Brain Tumor Detection via Asymmetry Quantification across Mid Sagittal Plane

2020 ◽  
Vol 13 ◽  
Author(s):  
Shoaib Amin Banday ◽  
Mohammad Khalid Pandit
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Sagittal Plane ◽  
Early Stage ◽  
Imaging Techniques ◽  
Complex Structure ◽  
Magnetic Resonance Images ◽  
Absolute Difference ◽  
Brain Hemispheres ◽  
The Brain

Introduction: Brain tumor is among the major causes of morbidity and mortality rates worldwide. According to National Brain Tumor Foundation (NBTS), the death rate has nearly increased by as much as 300% over last couple of decades. Tumors can be categorized as benign (non-cancerous) and malignant (cancerous). The type of the brain tumor significantly depends on various factors like the site of its occurrence, its shape, the age of the subject etc. On the other hand, Computer Aided Detection (CAD) has been improving significantly in recent times. The concept, design and implementation of these systems ascend from fairly simple ones to computationally intense ones. For efficient and effective diagnosis and treatment plans in brain tumor studies, it is imperative that an abnormality is detected at an early stage as it provides a little more time for medical professionals to respond. The early detection of diseases has predominantly been possible because of medical imaging techniques developed from past many decades like CT, MRI, PET, SPECT, FMRI etc. The detection of brain tumors however, has always been a challenging task because of the complex structure of the brain, diverse tumor sizes and locations in the brain. Method: This paper proposes an algorithm that can detect the brain tumors in the presence of the Radio-Frequency (RF) inhomoginiety. The algorithm utilizes the Mid Sagittal Plane as a landmark point across which the asymmetry between the two brain hemispheres is estimated using various intensity and texture based parameters. Result: The results show the efficacy of the proposed method for the detection of the brain tumors with an acceptable detection rate. Conclusion: In this paper, we have calculated three textural features from the two hemispheres of the brain viz: Contrast (CON), Entropy (ENT) and Homogeneity (HOM) and three parameters viz: Root Mean Square Error (RMSE), Correlation Co-efficient (CC), and Integral of Absolute Difference (IAD) from the intensity distribution profiles of the two brain hemispheres to predict any presence of the pathology. First a Mid Sagittal Plane (MSP) is obtained on the Magnetic Resonance Images that virtually divides brain into two bilaterally symmetric hemispheres. The block wise texture asymmetry is estimated for these hemispheres using the above 6 parameters.

scholarly journals Performance Analysis of Deep Belief Neural Network for Brain Tumor Classification

2020 ◽  
Vol 1 (3) ◽  
pp. 29-34
Author(s):  
Sreenivas Eeshwaroju ◽  
◽  
Praveena Jakula ◽  
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Imaging Techniques ◽  
Structural Heterogeneity ◽  
Low Complexity ◽  
Automated Classification ◽  
Tumor Identification ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

The brain tumors are by far the most severe and violent disease, contributing to the highest degree of a very low life expectancy. Therefore, recovery preparation is a crucial step in improving patient quality of life. In general , different imaging techniques such as computed tomography ( CT), magnetic resonance imaging ( MRI) and ultrasound imaging have been used to examine the tumor in the brain, lung , liver, breast , prostate ... etc. MRI images are especially used in this research to diagnose tumor within the brain with classification results. The massive amount of data produced by the MRI scan, therefore, destroys the manual classification of tumor vs. non-tumor in a given period. However for a limited number of images, it is presented with some constraint that is precise quantitative measurements. Consequently, a trustworthy and automated classification scheme is important for preventing human death rates. The automatic classification of brain tumors is a very challenging task in broad spatial and structural heterogeneity of the surrounding brain tumor area. Automatic brain tumor identification is suggested in this research by the use of the classification with Deep Belief Network (DBN). Experimental results show that the DBN archive rate with low complexity seems to be 97 % accurate compared to all other state of the art methods.

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 Segmentation of Brain Tumor using Glcm and Discrete Wavelet Transform

2020 ◽  
Vol 9 (6) ◽  
pp. 344-348
Keyword(s):  
Feature Extraction ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Tissue Sample ◽  
Early Stage ◽  
Discrete Wavelet ◽  
Effective Technique ◽  
Magnetic Resonance Imaging Mri ◽  
Occurrence Matrix ◽  
The Brain

To identify brain tumors at an early stage is a challenging task. The brain tumor is usually diagnosed with Magnetic Resonance Imaging (MRI). When MRI spectacles a tumor in the brain, the most common way of determining the type of brain tumor after a biopsy or surgery is to look at the results of a tissue sample. In this research to detect brain tumors faster and accurately the feature extraction techniques are used to segment the tumor affected area. One of such very effective technique of feature extraction measure is the Grayscale Co-occurrence Matrix (GLCM). This research focuses on the GLCM and Discrete Wavelet Transformation (DWT) technique to detect and label the tumor from an image based on the textures and categorizing it according to a tumor or non-tumor category. The convolutional neural network (CNN) uses these features to improve the accuracy to 91%.

