Parametric active contour model-based tumor area segmentation from brain MRI images using minimum initial points

—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

A new clinical diagnosis system for detecting brain tumor using integrated ResNet_Stacking with XGBoost

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).

Accurately separating healthy tissue from tumorous regions is crucial for effective diagnosis and treatment planning based on magnetic resonance imaging (MRI) data. Current manual detection methods rely heavily on human expertise, so MRI-based segmentation is essential to improving diagnostic accuracy and treatment outcomes. The purpose of this paper is to compare the performance of detecting brain tumors from MRI images through segmentation using an unmodified and modified U-Net architecture from deep neural network (DNN) that has been modified by adding batch normalization and dropout on the encoder layer with and without the freeze layer. The study utilizes a public 2D brain tumor dataset containing 3064 T1-weighted contrast-enhanced images of meningioma, glioma, and pituitary tumors. Model performance was evaluated using intersection over union (IoU) and standard metrics such as precision, recall, f1-score, and accuracy across training, validation, and testing stages. Statistical analysis, including ANOVA and Duncan's multiple range test, was conducted to determine the significance of performance differences across the architectures. Results indicate that while the modified architectures show improved stability and convergence, the freeze layer model demonstrated superior IoU and efficiency, making it a promising approach for more accurate and efficient brain tumor segmentation. The comparison of the three methods revealed that the modified U-Net architecture with a freeze layer significantly reduced training time by 81.72% compared to the unmodified U-Net while maintaining similar performance across validation and testing stages. All three methods showed comparable accuracy and consistency, with no significant differences in performance during validation and testing.

HQNet: A hybrid quantum network for multi-class MRI brain classification via quantum computing

Abstract: This work explores a novel approach to accurately identify brain tumors from magnetic resonance imaging (MRI) images by utilizing the complementary capabilities of fuzzy C-Means (FCM) clustering and support vector machines (SVM). Using the soft clustering approach, MRI images are meticulously segregated in the first stage, known as FCM clustering. By giving individual voxels varying degrees of membership, FCM deftly handles the inherent ambiguities and complexity of medical pictures to produce a nuanced representation of tissue kinds. SVM, a potent supervised learning technique for binary classification applications, comes after segmentation. uses this information in the second phase of classification. Through the creation of a hyperplane that divides tumor and non-tumor regions to the greatest extent possible, support vector machines (SVM) are able to optimize the margin between these classes and improve their ability to generalize. We deliberately integrate FCM clustering with SVM classification in order to take use of the complementary characteristics that each of these techniques possesses. While SVM's strong classification skills enable it to fine-tune the identification of tumor regions, FCM's sophisticated segmentation captures the spatial distribution of tissues.

The DCT-CNN-ResNet50 architecture to classify brain tumors with super-resolution, convolutional neural network, and the ResNet50

Here, a brain tumor classification method using the support vector machine (SVM) algorithm by utilizing discrete wavelet transform (DWT) transformation and feature extraction of gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP) has been implemented using the magnetic resonance imaging (MRI) image belong to the low-grade glioma (LGG) or high-grade glioma (HGG) group. SVM algorithm used as a classification method has been widely used in research that raises the topic of classification. Through the formation of a hyperplane between 2 data classes, the SVM algorithm can be said to be a reliable method but does not require complicated computations. The DWT transformation is intended to provide clearer feature details from the MRI image, so that when the feature extraction algorithm is applied, it is expected that the extracted features will differ between benign tumor MRI images and malignant tumor MRI images. In 1 level DWT using high-low (HL) sub-band yield the highest specificity, sensitivity, and accuracy than using 3 levels using HL or low-high (LH) sub-band in LGG MRI image.Compared with another research, our proposed method is slightly better in terms of accuracy to classify the brain tumor image with achieved the accuracy of 98.6486%.

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.

There is an emerging need for medical imaging data to provide patients with timely diagnosis. Magnetic resonance imaging (MRI) images based on brain tumor segmentation approaches possess greater importance in planning treatment. Though, mechanizing the process with different imaging conditions and accuracy is a major challenge due to variations in tumor structures. Hence, an efficient optimization-driven classifier, called BirCat optimization-based deep belief network (BirCat-based DBN)isdeveloped to detect brain tumors. The introduced BirCat is devised by incorporating birdswarm algorithm (BSA) into cat swarm optimization (CSO) algorithm and is employed in tuning the DBN classifier. Here, the first step is pre-processing, where noises, as well as artifacts in input image, are eliminated by means of ROI extraction and filtering method. Then, for segmentation, region growing algorithm is used in which the distance is calculated by the modified Bhattacharya measure. Afterward, each segment is adapted for mining the segment-based features and pixel-based features used for classification. Then, the feature vector is formed and given to the DBN classifier, which is tuned with the help of the introduced BirCat for brain tumor detection. The introduced technique effectively determines the regions with the tumor in the input MRI image. Finally, the change detection is evaluated by analyzing the post-operative MRI image and the segmented image by means of pixel mapping strategy with respect to SURF features. The pixel mapping is utilized to evaluate the percentage change in tumor pixels. The proposed BirCat surpassed other prevailing approaches by producing maximal values of specificity, accuracy, sensitivity, F1-score, and Dice score at 0.92, 0.927, 0.938, 0.909, and 0.937, correspondingly, for dataset 2.

