scholarly journals Analysis of Brain MRI Images Using Improved CornerNet Approach

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1856
Author(s):  
Marriam Nawaz ◽  
Tahira Nazir ◽  
Momina Masood ◽  
Awais Mehmood ◽  
Rabbia Mahum ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
Region Of Interest ◽  
Abnormal Growth ◽  
Novel Approach ◽  
Human Blood Cells ◽  
Average Accuracy ◽  
The One ◽  
Treatment Procedures

The brain tumor is a deadly disease that is caused by the abnormal growth of brain cells, which affects the human blood cells and nerves. Timely and precise detection of brain tumors is an important task to avoid complex and painful treatment procedures, as it can assist doctors in surgical planning. Manual brain tumor detection is a time-consuming activity and highly dependent on the availability of area experts. Therefore, it is a need of the hour to design accurate automated systems for the detection and classification of various types of brain tumors. However, the exact localization and categorization of brain tumors is a challenging job due to extensive variations in their size, position, and structure. To deal with the challenges, we have presented a novel approach, namely, DenseNet-41-based CornerNet framework. The proposed solution comprises three steps. Initially, we develop annotations to locate the exact region of interest. In the second step, a custom CornerNet with DenseNet-41 as a base network is introduced to extract the deep features from the suspected samples. In the last step, the one-stage detector CornerNet is employed to locate and classify several brain tumors. To evaluate the proposed method, we have utilized two databases, namely, the Figshare and Brain MRI datasets, and attained an average accuracy of 98.8% and 98.5%, respectively. Both qualitative and quantitative analysis show that our approach is more proficient and consistent with detecting and classifying various types of brain tumors than other latest techniques.

Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation

2021 ◽  
Vol 16 ◽  
Author(s):  
Ghazanfar Latif ◽  
Jaafar Alghazo ◽  
Fadi N. Sibai ◽  
D.N.F. Awang Iskandar ◽  
Adil H. Khan
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Brain Anatomy ◽  
Mri Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Low Contrast ◽  
Fuzzy C Means

Background: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques. Objective: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers. Results: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging. In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

scholarly journals Vascular phenotyping of brain tumors using magnetic resonance microscopy (μMRI)

2011 ◽  
Vol 31 (7) ◽  
pp. 1623-1636 ◽  
Author(s):  
Eugene Kim ◽  
Jiangyang Zhang ◽  
Karen Hong ◽  
Nicole E Benoit ◽  
Arvind P Pathak
Keyword(s):  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Three Dimensional ◽  
Region Of Interest ◽  
Tumor Model ◽  
Magnetic Resonance Microscopy ◽  
Vascular Phenotype ◽  
Brain Tumor Model ◽  
Novel Method

Abnormal vascular phenotypes have been implicated in neuropathologies ranging from Alzheimer's disease to brain tumors. The development of transgenic mouse models of such diseases has created a crucial need for characterizing the murine neurovasculature. Although histologic techniques are excellent for imaging the microvasculature at submicron resolutions, they offer only limited coverage. It is also challenging to reconstruct the three-dimensional (3D) vasculature and other structures, such as white matter tracts, after tissue sectioning. Here, we describe a novel method for 3D whole-brain mapping of the murine vasculature using magnetic resonance microscopy (μMRI), and its application to a preclinical brain tumor model. The 3D vascular architecture was characterized by six morphologic parameters: vessel length, vessel radius, microvessel density, length per unit volume, fractional blood volume, and tortuosity. Region-of-interest analysis showed significant differences in the vascular phenotype between the tumor and the contralateral brain, as well as between postinoculation day 12 and day 17 tumors. These results unequivocally show the feasibility of using μMRI to characterize the vascular phenotype of brain tumors. Finally, we show that combining these vascular data with coregistered images acquired with diffusion-weighted MRI provides a new tool for investigating the relationship between angiogenesis and concomitant changes in the brain tumor microenvironment.

scholarly journals Classification of Tumors in Brain MRI Images With Hybrid of Global and Local DWT Features using Decision Tree

2019 ◽  
Vol 8 (12) ◽  
pp. 3072-3077
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Decision Tree ◽  
Brain Mri ◽  
Extraction Process ◽  
Discrete Wavelet ◽  
Global Features ◽  
Tumor Identification ◽  
The Brain

