An improved watershed segmentation technique for microbleeds detection in MRI images

—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

Mutated Aquila Optimizer for assisting brain tumor segmentation

Alzheimer's disease (AD) is a kind of dementia characterized by memory loss in early stages. Damage to the nerve in the brain responsible for learning, reasoning, and remembering (a cognitive function) causes these symptoms. Treatment and patient care are most successful when they follow a timely and exact diagnosis. In this research, a Multi-Layered Dynamic- Feed Forward Neural Network (MLD-FFNN) method is used to analyse Magnetic resonance imaging (MRI) images to make accurate AD predictions. The approach has three essential components: image preprocessing, image segmentation, and classification. MRI datasets are used in the study to categorize subjects as non-demented, very mildly demented, mildly demented, or moderately demented. The initial step, data preprocessing, improves the quality of MRI images. Histogram equalization was designated as one of the methods discussed. Images' contrast may be improved with the use of technique called histogram equalization. Segmentation of images follows after preprocessing. Here, MRI images must be segmented into informative sections. The Gaussian filter (GF) method is used for image segmentation. When smoothing or filtering images, a method is used. Finally, a MLD-FFNN method is utilized for categorization. The segmented MRI scans are presumably the input for this neural network, which is trained to predict whether or not a patient has AD. This research shows effectiveness for early detection and treatment of AD because of its potential for precise prediction. Based on experimental results, this investigation achieved higher accuracy than other methods. The automated AD classification has the potential to serve as a useful aid for medical professionals in making diagnoses of AD and determining the best course of therapy.

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).

Medical image processing has gained a lot of relevance of late and is turning out to be a boon in different clinical aspects. One such emerging field is brain tumor detection from magnetic resonance images (MRI) of brain. Engineers are actively developing tools to process medical images and aid doctors in with their diagnosis. MRI images are clinical images which are obtained on a computer when a patient goes through MRI scanning done by a respective machine. A brain tumor is a cluster of an abnormal mass of tissue where cell growth is out of control due to an abnormality in the mechanisms that control normal cells. In this work, our main aim is to measure the size of a brain tumor in terms of its diameter and area from a patient's MRI imagery using MATLAB. The proposed method incorporates different layers of noise removal techniques to clean up the images as well as image segmentation and morphological operations for detection and extraction of tumors and determining their size. By measuring the size of the tumor in a patient's brain at regular intervals doctors can diagnose the severity of the tumor in a patient's brain and can administer suitable treatment before it gets malignant.

In the last decades, image processing has become an interesting task in the field of medical imaging. one of the most important medical imaging techniques used for diagnosis is the magnetic resonance image (MRI) which is considered to be a very useful medical tool for detecting the tumor progression of multiple sclerosis MS. The segmentation of brain tumors from MRI images is an effective technique. Several recent approaches for segmentation and classification of MRI sequences have been proposed for the automatic detection of MS outliers. This paper illustrates an algorithm based on the meta-heuristic “Ant Colonies Optimization ACO” for the segmentation of MRI images. We propose to apply the technique of ant colonies to estimate the segmentations of brain MRI images from a novel MR datasets of 30 MS patients, which were acquired with a 3T MR scanner with conventional sequences in order to optimize their overall rendering and to compare with consensual segmentation. The submitted algorithm is evaluated by using MATLAB GUI program.

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.

This study investigates the value of magnetic resonance imaging (MRI) based on a deep learning algorithm in the diagnosis of diabetic macular edema (DME) patients. A total of 96 patients with DME were randomly divided into the experimental group (N = 48) and the control group (N = 48). A deep learning 3D convolutional neural network (3D-CNN) algorithm for MRI images of patients with DME was designed. The application value of this algorithm was comprehensively evaluated by MRI image segmentation Dice value, sensitivity, specificity, and other indicators and diagnostic accuracy. The results showed that the quality of MRI images processed by the 3D-CNN algorithm based on deep learning was significantly improved, and the Dice value, sensitivity, and specificity index data were significantly better than those of the traditional CNN algorithm (P < 0.05). In addition, the diagnostic accuracy of MRI images processed by this algorithm was 93.78 ± 5.32%, which was significantly better than the diagnostic accuracy of 64.25 ± 10.24% of traditional MRI images in the control group (P < 0.05). In summary, the 3D-CNN algorithm based on deep learning can significantly improve the accuracy and sensitivity of MRI image recognition and segmentation in patients with DME, can significantly improve the diagnostic accuracy of MRI in patients with DME, and has a good clinical application value.

