Size Analysis of Brain Tumor from MRI Images Using MATLAB

Brain tumor detection and segmentation from Magnetic Resonance Imaging (MRI) images is being one of the emerging fields in the biomedicine. A formidable undertaking in brain tumor surgery, medical care, treatment programme and quantitative assessment of MRI images is to precisely diagnose its location and extent. Recently, the convolutional neural network (CNN) based detection and segmentation method on brain tumor MRI images is being one of the emerging fields in the medical imaging as an automatic clinic treatment and evaluation solution. In this article, we put forward a brand new quadruplet loss in CNN framework, which achieves higher accuracy in brain tumor detection and segmentation than other pairwise loss and triplet loss methods. By applying the proposed quadruplet loss to the original L2Net CNN architecture leads to a more compact descriptor named QuadrupletNet. From our experiments, QuadrupletNet shows higher performance than other state-of-the-art loss functions e.g., the Triplet loss, as indicated in experiments on Multimodal Brain Tumor Image Segmentation (BRATS 2018) datasets, and on our own collected MRI brain tumor datasets (named MBTD).

—Segmenting tumor from MRI images is an essential but time consuming manual duty. Performing an automatic segmentation is a defying task since different forms of tumor tissue exist for diverse patients and in many cases the tumor is similar to the normal tissue. Various studies proposed earlier to handle the issue of precisely segmenting the tumor but they discard the degradations and their effect to the precision of the segmentation. This article provides a more precise segmentation process through the use of appropriate pre-processing algorithms. The authors studied many enhancement and restoration algorithms and selected the NL-means, Laplacian filter and histogram equalization to be used as preprocessing techniques. Experimental results showed that using a suitable preprocessing scheme would produce a better segmentation process. Index Terms — Medical image processing, Tumor segmentation, MRI images, Non-local Means, Histogram equalization, Laplacian filter. I. INTRODUCTION A tumor is a mass of tissue that grows out of control of the normal forces that regulates growth. Brain tumor has a higher mortality rate when compared to more common types of cancer. This underlines the need of accurate prognosis of brain tumor in health care industry [1]. MRI has become a widely-used method of high-quality medical imaging, especially in brain imaging [2]. Image segmentation refers to the process of partitioning a digital image into multiple segments, also known as super pixels. Image segmentation is an important process in computer vision and image processing, which divides an image into a number of disjoint regions such that the pixels have high similarity in order to use its different fields; science, medical, agriculture and industrial fields [3]. Besides, Magnetic Resonance Imaging (MRI) images are known to be corrupted by noise [4], blur [5] and low contrast artifact [6]. Therefore, this article discusses the issue of achieving a better segmentation through the use of proper image restoration and enhancement techniques. Moreover, the accurate and automatic segmentation of a brain tumor on MRI image is of great interest for assessing tumor growth and treatment responses, enhancing computer-assisted surgery, and constructing tumor growth models [7]. For those reasons, an automatic and reliable tumor segmentation method would be a useful tool since the medical image segmentation plays an instrumental role in clinical diagnosis [8]. The rest of the article sections are organized as the following: in section two, a brief elucidation about some related studies is presented as the used literature review. Moreover, detailed explanations about the employed algorithms are delivered in section three as a methodology. Also, the results of the proposed methodology are illustrated in section four as the experimental results. Likewise, a detailed discussion about the use of certain algorithms and its results is deliberated in section five. Finally, a brief closure about this article is provided in section six as a conclusion. II. LITERATURE REVIEW In this section, three recent studies are briefly described. The first study was presented by [9] which proposed a texture based analysis to identify irregularity in the brain and automatic segmentation using seeded region growing segmentation. The method of tumor detection based on the texture of the MRI is proposed to segment the abnormality from the regular adjacent, although small quantities misclassified, but the segmentation result is precise. Moreover, [10] suggested a strong technique for automatic detection of tumor for brain MRI images. He also proposed a new morphological technique which is used for the stripped skull region from the brain images. Detecting tumor precisely based on seeded region growing segmentation on an improved single seed point selection algorithm is

