Image Segmentation for MR Brain Tumor Detection Using Machine Learning: A Review.

Magnetic Resonance Imaging (MRI) has commonly been used to detect and diagnose brain disease and monitor treatment as non-invasive imaging technology. MRI produces three-dimensional images that help neurologists to identify anomalies from brain images precisely. However, this is a time-consuming and labor-intensive process. The improvement in machine learning and efficient computation provides a computer-aid solution to analyze MRI images and identify the abnormality quickly and accurately. Image segmentation has become a hot and research-oriented area in the medical image analysis community. The computer-aid system for brain abnormalities identification provides the possibility for quickly classifying the disease for early treatment. This article presents a review of the research papers (from 1998 to 2020) on brain tumors segmentation from MRI images. We examined the core segmentation algorithms of each research paper in detail. This article provides readers with a complete overview of the topic and new dimensions of how numerous machine learning and image segmentation approaches are applied to identify brain tumors. By comparing the state-of-the-art and new cutting-edge methods, the deep learning methods are more effective for the segmentation of the tumor from MRI images of the brain.

Selective image segmentation driven by region, edge and saliency functions.

Present active contour methods often struggle with the segmentation of regions displaying variations in texture, color, or intensity a phenomenon referred to as inhomogeneities. These limitation impairs their ability to precisely distinguish and outline diverse components within an image. Further some of these methods employ intricate mathematical formulations for energy minimization. Such complexity introduces computational sluggishness, making these methods unsuitable for tasks requiring real-time processing or rapid segmentation. Moreover, these methods are susceptible to being trapped in energy configurations corresponding to local minimum points. Consequently, the segmentation process fails to converge to the desired outcome. Additionally, the efficacy of these methods diminishes when confronted with regions exhibiting weak or subtle boundaries. To address these limitations comprehensively, our proposed approach introduces a fresh paradigm for image segmentation through the synchronization of region-based, edge-based, and saliency-based segmentation techniques. Initially, we adapt an intensity edge term based on the zero crossing feature detector (ZCD), which is used to highlight significant edges of an image. Secondly, a saliency function is formulated to detect salient regions from an image. We have also included a globally tuned region based SPF (signed pressure force) term to move contour away and capture homogeneous regions. ZCD, saliency and global SPF are jointly incorporated with some scaled value for the level set evolution to develop an effective image segmentation model. In addition, proposed method is capable to perform selective object segmentation, which enables us to choose any single or multiple objects inside an image. Saliency function and ZCD detector are considered feature enhancement tools, which are used to get important features of an image, so this method has a solid capacity to segment nature images (homogeneous or inhomogeneous) precisely. Finally, the adaption of the Gaussian kernel removes the need of any penalization term for level set reinitialization. Experimental results will exhibit the efficiency of the proposed method.

Segmentation of Intensity-Corrupted Medical Images Using Adaptive Weight-Based Hybrid Active Contours.

Segmentation accuracy is an important criterion for evaluating the performance of segmentation techniques used to extract objects of interest from images, such as the active contour model. However, segmentation accuracy can be affected by image artifacts such as intensity inhomogeneity, which makes it difficult to extract objects with inhomogeneous intensities. To address this issue, this paper proposes a hybrid region-based active contour model for the segmentation of inhomogeneous images. The proposed hybrid energy functional combines local and global intensity functions; an incorporated weight function is parameterized based on local image contrast. The inclusion of this weight function smoothens the contours at different intensity level boundaries, thereby yielding improved segmentation. The weight function suppresses false contour evolution and also regularizes object boundaries. Compared with other state-of-the-art methods, the proposed approach achieves superior results over synthetic and real images. Based on a quantitative analysis over the mini-MIAS and PH2 databases, the superiority of the proposed model in terms of segmentation accuracy, as compared with the ground truths, was confirmed. Furthermore, when using the proposed model, the processing time for image segmentation is lower than those when using other methods.

SRIS: Saliency-Based Region Detection and Image Segmentation of COVID-19 Infected Cases.