Automated brain tumor identification and classification is still an open problem for research in the medical image processing domain. Brain tumor is a bunch of unwanted cells that develop in the brain. This growth of a tumor takes up space within skull and affects the normal functioning of brain. Automated segmentation and detection of brain tumors are important in MRI scan analysis as it provides information about neural architecture of brain and also about abnormal tissues that are extremely necessary to identify appropriate surgical plan. Automating this process is a challenging task as tumor tissues show high diversity in appearance with different patients and also in many cases they tend to appear very similar to the normal tissues. Effective extraction of features that represent the tumor in brain image is the key for better classification. In this paper, we propose a hybrid feature extraction process. In this process, we combine the local and global features of the brain MRI using first by Discrete Wavelet Transformation and then using texture based statistical features by computing Gray Level Co-occurrence Matrix. The extracted combined features are used to construct decision tree for classification of brain tumors in to benign or malignant class.

scholarly journals Brain MRI Images Segmentation Based on U-Net Architecture

2021 ◽  
Vol 18 (1) ◽  
pp. 21-27
Author(s):  
Assalah Atiyah ◽  
Khawla Ali
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Performance Metrics ◽  
Brain Mri ◽  
Regular Function ◽  
Survival Rates ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Edge Based ◽  
The Brain

Brain tumors are collections of abnormal tissues within the brain. The regular function of the brain may be affected as it grows within the region of the skull. Brain tumors are critical for improving treatment options and patient survival rates to prevent and treat them. The diagnosis of cancer utilizing manual approaches for numerous magnetic resonance imaging (MRI) images is the most complex and time-consuming task. Brain tumor segmentation must be carried out automatically. A proposed strategy for brain tumor segmentation is developed in this paper. For this purpose, images are segmented based on region-based and edge-based. Brain tumor segmentation 2020 (BraTS2020) dataset is utilized in this study. A comparative analysis of the segmentation of images using the edge-based and region-based approach with U-Net with ResNet50 encoder, architecture is performed. The edge-based segmentation model performed better in all performance metrics compared to the region-based segmentation model and the edge-based model achieved the dice loss score of 0. 008768, IoU score of 0. 7542, f1 score of 0. 9870, the accuracy of 0. 9935, the precision of 0. 9852, recall of 0. 9888, and specificity of 0. 9951.

scholarly journals A Review on Some Classification Methods of Brain Tumor MRI Datasets

2020 ◽  
pp. 421-424
Author(s):  
Nirmal Mungale ◽  
Snehal Kene ◽  
Amol Chaudhary
Keyword(s):  
Brain Tumor ◽  
Brain Mri ◽  
Mr Images ◽  
Classification Techniques ◽  
Abnormal Growth ◽  
Life Threatening ◽  
Magnetic Resonance Imaging Mri ◽  
Recent Classification ◽  
The Brain

Brain tumor is a life-threatening disease. Brain tumor is formed by the abnormal growth of cells inside and around the brain. Identification of the size and type of tumor is necessary for deciding the course of treatment of the patient. Magnetic Resonance Imaging (MRI) is one of the methods for detection of tumor in the brain. The classification of MR Images is a difficult task due to variety and complexity of brain tumors. Various classification techniques have been identified for brain MRI tumor images. This paper reviews some of these recent classification techniques.

scholarly journals MRI Modality-based Brain Tumor Segmentation Using Deep Neural Networks

2021 ◽  
Author(s):  
Pankaj Eknath Kasar ◽  
Shivajirao M. Jadhav ◽  
Vineet Kansal
Keyword(s):  
Neural Networks ◽  
Brain Tumor ◽  
Clinical Practice ◽  
Brain Tumors ◽  
Deep Neural Networks ◽  
Automatic Segmentation ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Dice Similarity Coefficient ◽  
Manual Segmentation