scholarly journals Brain Tumor Segmentation using Normalized Graph Cuts

2019 ◽  
Vol 8 (6) ◽  
pp. 3159-3161
Keyword(s):  
Brain Tumor ◽  
Complex Structure ◽  
Weighted Graph ◽  
Graph Cut ◽  
Magnetic Resonance Images ◽  
Comprehensive Analysis ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Blood Clots ◽  
The Brain

Normalized graph cut algorithm is an efficient method where the technique of graph theory is adopted and in which the images are taken in the form of weighted graph in order to segment the images. This paper comprises of the fundamental concept of Normalized graph cut algorithm and its application towards the segmentation of Brain tumor. Identifying defects such as tumors is a very challenging because differentiating the components is difficult in a complex structure like a human brain. The diagnosis becomes even more complex because the tumor, blood clots and some part of the brain tissues appear as the same Brain tumor is generally detected and analyzed through a comprehensive analysis of the Magnetic Resonance Images of the brain. This technique gives a second opinion regarding the presence or absence of the brain tumor. This paper performs the study of Normalized graph cut algorithm and shows its efficiency in detecting tumors and compares it with other commonly used algorithms

A Review of Various Machine Learning Techniques for Brain Tumor Detection from MRI Images

2020 ◽  
Vol 16 (8) ◽  
pp. 937-945
Author(s):  
Aaishwarya Sanjay Bajaj ◽  
Usha Chouhan
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Ct Scan ◽  
Imaging Techniques ◽  
Critical Evaluation ◽  
Tumor Detection ◽  
Machine Learning Techniques ◽  
X Rays ◽  
Detection Techniques ◽  
The Brain

Background: This paper endeavors to identify an expedient approach for the detection of the brain tumor in MRI images. The detection of tumor is based on i) review of the machine learning approach for the identification of brain tumor and ii) review of a suitable approach for brain tumor detection. Discussion: This review focuses on different imaging techniques such as X-rays, PET, CT- Scan, and MRI. This survey identifies a different approach with better accuracy for tumor detection. This further includes the image processing method. In most applications, machine learning shows better performance than manual segmentation of the brain tumors from MRI images as it is a difficult and time-consuming task. For fast and better computational results, radiology used a different approach with MRI, CT-scan, X-ray, and PET. Furthermore, summarizing the literature, this paper also provides a critical evaluation of the surveyed literature which reveals new facets of research. Conclusion: The problem faced by the researchers during brain tumor detection techniques and machine learning applications for clinical settings have also been discussed.

Intracranial Tumors- Overview, Histological Types, Symptoms and Treatment Plans

2021 ◽  
Vol 11 (10) ◽  
pp. 133-144
Author(s):  
Dipak Chaulagain ◽  
Volodymyr Smolanka ◽  
Andriy Smolanka
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Marital Status ◽  
Size Variation ◽  
Intracranial Tumors ◽  
Regional Center ◽  
East And Southeast Asia ◽  
Abnormal Cells ◽  
Treatment Plans ◽  
The Brain

People, in general, are affected by a brain or intracranial tumor when abnormal cells started functioning within their brain. This paper explores mainly tumors that affect the brain. Almost every type of brain tumor might create symptoms which are based on the parts of the brain affected. In order to better understand reasons of occurrence intracranial tumors in various sections of the population, the study examined the relationship between sociodemographic variables, i.e., age, gender and marital status and the relative frequency of intracranial tumors as part of a study. Samples are collected based on the information from Uzhhorod Regional Center of Neurosurgery and Neurology in Ukraine. And factors such as age, gender and marital status has been considered to examine tumor size variation. The ratios of organ cancers in Ukrainians are evidently increasing. Besides, there has been growing trend in affected rates in both the genders of CNS cancers have been noticed in all the records. The ratio of most harmful brain tumors is comparatively in minimal ratio in East and Southeast Asia, and India. On the other hand, the highest ratio has been noted in European countries and as well United States, and Ukraine is one of those countries. The major burdens of cancer frequency in Ukraine have been noticed in the lung, breast, and prostate and brain. Of these, brain tumor rate in Ukraine had been hardly studied. The major difference in frequency in Asian and European populations implies the potential influence of genetic or environmental factors in malignant brain tumors. Continuing monitoring of tumor ratio in Ukraine is essential to comprehend how best to reduce cancer burden globally and to explain the causes of provincial variations, for example access to diagnosis methods and ecological exposures. Key words: Intracranial tumors, symptoms, tumor incidence in Ukraine, treatment plans, survival rate of cancer in Ukraine.

scholarly journals Pharmacodynamic Analysis of Magnetic Resonance Imaging-Monitored Focused Ultrasound-Induced Blood-Brain Barrier Opening for Drug Delivery to Brain Tumors

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Po-Chun Chu ◽  
Wen-Yen Chai ◽  
Han-Yi Hsieh ◽  
Jiun-Jie Wang ◽  
Shiaw-Pyng Wey ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Small Molecule ◽  
Focused Ultrasound ◽  
Therapeutic Agents ◽  
Tumor Treatment ◽  
Target Region ◽  
Pharmacodynamic Analysis ◽  
The Brain ◽  
Bbb Opening