Background/Objectives: In recent years, medical imaging plays an important role to detect diseases. Especially, Magnetic Resonance Imaging (MRI) images are indispensable for tumor detection. In medical image processing, the Region Based Segmentation (RBS) algorithms have attained an essentially significance to detect the tumor and are utilized for optimum results with the segregation of tumor part in the MRI image. The aim of the work is to provide effective algorithm to extract tumor and size, the process of de-noising technique, segmentation and extraction is the best way for this. At first, the enhanced de-noising method helps to enhance the MRI image to extract the tumor alone. Methods/Statistical Analysis: In image processing, automatic image segmentation plays a vital role and for RBS, the selection of seed has to done automatically to achieve this. Even if this technique is well performed with noises, the images are difficult to segment due to pixel similarity and the presence of noise. The noises can be removed by the combinations of median and Stationary Wavelet Transform (SWT) before preceding this, contrast enhancement is needed. In this paper, the combined features of de-noising technique are used for minimizing the effect of noises in the MRI brain images. After the process of denoising, the segmented results will be a better one than non-de-noise (i.e. Original) images. The extracted tumor results are compared by the various quality metrics as MSE, PSNR, NCC, AD, NAE, SE etc. with the ground truth image. This enhanced de-noising technique is used to test 50 images and is performance evaluated based on their MSE and PSNR. Findings: The enhanced de-noising technique gives better result than existing de-noising technique. Thus, the tumor extraction can be done easily. Improvements/Applications: This technique is used mainly for medical imaging applications.

In the last few years there has been rapid progress in the application of nuclear magnetic resonance clinical imaging as a diagnostic aid (Partain et al, 1983; Steiner, 1983; Worthington, 1983). Records kept by the operators of NMR imaging equipment indicate that about 1800 individuals have now been examined without incident within the United Kingdom. In view of this progress, an advisory group (Appendix) appointed by the National Radiological Protection Board recommends some modifications to the original advice given in 1981 (NRPB, 1981). The principal modifications are that: (1) It is no longer considered necessary to exclude volunteers with a history of epilepsy or cardiac disease. (2) It is not obligatory that a registered medical practitioner trained in resuscitation techniques be present during an exposure. (3) Higher limits are recommended for rates of change of magnetic flux density when the period of change is less than 10 ms. (4) The radiofrequency limit of 15 MHz has been removed. Theoretical considerations indicate that the interaction of an applied magnetic field with the intrinsic magnetic moments of protons and electrons within a molecule could alter its rate of chemical reaction (McLauchlan, 1981). Chemical reactions involving radical pair intermediates are expected to be sensitive to magnetic fields for this reason and a number of enzyme reactions that might be expected to be affected have been identified (A. Cass, personal communication). However, there is no experimental confirmation of these theoretical possibilities at present.

The region clustering of a magnetic resonance imaging (MRI) image is more complicate than a computed topography (CT) image because a MRI image composes of three components such as Tl -weighted, T2-weighted, and proton density (PD) in each layer. However, the MRI images provide more detail than the CT images. Therefore, we propose a technique of the region clustering of MRI image by using fuzzy c-means (PCM). The fuzzy c-means algorithm is an iterative operation, that is very time-consuming and makes the algorithm impractical for using in image segmentation. To cope with this problem, the discrete curve evolution (DCE) technique is applied to find the actual cluster center to refine the initial value of the fuzzy c-means algorithm, which reduces the convergence time. In experimental results, the proposed technique provides the same segmentation accuracy as the fuzzy c-means technique. Moreover, this technique takes lower computational time comparing to the previous method.