Digital Image Processing (DIP) is a well-developed field in the biological sciences which involves classification and detection of tumour. In medical science, automatic brain tumor diagnosis is an important phase. Brain tumor detection is performed by Computer-Aided Diagnosis (CAD) systems. The human image creation is greatly achieved by an approach namely medical imaging which is exploited for medical and research purposes. Recently Automatic brain tumor detection from MRI images has become the emerging research area of medical research. Brain tumor diagnosis mainly performed for obtaining exact location, orientation and area of abnormal tissues. Cancer and edema regions inference from brain magnetic resonance imaging (MRI) information is considered to be great challenge due to brain tumors complex structure, blurred borders, besides exterior features like noise. The noise compassion is mainly reduced along with segmentation stability by suggesting efficient hybrid clustering method merged with morphological process for brain cancer segmentation. Combined form of Median Modified Wiener filter (CMMWF) is chiefly deployed for denoising, and morphological operations which in turn eliminate nonbrain tissue, efficiently dropping technique’s sensitivity to noise. The proposed system contains the main phases such as preprocessing, brain tumor extraction and post processing. Image segmentation is greatly achieved by presenting Intuitionist Possibilistic Fuzzy Clustering (IPFC) algorithm. The algorithm’s stability is greatly enhanced by this clustering along with clustering parameters sensitivity reduction. Then, the post processing of images are done through morphological operations along with Hybrid Median filtering (HMF) for attaining exact tumors representations. Additionally, suggested algorithm is substantiated by comparing with other existing segmentation algorithms. The outcomes reveal that suggested algorithm achieves improved outcomes pertaining to accuracy, sensitivity, specificity, and recall.

In this paper, we look at how to design and build a system to find tumors using 2 Convolutional Neural Network (CNN) models. With the help of digital image processing and deep Learning, we can make a system that automatically diagnoses and finds different diseases and abnormalities. The tumor detection system may include image enhancement, segmentation, data enhancement, feature extraction, and classification. These options are set up so that the CNN model can give the best results. During the training phase, the learning rate is used to change the weights and bias. The learning rate also changes the weights. One Epoch is when all of the training images are shown to the model. As the training data may be very large, the data in each epoch are split into batches. Every epoch has a training session and a test session. After each epoch, the weights are changed based on how fast the CNN is learning. This is done with the help of optimization algorithms. The suggested technique uses the anticipated mean intersection over union value to identify failure instances in addition to forecasting the mean intersection over union. This paper talks about how to separate brain tumors from magnetic resonance images of patients taken from "Brain web." Using basic ideas of digital image processing, magnetic resonance images are used to extract and find tumors using a hybrid method. In this paper, the proposed algorithm is applied with the help of MATLAB. In medical image processing, brain tumor segmentation is an important task. The goal of this paper is to look at different ways to divide brain tumors using magnetic resonance imaging. Recently, automatic segmentation using deep learning methods has become popular because these methods get the best results and are better at solving this problem than others. Deep learning methods can also be used to process and evaluate large amounts of magnetic resonance imaging image data quickly and objectively. A classification method based on a convolution neural network is also added to the proposed scheme to make it more accurate and cut down on the amount of time it takes to do the calculations. Also, the results of the classification are given as images of a tumor or a healthy brain. The training is 98.5% correct. In the same way, both the validation accuracy and validation loss are high.

