MRI Brain Tumor Segmentation and Analysis using Rough-Fuzzy C-Means and Shape Based Properties

M2GCNet: Multi-Modal Graph Convolution Network for Precise Brain Tumor Segmentation Across Multiple MRI Sequences

Accurate segmentation of brain tumors across multiple MRI sequences is essential for diagnosis, treatment planning, and clinical decision-making. In this paper, I propose a cutting-edge framework, named multi-modal graph convolution network (M2GCNet), to explore the relationships across different MR modalities, and address the challenge of brain tumor segmentation. The core of M2GCNet is the multi-modal graph convolution module (M2GCM), a pivotal component that represents MR modalities as graphs, with nodes corresponding to image pixels and edges capturing latent relationships between pixels. This graph-based representation enables the effective utilization of both local and global contextual information. Notably, M2GCM comprises two important modules: the spatial-wise graph convolution module (SGCM), adept at capturing extensive spatial dependencies among distinct regions within an image, and the channel-wise graph convolution module (CGCM), dedicated to modelling intricate contextual dependencies among different channels within the image. Additionally, acknowledging the intrinsic correlation present among different MR modalities, a multi-modal correlation loss function is introduced. This novel loss function aims to capture specific nonlinear relationships between correlated modality pairs, enhancing the model's ability to achieve accurate segmentation results. The experimental evaluation on two brain tumor datasets demonstrates the superiority of the proposed M2GCNet over other state-of-the-art segmentation methods. Furthermore, the proposed method paves the way for improved tumor diagnosis, multi-modal information fusion, and a deeper understanding of brain tumor pathology.

Positron Emission Tomography Image Segmentation Based on Atanassov’s Intuitionistic Fuzzy Sets

In this paper, we present an approach to fully automate tumor delineation in positron emission tomography (PET) images. PET images play a major role in medicine for in vivo imaging in oncology (PET images are used to evaluate oncology patients, detecting emitted photons from a radiotracer localized in abnormal cells). PET image tumor delineation plays a vital role both in pre- and post-treatment stages. The low spatial resolution and high noise characteristics of PET images increase the challenge in PET image segmentation. Despite the difficulties and known limitations, several image segmentation approaches have been proposed. This paper introduces a new unsupervised approach to perform tumor delineation in PET images using Atanassov’s intuitionistic fuzzy sets (A-IFSs) and restricted dissimilarity functions. Moreover, the implementation of this methodology is presented and tested against other existing methodologies. The proposed algorithm increases the accuracy of tumor delineation in PET images, and the experimental results show that the proposed method outperformed all methods tested.

An efficient brain tumor image classifier by combining multi-pathway cascaded deep neural network and handcrafted features in MR images

Accurate segmentation and delineation of the sub-tumor regions are very challenging tasks due to the nature of the tumor. Traditionally, convolutional neural networks (CNNs) have succeeded in achieving most promising performance for the segmentation of brain tumor; however, handcrafted features remain very important in identification of tumor's boundary regions accurately. The present work proposes a robust deep learning-based model with three different CNN architectures along with pre-defined handcrafted features for brain tumor segmentation, mainly to find out more prominent boundaries of the core and enhanced tumor regions. Generally, automatic CNN architecture does not use the pre-defined handcrafted features because it extracts the features automatically. In this present work, several pre-defined handcrafted features are computed from four MRI modalities (T2, FLAIR, T1c, and T1) with the help of additional handcrafted masks according to user interest and fed to the convolutional features (automatic features) to improve the overall performance of the proposed CNN model for tumor segmentation. Multi-pathway CNN is explored in this present work along with single-pathway CNN, which extracts simultaneously both local and global features to identify the accurate sub-regions of the tumor with the help of handcrafted features. The present work uses a cascaded CNN architecture, where the outcome of a CNN is considered as an additional input information to next subsequent CNNs. To extract the handcrafted features, convolutional operation was applied on the four MRI modalities with the help of several pre-defined masks to produce a predefined set of handcrafted features. The present work also investigates the usefulness of intensity normalization and data augmentation in pre-processing stage in order to handle the difficulties related to the imbalance of tumor labels. The proposed method is experimented on the BraST 2018 datasets and achieved promising results than the existing (currently published) methods with respect to different metrics such as specificity, sensitivity, and dice similarity coefficient (DSC) for complete, core, and enhanced tumor regions. Quantitatively, a notable gain is achieved around the boundaries of the sub-tumor regions using the proposed two-pathway CNN along with the handcrafted features. Graphical Abstract This data is mandatory. Please provide.