Abstract The tumor detection is major challenging task in brain tumor quantitative evaluation. In recent years, owing to non-invasive and strong soft tissue comparison, Magnetic Resonance Imaging (MRI) has gained great interest. MRI is a commonly used image modality technique to locate brain tumors. An immense amount of data is produced by the MRI. Heterogeneity, isointense and hypointense tumor properties restrict manual segmentation in a fair period of time, thus restricting the use of reliable quantitative measures in clinical practice. In the clinical practice manual segmentation task is quite time consuming and their performance is highly depended on the operator’s experience. Accurate and automated tumor segmentation techniques are also needed; however, the severe spatial and structural heterogeneity of brain tumors makes automatic segmentation a difficult job. This paper proposes fully automatic segmentation of brain tumors using encoder-decoder based convolutional neural networks. The paper focuses on well-known semantic segmentation deep neural networks i.e., UNET and SEGNET for segmenting tumors from Brain MRI images. The networks are trained and tested using freely accessible standard dataset, with Dice Similarity Coefficient (DSC) as metric for whole predicted image i.e., including tumor and background. UNET’s average DSC on test dataset is 0.76 whereas for SEGNET we got average DSC 0.67. The evaluation of results proves that UNET is having better performance than SEGNET.

scholarly journals Clustering of Brain Tumors in Brain MRI Images based on Extraction of Textural and Statistical Features

2020 ◽  
Vol 16 (12) ◽  
pp. 116
Author(s):  
Hamed Samadi Ghoushchi ◽  
Yaghoub Pourasad
Keyword(s):  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Imaging Techniques ◽  
Brain Mri ◽  
Support Vector ◽  
Statistical Features ◽  
Resonance Imaging ◽  
The Brain

<p>The purpose of this article is to investigate techniques for classifying tumor grade from magnetic resonance imaging (MRI). This requires early diagnosis of the brain tumor and its grade. Magnetic resonance imaging may show a clear tumor in the brain, but doctors need to measure the tumor in order to treat more or to advance treatment. For this purpose, digital imaging techniques along with machine learning can help to quickly identify tumors and also treatments and types of surgery. These combined techniques in understanding medical images for researchers are an important tool to increase the accuracy of diagnosis. In this paper, classification methods for MRI images of tumors of the human brain are performed to review the astrocytoma-containing glands. Methods used to classify brain tumors, including preprocessing, screening, tissue extraction, and statistical features of the tumor using two types of T<sub>1</sub>W and Flair brain MRI images and also the method of dimensionality reduction of extracted features and how to train them in classification are also explained. Determine the tumor area using three classification of Fuzzy Logic <em>C</em><em>-</em><em>Means</em><em> </em>Clustering (FCM), Probabilistic Neural Networks (PNN) and Support Vector Machines (SVM). In this paper, simulated and real MRI images are used. The results obtained from the proposed methods in this paper are compared with the reference results and the results show that the proposed approach can increase the reliability of brain tumor diagnosis.</p>

scholarly journals A Comparative Study of Enhanced Machine Learning Algorithms for Brain Tumor Detection and Classification

2021 ◽  
Author(s):  
ANKIT GHOSH ◽  
ALOK KOLE
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Binary Classification ◽  
Brain Mri ◽  
Random Forest Classifier ◽  
The Other ◽  
Gradient Boosting ◽  
Multi Class Classification