Brain tumor is a type of cancer which causes abnormal cells growth in the brain. It can be cured if we detect the brain tumor at an early stage. Brain tumor patients often suffer from blood clot, movement control loss, vision loss, behavioral changes, hormone changes, etc. The location, type, and size of the tumor have an effect on the normal functioning of the individual. To examine the size, shape, and location of tumor in the brain, Magnetic Resonance Imaging (MRI) is used. MRI image produces a clear anatomical view of brain and any small abnormality of brain is perceptible by MRI image. Brain tumor classification, detection, and segmentation are huge concerns of researchers. Artificial Intelligence (AI)-based methods can be used to detect brain tumor. Among various deep learning-based algorithms CNN acquired a better position in image classification. CNN classification accuracy depends on some network parameters as convolutional filters, rectification functions, polling functions, and iteration numbers, etc. It is a problem to determine effective values of the convolution filter size, number, and convolution stride for better accuracy in classification. CNN has the capabilities to produce and learn effective features on large datasets. In this paper, we are using convolutional neural network model which takes the feature maps preprocessed to classify the MRI brain image datasets. This paper uses deep learning which processes NIFTI images and creates 3D convolutional neural network, which can be trained to detect and segment structures if corresponding ground truth labels are provided for training.

The aim of this paper is to present a new approach for improving deformable models and especially the Level Set Method (LSM) for segmentation and extraction of brain tumors in MRI (Magnetic Resonance Imaging) with more accuracy, the contribution of this work is to exploit thermal behavior of brain tumors for correcting level set contours. Human Body temperature distribution is an indicator of health condition, the brain tumor cells generate more heat than normal brain cells due to their higher metabolism and their vascular dilation. Heat distribution in human body is modeled using Pennes BioHeat Transfer Equation (PBHTE) solved by Finite Volume Method (FVM), and with the inverse analysis using Genetic Algorithm (GA) we will estimate the size and location of brain tumor, with this way Level Set Method extracts tumors contours with more accuracy and efficiency. To our knowledge, this is the first approach which introduces thermal analysis to improve the accuracy of segmentation and extraction of tumors in MRI images.

Brain tumor is the phenomenal growth of abnormal tissues in the human brain. A malignant tumor is known as a cancerous cell, and it is the major cause of death among people across the globe. Nowadays, the detection and classification of brain tumors from Magnetic Resonance Images (MRI) is a very crucial task. This chapter addresses various computing methods such as Edge Detection (ED), feature extraction, and classification techniques for the detection and classification of brain tumor regions from MRI datasets. Initially, MRI images are collected, and then various pre-processing steps such as filtering and edge detection are applied. Then, different edge detection methods such as Sobel ED, Robert’s ED, Prewitt ED, Canny ED, Laplacian ED, and Laplacian of Gaussian (LoG) with sigma 3 are applied for the MRI datasets. Subsequently, segmentation techniques are applied for detecting tumor regions from MRI, and essential features are extracted using Discrete Wavelet Transform (DWT) method. Otsu’s thresholding and K-means clustering segmentation methods are used for the investigation. Further, the support vector machines (SVMs), Naïve Bayes (NB), K-nearest neighbors (KNN), Back Propagation Neural Network (BPNN), and feedforward neural networks (FFNN) classifiers are employed for the MRI classification purpose. The experiments are 146conducted using the brain tumor dataset in the MATLAB 2019a software environment. The experimental results are analyzed in multiple dimensions, and it shows that the SVM with Otsu’s thresholding method exhibits better performance with 86.11% accuracy during the classification.

Currently, the radiologist can more accurately identify brain tumours through the development of Computer-Assisted Diagnosis (CAD), Machine Learning and Deep Learning. Recently, Deep Learning (DL) strategies have gained traction as a means to rapidly and accurately construct automated systems for diagnosing and segmenting the image. The standard approach to this issue is to create a custom feature for classification. Most neurological diseases originate from abnormal growth of brain cells, which can compromise brain architecture and even lead to malignant brain tumours. Brain tumour detection and classification algorithms that are both quick and accurate have been the subject of extensive study. This facilitates the straight forward diagnosis of brain tumours using Magnetic Resonance Image (MRI) images. Through Deep Learning (DL) model the diagnosis of brain malignancies in MRI images using Convolutional Neural Network (CNN) is possible by training the data. So, in this paper the brain tumouris predicted byproposing a Hybridfeature extraction technique i.e., tuned CNN model with ResNet150 and U-net.

