importance for providing prognosis, treatment, and a deeper understanding of how the brain functions. Dementia due to Alzheimer's disease constitutes the fourth most common disorder among the elderly. Early detection of dementia and correct staging of the severity of dementia is critical to select the optional treatment. The present study was designed to classify and categorize brain images of dementia patients into three distinct classes i.e., Normal, Moderately diseased, and Severe. Decision Forest Classifier was employed to classify the various Magnetic Resonance Images (MRIs) of dementia patients. Results of screening the MRIs are organized by classification and finally grouped into the three categories, i.e., Normal, Moderate and Severe. Experimental results obtained indicated that the proposed method performs relatively well with the classification accuracy reaching nearly 99.32% in comparison with the already existing algorithms.

IRJET-ANALYZING GUIDANCE INFORMATION USING RANDOM FORESTS FOR THERAPEUTIC IMAGE SEGMENTATION PROCESS

Labeled training data be used for challenging medical image segmentation problems to learn different characteristics of the relevant domain. The Random Forest (RF) classifiers and their learned knowledge during training and ways to exploit it for improved image segmentation. Apart early learning discriminative features, RFs also quantify their importance in classification. Feature importance is use to design a feature selection strategy critical for high segmentation and classification accuracy, and also to propose a efficiency cost in a second-order MRF framework for graph cut segmentation. The cost function combines the contribution of different image skin texture like intensity, texture, and curving information. Experimental outcome on medical images show that this strategy leads to better segmentation accuracy than conventional graph cut algorithms that use only intensity information in the smoothness cost.

MRI Brain Tumor Segmentation and Patient Survival Prediction Using Random Forests and Fully Convolutional Networks

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

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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.

Identifying the Brain Tumors and Classified Using a New Approach with The Support of Random Forest Decision Tree

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.

Low and high grade glioma segmentation in multispectral brain MRI data

Acta Universitatis Sapientiae, Informatica, 2018

Several hundreds of thousand humans are diagnosed with brain cancer every year, and the majority dies within the next two years. The chances of survival could be easiest improved by early diagnosis. This is why there is a strong need for reliable algorithms that can detect the presence of gliomas in their early stage. While an automatic tumor detection algorithm can support a mass screening system, the precise segmentation of the tumor can assist medical staff at therapy planning and patient monitoring. This paper presents a random forest based procedure trained to segment gliomas in multispectral volumetric MRI records. Beside the four observed features, the proposed solution uses 100 further features extracted via morphological operations and Gabor wavelet filtering. A neighborhood-based post-processing was designed to regularize and improve the output of the classifier. The proposed algorithm was trained and tested separately with the 54 low-grade and 220 high-grade tumor volumes...

Exploring Statistical Parameters of Machine Learning Techniques for Detection and Classification of Brain Tumor

International Journal of Innovative Technology and Exploring Engineering, 2019

A computerized system can improve the disease identifying abilities of doctor and also reduce the time needed for the identification and decision-making in healthcare. Gliomas are the brain tumors that can be labeled as Benign (non- cancerous) or Malignant (cancerous) tumor. Hence, the different stages of the tumor are extremely important for identification of appropriate medication. In this paper, a system has been proposed to detect brain tumor of different stages by MR images. The proposed system uses Fuzzy C-Mean (FCM) as a clustering technique for better outcome. The main focus in this paper is to refine the required features in two steps with the help of Discrete Wavelet Transform (DWT) and Independent Component Analysis (ICA) using three machine learning techniques i.e. Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machine (SVM). The final outcome of our experiment indicated that the proposed computerized system identifies the brain tumor using RF, AN...

Analyzing Training Information from Random Forests for Improved Image Segmentation

Labeled training data are used for challenging medical image segmentation problems to learn different characteristics of the relevant domain. In this paper, we examine random forest (RF) classifiers, their learned knowledge during training and ways to exploit it for improved image segmentation. Apart from learning discriminative features, RFs also quantify their importance in classification. Feature importance is used to design a feature selection strategy critical for high segmentation and classification accuracy, and also to design a smoothness cost in a second-order MRF framework for graph cut segmentation. The cost function combines the contribution of different image features like intensity, texture, and curvature information. Experimental results on medical images show that this strategy leads to better segmentation accuracy than conventional graph cut algorithms that use only intensity information in the smoothness cost.

Lesion segmentation from multimodal MRI using random forest following ischemic stroke

NeuroImage, 2014

Understanding structure-function relationships in the brain after stroke is reliant not only on the accurate anatomical delineation of the focal ischemic lesion, but also on previous infarcts, remote changes and the presence of white matter hyperintensities. The robust definition of primary stroke boundaries and secondary brain lesions will have significant impact on investigation of brain-behavior relationships and lesion volume correlations with clinical measures after stroke. Here we present an automated approach to identify chronic ischemic infarcts in addition to other white matter pathologies, that may be

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