Detection of small lesions in magnetic resonance imaging (MRI) images is one of the most challenging tasks. Compared with detection in natural images, small lesion detection in MRI images faces two major problems: First, small lesions only occupy a small fraction of voxels within an image, yielding insufficient features and information for them to be distinguished from the surrounding tissues. Second, an accurate outline of these small lesions manually is time-consuming and inefficient even for medical experts in pathology. Hence, existing methods cannot accurately detect lesions with such a limited amount of information. To solve these problems, we propose a novel multi-task convolutional neural network (CNN), which simultaneously performs regression of lesion number and detection of lesion location. Both lesion number and location can be obtained through point annotations, which is much easier and efficient than a full segmentation of lesion manually. We use an encoder-decoder structure that outputs a distance map of each pixel to the nearest lesion centers. Additionally, a regression branch is added after the encoder to learn the counting of lesion numbers, thus providing an extra regularization. Note that these two tasks share the same encoder weights. We demonstrate that our model enables the counting and locating of extremely small lesions within 3–5 voxels (300 × 300 voxels per image) with a recall of 72.66% on a large mouse brain MRI image dataset (more than 1000 images), and outperforms other methods.

The Brain is the major central monitoring region of the human nervous system. Tumor growth is a speedy and abandoned growth of unhealthy cells of the brain. Magnetic Resonance Imaging (MRI) is the method frequently used to identify tumor region in the brain. Usually, MR images are not that much appropriate for comprehensive examination, because of this reason medical image treating is one of the most significant, demanding and rising field during this era of modern medical technology. The fundamental goal of this proposed technique is to highlight the borders of tumor area. This research technique is based upon three major tasks filtration, Edge preservation, and Segmentation. For this purpose first step is to remove ‘salt and pepper’ noise from MR scan by applying median filter because of the efficiency, then preserve the edges in order to enhance the borders of tumor region. For this operation combination of two edge operators ‘Sobel and Canny’ are used to design hybrid edge operator and display all the entities present in MR scan. Segmentation is one of the most important techniques required to evaluate and diagnose the brain tumor. Segmentation techniques are used to split an image in multiple regions. In segmentation pixels are labeled with same characteristics many different methods are used for segmentation of medical images just like C-means clustering, K-means clustering, region merging, region splitting and watershed segmentation etc. This proposed technique is the combination of three major segmentation approaches ‘K-means clustering, Watershed algorithm and Otsu binarization. This paper has displayed the contrast between all techniques at each step and also implemented the hybridized result by extracting tumor region in MR image. Results have compared and evaluated on the basis of different image parameters like MSE and PSNR, etc.

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

Medical images are widely used by the physicians to find abnormalities in human bodies. However the images sometimes are corrupted with a noise which normally exist or occurs during storage, or while transfer the image and sometimes while handling the devices. Therefore the need to enhance the image is crucial in order to improve the image quality. Segmentation technique for Magnetic Resonance Imaging (MRI) of the brain is one of the method used by radiographer to detect any abnormality happened specifically for brain. In this study we have proposed a method for segment the normal and abnormal tissues in the MRI images. At first we select the input image from the BMRI database. Then apply the skull stripping method to the input brain image. After that the proposed method perform the segmentation technique with the help of improved artificial neural networks here the weights are optimized by means of adaptive genetic algorithm. After the classification, the normal tissues like White Matter (WM), Grey Matter (GM) and Cerebrospinal Fluid (CSF) are segmented from the normal BMRI image. Abnormal tissues like Tumor and Edema are segmented from the abnormal BMRI images. The abnormal tissue segmentation will be carried out by optimal SVD and HHT based segmentation in which optimization can be done by Binary cuckoo search algorithm. Both the classification and the segmentation performance of the proposed technique are evaluated in terms of accuracy, sensitivity and specificity. The implementation of the proposed method is done in the working platform of MATLAB.