<p>The improvement of Artificial Intelligence (AI) and Machine Learning (ML) can help radiologists in tumor diagnostics without invasive measures. Magnetic resonance imaging (MRI) is a very useful method for diagnosis of tumors in human brain. In this paper, brain MRI images have been analyzed to detect the regions containing tumors and classify these regions into three different tumor categories: meningioma, glioma, and pituitary. This paper presents the implementation and comparison of various enhanced ML algorithms for the detection and classification of brain tumors. A brain tumor is the growth of abnormal cells in the human brain. Brain tumors can be cancerous or non-cancerous. Cancerous or malignant brain tumors can be life threatening. Hence, detection and classification of brain tumors at an early stage is extremely important. In this paper, enhanced ML algorithms have been implemented to predict the presence or the absence of brain tumors using binary classification and to predict whether a patient has brain tumor or not and if he does, detect the type of brain tumor using multi-class classification. The dataset that has been used to perform the binary classification task comprises of two types of brain MRI images with tumor and without tumor. Here nine ML algorithms namely, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbor (KNN), Naïve Bayes (NB), Decision Tree (DT) classifier, Random Forest classifier, XGBoost classifier, Stochastic Gradient Descent (SGD) classifier and Gradient Boosting classifier have been used to classify the MRI images. A comparative analysis of the ML algorithms has been performed based on a few performance metrics such as accuracy, recall, and precision, F1-score, AUC-ROC curve and AUC-PR curve. Gradient Boosting classifier has outperformed all the other algorithms with an accuracy of 92.4%, recall of 94.4%, precision of 85%, F1-score of 89.5%, AUC-ROC of 97.2% and an AUC-PR of 91.4%. To address the multi-class classification problem, four ML algorithms namely, SVM, KNN, Random Forest classifier and XGBoost classifier have been employed. In this case, the dataset that has been used consists of four types of brain MRI images with glioma tumor, meningioma tumor, and pituitary tumor and with no tumor. The performances of the ML algorithms have been compared based on accuracy, recall, precision and the F1-score. XGBoost classifier has surpassed all the other algorithms in terms of accuracy, precision, recall and F1-score. XGBoost has produced an accuracy of 90%, precision of 90%, and recall of 90% and F1-score of 90%.</p>

scholarly journals Evaluating Brush Movements for Chinese Calligraphy: A Computer Vision Based Approach

2018 ◽  
Author(s):  
Pengfei Xu ◽  
Lei Wang ◽  
Ziyu Guan ◽  
Xia Zheng ◽  
Xiaojiang Chen ◽  
...  
Keyword(s):  
Computer Vision ◽  
Video Stream ◽  
Movement Trajectory ◽  
Writing Quality ◽  
Chinese Calligraphy ◽  
Art Form ◽  
Novel Approach ◽  
Average Accuracy ◽  
Cultural Sphere ◽  
The One

Chinese calligraphy is a popular, highly esteemed art form in the Chinese cultural sphere and worldwide. Ink brushes are the traditional writing tool for Chinese calligraphy and the subtle nuances of brush movements have a great impact on the aesthetics of the written characters. However, mastering the brush movement is a challenging task for many calligraphy learners as it requires many years’ practice and expert supervision. This paper presents a novel approach to help Chinese calligraphy learners to quantify the quality of brush movements without expert involvement. Our approach extracts the brush trajectories from a video stream; it then compares them with example templates of reputed calligraphers to produce a score for the writing quality. We achieve this by first developing a novel neural network to extract the spatial and temporal movement features from the video stream. We then employ methods developed in the computer vision and signal processing domains to track the brush movement trajectory and calculate the score. We conducted extensive experiments and user studies to evaluate our approach. Experimental results show that our approach is highly accurate in identifying brush movements, yielding an average accuracy of 90%, and the generated score is within 3% of errors when compared to the one given by human experts.

scholarly journals A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI images

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 744
Author(s):  
Momina Masood ◽  
Tahira Nazir ◽  
Marriam Nawaz ◽  
Awais Mehmood ◽  
Junaid Rashid ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Abnormal Growth ◽  
Irregular Shapes ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Domain Expertise ◽  
Comparative Results ◽  
Bounding Boxes

A brain tumor is an abnormal growth in brain cells that causes damage to various blood vessels and nerves in the human body. An earlier and accurate diagnosis of the brain tumor is of foremost important to avoid future complications. Precise segmentation of brain tumors provides a basis for surgical planning and treatment to doctors. Manual detection using MRI images is computationally complex in cases where the survival of the patient is dependent on timely treatment, and the performance relies on domain expertise. Therefore, computerized detection of tumors is still a challenging task due to significant variations in their location and structure, i.e., irregular shapes and ambiguous boundaries. In this study, we propose a custom Mask Region-based Convolution neural network (Mask RCNN) with a densenet-41 backbone architecture that is trained via transfer learning for precise classification and segmentation of brain tumors. Our method is evaluated on two different benchmark datasets using various quantitative measures. Comparative results show that the custom Mask-RCNN can more precisely detect tumor locations using bounding boxes and return segmentation masks to provide exact tumor regions. Our proposed model achieved an accuracy of 96.3% and 98.34% for segmentation and classification respectively, demonstrating enhanced robustness compared to state-of-the-art approaches.

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