Noise or artifacts in an image, such as shadow artifacts, deteriorate the performance of state-of-the-art models for the segmentation of an image. In this study, a novel saliency-based region detection and image segmentation (SRIS) model is proposed to overcome the problem of image segmentation in the existence of noise and intensity inhomogeneity. Herein, a novel adaptive level-set evolution protocol based on the internal and external functions is designed to eliminate the initialization sensitivity, thereby making the proposed SRIS model robust to contour initialization. In the level-set energy function, an adaptive weight function is formulated to adaptively alter the intensities of the internal and external energy functions based on image information. In addition, the sign of energy function is modulated depending on the internal and external regions to eliminate the effects of noise in an image. Finally, the performance of the proposed SRIS model is illustrated on complex real and synthetic images and compared with that of the previously reported state-of-the-art models. Moreover, statistical analysis has been performed on coronavirus disease (COVID-19) computed tomography images and THUS10000 real image datasets to confirm the superior performance of the SRIS model from the viewpoint of both segmentation accuracy and time efficiency. Results suggest that SRIS is a promising approach for early screening of COVID-19.

Hybrid two-stage active contour method with region and edge information for intensity inhomogeneous image segmentation.

This paper presents a novel two-stage image segmentation method using an edge scaled energy functional based on local and global information for intensity inhomogeneous image segmentation. In the first stage, we integrate global intensity term with a geodesic edge term, which produces a preliminary rough segmentation result. Thereafter, by taking final contour of the first stage as initial contour, we begin second stage segmentation process by integrating local intensity term with geodesic edge term to get final segmentation result. Due to the suitable initialization from the first stage, the second stage precisely achieves desirable segmentation result for inhomogeneous image segmentation. Two stage segmentation technique not only increases the accuracy but also eliminates the problem of initial contour existed in traditional local segmentation methods. The energy function of the proposed method uses both global and local terms incorporated with compacted geodesic edge term in an additive fashion which uses image gradient information to delineate obscured boundaries of objects inside an image. A Gaussian kernel is adapted for the regularization of the level set function and to avoid an expensive re-initialization. The experiments were carried out on synthetic and real images. Quantitative validations were performed on Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) 2015 and PH2 skin lesion database. The visual and quantitative comparisons will demonstrate the efficiency of the proposed method.

Segmentation of Left and Right Ventricles in Cardiac MRI Using Active Contours.

Segmentation of left and right ventricles plays a crucial role in quantitatively analyzing the global and regional information in the cardiac magnetic resonance imaging (MRI). In MRI, the intensity inhomogeneity and weak or blurred object boundaries are the problems, which makes it difficult for the intensity-based segmentation methods to properly delineate the regions of interests (ROI). In this paper, a hybrid signed pressure force function (SPF) is proposed, which yields both local and global image fitted differences in an additive fashion. A characteristic term is also introduced in the SPF function to restrict the contour within the ROI. The overlapping dice index and Hausdorff-Distance metrics have been used over cardiac datasets for quantitative validation. Using 2009 LV MICCAI validation dataset, the proposed method yields DSC values of 0.95 and 0.97 for endocardial and epicardial contours, respectively. Using 2012 RV MICCAI dataset, for the endocardial region, the proposed method yields DSC values of 0.97 and 0.90 and HD values of 8.51 and 7.67 for ED and ES, respectively. For the epicardial region, it yields DSC values of 0.92 and 0.91 and HD values of 6.47 and 9.34 for ED and ES, respectively. Results show its robustness in the segmentation application of the cardiac MRI.

Active Contours Using Additive Local and Global Intensity Fitting Models for Intensity Inhomogeneous Image Segmentation.

This paper introduces an improved region based active contour method with a level set formulation. The proposed energy functional integrates both local and global intensity fitting terms in an additive formulation. Local intensity fitting term influences local force to pull the contour and confine it to object boundaries. In turn, the global intensity fitting term drives the movement of contour at a distance from the object boundaries. The global intensity term is based on the global division algorithm, which can better capture intensity information of an image than Chan-Vese (CV) model. Both local and global terms are mutually assimilated to construct an energy function based on a level set formulation to segment images with intensity inhomogeneity. Experimental results show that the proposed method performs better both qualitatively and quantitatively compared to other state-of-the-art-methods.