In view of insights of the Central Brain Tumor Registry of the United States (CBTRUS), brain tumor is one of the main sources of disease related deaths in the World. It is the subsequent reason for tumor related deaths in adults under the age 20-39. Magnetic Resonance Imaging (MRI) is assuming a significant job in the examination of neuroscience for contemplating brain images. The investigation of brain MRI Images is useful in brain tumor analysis process. Features will be extricated and selected from the segmented pictures and afterward grouped by utilizing the classification procedures to analyze whether the patient is ordinary (having no tumor) or irregular (having tumor). One of the most dangerous cancers is brain tumor or cancer which affects the human body's main nervous system. Infection that can affect is very sensitive to the brain. Two types of brain tumors are present. The tumor may be categorized as benign and malignant. The benign tumor represents a change in the shape and structure of the cells, but cannot contaminate or spread to other cells in the brain. The malignant tumor can spread and grow if not carefully treated and removed. The detection of brain tumors is a difficult and sensitive task involving the classifier's experience. In the proposed work a Group based Classifier for Brain Tumor Recognition (GbCBTD) is introduced for the efficient segmentation of MRI images and for identification of tumor. The use of Convolutional Neural Network (CNN) system to classify the brain tumor type is presented in this work. Relevant features are extracted from images and by using CNN with machine learning technique, tumor can be recognized. CNN can reduce the cost and increase the performance of brain tumor detection. The proposed work is compared to the traditional methods and the results show that the proposed method is effective in detecting tumors.

Brain tumor segmentation in magnetic resonance imaging (MRI) has become emanant research area in the field of medical imaging system. To find the exact size and location of tumor, Brain tumor detection is very useful. K-means clustering algorithm is proposed in this paper for tumor detection based on segmentation and morphological operation. Firstly, MRI scanned image is pre-processed. The image is then subjected to the K-means clustering after that morphological operator is applied to extract the tumor in the pre-processed MRI scanned image. Finally, the area of extracted tumor part is calculated.

Abstract: A brain tumour is described by the presence of abnormal cells in the brain's tissues. Brain tumours can be benign (not cancerous) or malignant (cancerous). The malignant brain tumour is one of the leading and common cancers in the world. There are two types of tumours, primary tumours that develop in the brain and secondary tumours that start in another region of the body and then spread to the brain. The precise identification of the size and location of a brain tumour is crucial in the diagnosis of a brain tumour and is often diagnosed with magnetic resonance imaging (MRI). This book chapter discusses the major types of brain tumours and the advancements in computeraided approaches for detecting brain tumours. The manuscript gives an overview of various recent machine learning and medical image processing approaches developed recently for the identification and classification of brain tumours. Several medical image dataset available for the research works in this domain is also briefed in this article. The major research challenges which can be addressed by the researchers in the domain of brain tumour detection are also discussed in this article. Method: This manuscript mainly discusses various medical image processing methods to detect brain tumors. Results: The manuscript discusses the efficiency of the existing schemes and the key areas where further improvement is required Conclusion: This manuscript gives an overview of various categories of brain tumors, existing methods to detect brain tumors, the critical challenges in this domain and the medical image dataset available for the study.

Brain tumor disease is a serious and complex health problem worldwide. Early and accurate detection of brain tumors has a major impact on patient care and prognosis. Magnetic Resonance Imaging (MRI) has become one of the main diagnostic tools in detecting brain tumors, manual interpretation of MRI images requires high clinical expertise and requires a long time. In recent years, advances in deep learning techniques and image processing have opened up new opportunities in the detection of brain tumors via MRI images. Deep learning techniques, especially the use of Vision Transformers (ViTs) models, have been successful in various complex pattern recognition tasks in images. The Vision Transformers model was chosen due to the performance improvements shown in many image recognition tasks, outperforming convolutional neural networks (CNN) based methods. Tensorflow and Keras are used as frameworks for development and training models, which have been proven effective and efficient in various previous studies. This study focuses on the performance of the Vision Transformer (ViT) in detecting brain tumors through two Magnetic Resonance Imaging (MRI) image datasets, with different numbers of datasets, as well as the maximum accuracy value that can be achieved from the ViT architecture. From several experimental parameters on ViT, the number of datasets and iterations, the results obtained from the first dataset with 253 image data obtained an accuracy value of 88%, and in the second study by combining the two datasets, with 3.123 data images obtained an accuracy of 97.9%.