Medical imaging like MRI and CT scan images are crucial for accurately diagnosing human brain disease. The traditional method for tumour analysis relies on the radiologist or physician visually inspecting the specimen, which can result in some incorrect classifications when a large number of MRI pictures need to be processed. An automated intelligent classification system is suggested that requires picture categorization in order to reduce human mistake rates. One of the illnesses that kills the majority of individuals worldwide is the brain tumour. If the tumour is accurately anticipated at an early stage, the likelihood that someone would survive can be increased. The human brain is studied using the magnetic resonance imaging (MRI) method to identify illnesses. In this project, Support Vector Machines (SVM)-based classification approaches are suggested and implemented to classify brain images; DWT will extract features from MRI images. The primary goal of this research is to provide a superior result, which is higher accuracy and reduced error rates for SVM-based MRI brain tumour prediction.

Brain tumour segmentation from MRI using superpixels based spectral clustering

A brain tumor is a dangerous neural illness produced by the strict growth of prison cells in the brain or head. The segmentation, analysis, and separation of unclean tumor parts from Magnetic Resonance Imaging (MRI) images are the main sources of anxiety. To report the segmented MRI images including tumor, the usage of computer-assisted methods is necessary. In this paper, a Convolutional Neural Network (CNN) approach is applied to identify brain cancers in MRI images. Two datasets are used in this study, namely Kaggle Brain MRI database and Figshare Brain MRI database. Models of deep CNN, consisting of VGG16, AlexNet, and ResNet, are utilized to extract deep features. The classification accuracies of the aforementioned Deep Learning (DL) networks are used to measure the efficiencies of the implemented systems. For the Kaggle database, AlexNet achieves 98% accuracy, VGG16 has 97% accuracy and ResNet has 66% accuracy. Among these networks, AlexNet has provided the highest level of accuracy. In the Figshare dataset, AlexNet and VGG16 both achieve 99% accuracy, and ResNet has 96% accuracy. In terms of accuracy, AlexNet and VGG16 outperform ResNet. These performances aid in the early detection of cancers before they cause physical harm such as paralysis and other complications.

Background: Alzheimer’s disease (AD) is a prevalent, neurological disease without effective treatment. However, if diagnosed early, the progression of the disease could be delayed through medication. Currently, one method to effectively diagnose AD early is to use Alternate Covering Neural Network (ACNN) network to discern various non-invasive Magnetic Resonance Imaging (MRI) images. This research aims to create an approach better than the current one and thus increase the accuracy of classifying MRI images, thereby diagnosing AD earlier and more perfectly. Methods: Alzheimer’s Disease Neuroimaging Initiative (ADNI; National Institutes of Health Grant U19 AG024904) database provided 3013 different sets of 3D MRI images labeled as cognitively normal (CN), mild cognitive impairment (MCI), and AD. A newly-proposed, modified Residual Network (ResNet) and an ACNN network were then constructed. Their common goal was to learn how to classify these labeled MRI images. After training, the two models got unique parameters for using the updated network to diagnose new images. Finally, inference, or testing the diagnostic accuracy of the two models, were performed based on another 469 different 3D MRI image sets. The accuracy of classification for two separate models were compared. Results: Compared with the ACNN network with a weighted classification accuracy of 80.17%, the newly proposed ResNet network enhances the weighted accuracy to 85.07% and showed statistical significance (p<0.001). Through analyzing the occurrence of falsepositive cases by two models, a Receiver Operating Characteristic (ROC) curve was drawn. The area under the curve of the ROC confirms this enhancement as the area under the curve of ROC is greater than that of the ACNN model in two of the three cases (MCI 0.9293>0.9196; AD 0.9389>0.9146). Conclusions: The research proposed a new deep learning convolutional network to classify 3D structural MRI images. The new ResNet is better in that it showed increased accuracy with statistical significance and had fewer false-positive results compare with the traditional ACNN network, thereby promising to help doctors diagnose AD more quickly and more accurately.

Objective To investigate the value of computed tomography (CT)-magnetic resonance imaging (MRI) image fusion in target volume delineation of postoperative precise radiotherapy for gliomas. Methods Thirty-six patients newly diagnosed with gliomas were enrolled and received postoperative intensity-modulated radiotherapy. Landmark (L) combined with manual fusion (M) and maximization of mutual information (MI) was used for image fusion. Target volume and organs at risk were delineated based on CT images and fused images, respectively. Comparison of the volume was made by pairwise t test. The distance from a landmark on CT image to the corresponding one on MRI image was calculated. The volume method and geometric center method were used to calculate the degree of volume overlap and changes in central positions after image fusion. Results The L+ M+ MI method achieved a high registration accuracy in image fusion, with a registration error less than 2 mm. In patients with grade Ⅲ-IV gliomas, the CT images yielded significantly smaller gross tumor volume (GTV) and clinical target volume (CTV) than the MRI images (74.62±46.91 vs. 105.99±58.86 cm3,P=0.042; 304.03±130.05 vs. 387.94±150.12 cm3,P=0.040). After image fusion, the smallest change in central position occurred in the chiasma (1.32±1.42 mm), and the largest change occurred in CTV (7.99±11.06 mm), followed by GTV and the brain stem. Conclusions CT-MRI image fusion helps to reduce the uncertainty of target volume delineation in patients with gliomas, especially in those with edema and residual tumor after surgery. Key words: Tomography, X-ray computed; Magnetic resonance imaging; Image fusion; Brain neoplasms/radiotherapy