B-Map: a fuzzy-based model to detect foreign objects in a brain

To cope up with the medical complications, scientists and physicians rely more on digitized historical evidence. It helps them to identify the disease and to develop new drugs and strategies. The authors have designed a model called B-Map. It can detect and segmenting any foreign object in the brain using fuzzy rules. The model can detect objects such as cancer and brain tumor. The proposed work aims at designing a classifier. The classifier would help to detect all possible foreign objects using one application. B-Map has been compared with benchmark algorithms such as K-means and ANN. It was found that the proposed model performs significantly better than the current techniques. Original patients' sample reports are taken from various medical laboratories. The figure numbers are retained as in the paper. The proposed model is able to find the edges and segment different types of foreign objects or one can say unexpected developments. Figure 12 shows the outer edges of a section of a brain MRI. The patient's MRI very clearly shows Hydrocephalus. The same is segmented and shown in Fig. 13. Figure 14 shows a segment of benign development and 15 shows a cancerous development which are again successfully segmented by the proposed model. The data on which testing is done is clinical data of the original patients. As the patient's details and data cannot be shared the author's cannot upload the data in the repository. As soon as the research completes, a benchmark dataset will be created and uploaded in public domain so that researchers can access it.

A Fully Automatic Procedure for Brain Tumor Segmentation from Multi-Spectral MRI Records Using Ensemble Learning and Atlas-Based Data Enhancement

The accurate and reliable segmentation of gliomas from magnetic resonance image (MRI) data has an important role in diagnosis, intervention planning, and monitoring the tumor’s evolution during and after therapy. Segmentation has serious anatomical obstacles like the great variety of the tumor’s location, size, shape, and appearance and the modified position of normal tissues. Other phenomena like intensity inhomogeneity and the lack of standard intensity scale in MRI data represent further difficulties. This paper proposes a fully automatic brain tumor segmentation procedure that attempts to handle all the above problems. Having its foundations on the MRI data provided by the MICCAI Brain Tumor Segmentation (BraTS) Challenges, the procedure consists of three main phases. The first pre-processing phase prepares the MRI data to be suitable for supervised classification, by attempting to fix missing data, suppressing the intensity inhomogeneity, normalizing the histogram of observed data channels, generating additional morphological, gradient-based, and Gabor-wavelet features, and optionally applying atlas-based data enhancement. The second phase accomplishes the main classification process using ensembles of binary decision trees and provides an initial, intermediary labeling for each pixel of test records. The last phase reevaluates these intermediary labels using a random forest classifier, then deploys a spatial region growing-based structural validation of suspected tumors, thus achieving a high-quality final segmentation result. The accuracy of the procedure is evaluated using the multi-spectral MRI records of the BraTS 2015 and BraTS 2019 training data sets. The procedure achieves high-quality segmentation results, characterized by average Dice similarity scores of up to 86%.

3D-MRI Brain Tumor Detection Model Using Modified Version of Level Set Segmentation Based on Dragonfly Algorithm

Accurate brain tumor segmentation from 3D Magnetic Resonance Imaging (3D-MRI) is an important method for obtaining information required for diagnosis and disease therapy planning. Variation in the brain tumor’s size, structure, and form is one of the main challenges in tumor segmentation, and selecting the initial contour plays a significant role in reducing the segmentation error and the number of iterations in the level set method. To overcome this issue, this paper suggests a two-step dragonfly algorithm (DA) clustering technique to extract initial contour points accurately. The brain is extracted from the head in the preprocessing step, then tumor edges are extracted using the two-step DA, and these extracted edges are used as an initial contour for the MRI sequence. Lastly, the tumor region is extracted from all volume slices using a level set segmentation method. The results of applying the proposed technique on 3D-MRI images from the multimodal brain tumor segmentation challenge (BRATS) 2017 dataset show that the proposed method for brain tumor segmentation is comparable to the state-of-the-art methods.

An efficient wavelet and curvelet-based PET image denoising technique

Positron emission tomography (PET) image denoising is a challenging task due to the presence of noise and low spatial resolution compared with other imaging techniques such as magnetic resonance imaging (MRI) and computed tomography (CT). PET image noise can hamper further processing and analysis, such as segmentation and disease screening. The wavelet transform-based techniques have often been proposed for PET image denoising to handle isotropic (smooth details) features. The curvelet transform-based PET image denoising techniques have the ability to handle multi-scale and multi-directional properties such as edges and curves (anisotropic features) as compared with wavelet transform-based denoising techniques. The wavelet denoising method is not optimal for anisotropic features, whereas the curvelet denoising method sometimes has difficulty in handling isotropic features. In order to handle the weaknesses of individual wavelet and curvelet-based methods, the present research proposes an efficient PET image denoising technique based on the combination of wavelet and curvelet transforms, along with a new adaptive threshold selection to threshold the wavelet coefficients in each subband (except last level low pass (LL) residual). The proposed threshold utilizes the advantages of adaptive threshold taken from BayesShrink along with the neighborhood window concept. The present method was tested on both simulated phantom and clinical PET datasets. Experimental results show that our method has achieved better results than the existing methods such as VisuShrink, BayesShrink, NeighShrink, ModineighShrink, curvelet, and an existing wavelet curvelet-based method with respect to different noise measurement metrics, such as mean squared error (MSE), signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), and image quality index (IQI). Furthermore, notable performance is achieved in the case of medical applications such as gray matter segmentation and precise tumor region identification. Graphical Abstract Block diagram of the proposed method. (a) Steps of the proposed denoising method and (b) image information generated by each step of (a).