Artificial intelligence technology is making a growing influence in the healthcare industry. Growth of abnormal cells or lump in your brain is known as a brain tumour. Many different types of brain tumours exist. Primarily brain tumors can be classified into benign (non -cancerous) and malignant (cancerous). According to a random-effects model, the total incidence rate of all brain cancers is 10.82 (95% CI: 8.63-13.56) per 100 000 person-years. The most aggressive and potentially fatal brain tumours, gliomas grow extremely quickly. Due to their uneven shape and diffused borders with the surrounding area, gliomas might be difficult to segment using computer-aided diagnostics. A lot of work is being done to improve the effectiveness of Brain tumour Detection in the early stages. The most popular technique for visualising important brain areas is magnetic resonance imaging (MRI). MRI processing is one of the components of image processing, which has recently experienced the largest growth. The growth Detection is frequently the first stage. This study explains the thresholding method for brain tumour identification. The suggested method can effectively be used to recognise and isolate brain tumours in MRI images. It functions as a useful tool for doctors who practise in this area. In this paper, a deep learning-based technique for segmenting brain tumours from several MRI modalities is given.

One of the dreadful diseases that the world encounters today is brain tumor. When abnormal cells form in the brain, it is called a brain tumor. There are lot of variations in sizes and location of tumor, and hence this makes it really hard for a complete understanding of tumor. Radiologists can easily diagnose the disease with the help of medical image techniques, but making this process automatic is obviously useful. Magnetic Resonance Imaging (MRI) is the most effective method for detecting brain tumors where, MRI images are trained and tested in order to detect the tumor. The automated system would be able to detect and pin-point the exact location of the tumor in an MRI image. In this project, the automated system is built using Mask Regional-based Convolution Neural Network (Mask R-CNN) which segments the abnormal tissues in the brain and mask is applied over the segmented region. Mask R-CNN has the best performance compared to other methods such as R-CNN (Regional-based CNN), Fast R-CNN and Faster R-CNN.

The abnormal or irregular growth of cells in regions of the human body that affects surrounding tissues is termed a tumor. Brain tumors are among the most dangerous and life-threatening types of tumors, arising from the abnormal growth of cells within the brain. However, existing detection methods often suffer from limitations, such as poor noise handling in MRI images, inaccurate segmentation, and low generalization across varying datasets. To resolve these difficulties, in this article, the convolutional-adaptive neuro fuzzy inference system (Conv-ANFIS) approach is devised for brain tumor detection from the magnetic resonance imaging (MRI) brain images. Conv-ANFIS is a combined model that merges a convolutional neural network (CNN) with an adaptive neuro-fuzzy inference system (ANFIS) and helps improve brain tumor detection accuracy. Initially, the de-noising of acquired input MRI images is done by applying a non-local means (NLM) filter. The skull portions are removed from the de-noised MRI image. Next, segmentation is performed by applying the structure correcting adversarial network (SCAN) to the image after skull removal. SCAN is a deep learning-based segmentation model designed for medical imaging. It ensures the accurate detection of tumors in MRI images. Subsequently, the features are mined from the segmented images by utilizing different feature extractors. Finally, the brain tumor is identified using the designed Conv-ANFIS model from the extracted feature vector. Furthermore, the effectiveness of Conv-ANFIS in detection is confirmed, achieving a recall of 92.31%, precision of 90.13%, and an F1-score of 91.21%, outperforming existing approaches.