Microbubble-enhanced focused ultrasound (FUS) can enhance the delivery of therapeutic agents into the brain for brain tumor treatment. The purpose of this study was to investigate the influence of brain tumor conditions on the distribution and dynamics of small molecule leakage into targeted regions of the brain after FUS-BBB opening. A total of 34 animals were used, and the process was monitored by 7T-MRI. Evans blue (EB) dye as well as Gd-DTPA served as small molecule substitutes for evaluation of drug behavior. EB was quantified spectrophotometrically. Spin-spin (R1) relaxometry and area under curve (AUC) were measured by MRI to quantify Gd-DTPA. We found that FUS-BBB opening provided a more significant increase in permeability with small tumors. In contrast, accumulation was much higher in large tumors, independent of FUS. The AUC values of Gd-DTPA were well correlated with EB delivery, suggesting that Gd-DTPA was a good indicator of total small-molecule accumulation in the target region. The peripheral regions of large tumors exhibited similar dynamics of small-molecule leakage after FUS-BBB opening as small tumors, suggesting that FUS-BBB opening may have the most significant permeability-enhancing effect on tumor peripheral. This study provides useful information toward designing an optimized FUS-BBB opening strategy to deliver small-molecule therapeutic agents into brain tumors.

Brain Tumor Reporting and Data System: A Pictorial Review

Neurographics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 175-185
Author(s):  
B. Rao ◽  
I. Ikuta ◽  
A. Mahajan ◽  
A.A. Karam ◽  
V.M. Zohrabian
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Response To Therapy ◽  
Data System ◽  
Imaging Features ◽  
New Paradigm ◽  
Advantages And Disadvantages ◽  
Pictorial Review ◽  
Almost All ◽  
The Brain

Brain tumors are a diverse group of neoplasms that are a source of substantial morbidity and mortality worldwide. Primary gliomas constitute almost all malignant brain tumors, with the most aggressive as well as most common form in adults, grade IV glioma or glioblastoma multiforme, carrying an especially poor prognosis. Neuroimaging is critical not only in the identification of CNS tumor but also in treatment-planning and assessing the response to therapy. Structured reporting continues to gain traction in radiology by reducing report ambiguity and improving consistency, while keeping referring clinicians and patients informed. The Brain Tumor Reporting and Data System (BT-RADS) is a relatively new paradigm that attempts to simplify and maximize consistency in radiologic reporting. BT-RADS incorporates MR imaging features, clinical assessment, and timing of therapy to assign each study a score or category, which is, in turn, linked to a management suggestion. The purpose of this pictorial review article is to familiarize radiologists and nonradiology neurologic specialists alike with BT-RADS, highlighting both advantages and limitations, in the hope that adoption of this system might ultimately facilitate more effective communication and improve consistency among reports.Learning Objective: To describe the features and underscore the advantages and disadvantages of the Brain Tumor Reporting and Data System (BT-RADS), a relatively new classification system that attempts to simplify and maximize consistency in radiologic reporting

scholarly journals Multi-Task Learning for Small Brain Tumor Segmentation from MRI

2020 ◽  
Vol 10 (21) ◽  
pp. 7790
Author(s):  
Duc-Ky Ngo ◽  
Minh-Trieu Tran ◽  
Soo-Hyung Kim ◽  
Hyung-Jeong Yang ◽  
Guee-Sang Lee
Keyword(s):  
Brain Tumor ◽  
Early Stage ◽  
Tumor Segmentation ◽  
Great Success ◽  
Brain Tumor Segmentation ◽  
Dilated Convolution ◽  
Task Learning ◽  
Novel Approach ◽  
New Development ◽  
The Brain

Segmenting brain tumors accurately and reliably is an essential part of cancer diagnosis and treatment planning. Brain tumor segmentation of glioma patients is a challenging task because of the wide variety of tumor sizes, shapes, positions, scanning modalities, and scanner’s acquisition protocols. Many convolutional neural network (CNN) based methods have been proposed to solve the problem of brain tumor segmentation and achieved great success. However, most previous studies do not fully take into account multiscale tumors and often fail to segment small tumors, which may have a significant impact on finding early-stage cancers. This paper deals with the brain tumor segmentation of any sizes, but specially focuses on accurately identifying small tumors, thereby increasing the performance of the brain tumor segmentation of overall sizes. Instead of using heavyweight networks with multi-resolution or multiple kernel sizes, we propose a novel approach for better segmentation of small tumors by dilated convolution and multi-task learning. Dilated convolution is used for multiscale feature extraction, however it does not work well with very small tumor segmentation. For dealing with small-sized tumors, we try multi-task learning, where an auxiliary task of feature reconstruction is used to retain the features of small tumors. The experiment shows the effectiveness of segmenting small tumors with the proposed method. This paper contributes to the detection and segmentation of small tumors, which have seldom been considered before and the new development of hierarchical analysis using multi-task learning.

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