Enhanced De-Noising Technique for Region Growing Segmentation

—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

Brain Magnetic Resonance Imaging (MRI) image segmentation is one of the critical technologies of clinical medicine, and is the basis of three-dimensional reconstruction and downstream analysis between normal tissues and diseased tissues. However, there are various limitations in brain MRI images, such as gray irregularities, noise, and low contrast, reducing the accuracy of the brain MRI images segmentation. In this paper, we propose two optimization solutions for the fuzzy clustering algorithm based on local Gaussian probability fuzzy C-means (LGP-FCM) model and anisotropic weight fuzzy C-means (AW-FCM) model and apply it in brain MRI image segmentation. An FCM clustering algorithm is proposed based on AW-FCM. By introducing the new neighborhood weight calculation method, each point has the weight of anisotropy, effectively overcomes the influence of noise on the image segmentation. In addition, the LGP model is introduced in the objective function of fuzzy clustering, and a fuzzy clustering segmentation algorithm based on LGP-FCM is proposed. A clustering segmentation algorithm of adaptive scale fuzzy LGP model is proposed. The neighborhood scale corresponding to each pixel in the image is automatically estimated, which improves the robustness of the model and achieves the purpose of precise segmentation. Extensive experimental results demonstrate that the proposed LGP-FCM algorithm outperforms comparison algorithms in terms of sensitivity, specificity and accuracy. LGP-FCM can effectively segment the target regions from brain MRI images.

The location of the tumor, the evaluation of its shape, its position and its interactions with nearby brain structures, etc. are signs and desired indicators. Nowadays clinical tools for in vivo observation areas of the brain such as X-ray scanner, the magnetic resonance imaging (MRI) et., are numerous. Among these techniques, MRI is increasingly used in clinical routine. The image allows precise localization of different structures. However, observation and tumor image analysis prove to be a delicate exercise that requires to use multiple acquisitions under different protocols. In this article we present a new method for detecting and extracting tumor regions applied on brain MRI images. This method consists of three main stages: (i) Extracting the region of interest (the brain) by using the EMBE method. (ii) to study and to make an histogram analysis of the MRI image in order to create learning and initialize the classification algorithm for retrieving and locating the tumor. (iii) Detection and extraction of the tumor, by using by the classification algorithm “Support Vector Machine”. The image will be split into two components which are Tumor class and No-tumor class. Our method will be completed by a characterization of the tumor area to specify its geometric properties. This work will help the radiologists in handling the numerous number of MRI images they have to deal daily. Our contribution will facilitate tumor detection and extraction in a most minimal time.

In this paper, we presented precise and proficient techniques for measuring the human thalamus, medial dorsal and the pulvinar nucleus with magnetic resonance imaging (MRI). In spite of the fact that thalamic nuclei are not straightforwardly visible on traditional MRI image. We applied a novel and competent image pre-processing techniques to enhance the visual quality of MRI image. In addition to this we have used various segmentation algorithms to accurately extract entire thalamus from brain MRI images. Diffusion MRI is used to extract various nucleus of thalamus. Several optimal features such as textures, morphological are derived from thalamus and medial dorsal regions which are then used to train the artificial neural network model (ANN). Our artificial neural network model accurately classifies between schizophrenic and healthy subjects based on thalamic anatomy for larger sample sizes.

Utilising Artificial Intelligence (AI) to Automate Defacing of the Nose in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) images

Recently, the Magnetic Resonance Imaging (MRI) images have limited and unsatisfactory resolutions due to various constraints such as physical, technological and economic considerations. Super-resolution techniques can obtain high-resolution MRI images. The traditional methods obtained the resolution enhancement of brain MRI by interpolations, affecting the accuracy of the following diagnose process. The requirement for brain image quality is fast increasing. In this paper, we propose an image super-resolution (SR) method based on overcomplete dictionaries and inherent similarity of an image to recover the high-resolution (HR) image from a single low-resolution (LR) image. We explore the nonlocal similarity of the image to tentatively search for similar blocks in the whole image and present a joint reconstruction method based on compressive sensing (CS) and similarity constraints. The sparsity and self-similarity of the image blocks are taken as the constraints. The proposed method is summarized in the following steps. First, a dictionary classification method based on the measurement domain is presented. The image blocks are classified into smooth, texture and edge parts by analyzing their features in the measurement domain. Then, the corresponding dictionaries are trained using the classified image blocks. Equally important, in the reconstruction part, we use the CS reconstruction method to recover the HR brain MRI image, considering both nonlocal similarity and the sparsity of an image as the constraints. This method performs better both visually and quantitatively than some existing methods.

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.

Rationale:Brain magnetic resonance imaging (MRI) images of atypical teratoid rhabdoid tumor (ATRT) often present heterogeneous signals of various cells without remarkable features of the disease. We describe a unique case of atypical brain MRI images presenting as an type II neurofibromatosis and explore some diagnostic hints.Patient concerns:A 1-year-and-7-month-old boy admitted to our department with a 7-day history of drowsiness and 2-day history of emesis, and his presenting complaint was repeated vomit. On physical examination, he had drowsiness, positive sun set sign, slow light reflection, high muscular tension of limbs and 55 cm head circumference. MRI presented masses of bilateral auditory nerve distribution area, the fourth ventricle and right frontal lobe, obstructive hydrocephalus, and amplified cisterna magna. Particularly, dumbbell shape tumor in left cerebellopontine angle area and the fourth ventricle showed iso- or hypo-intensity on T1-weighted image and mix-intensity on T2-weighted image with irregular frontier, obvious mutual high and low signal on T2-weighted image, and growing along cerebrospinal fluid pathway.Diagnosis:The diagnosis of type II neurofibromatosis (NF-II) was considered pre-operatively. After surgery, postoperative histopathology confirmed the diagnosis of ATRT.Interventions:After ventriculo-peritoneal (VP) shunt, no evidence of tumor was inspected in cerebrospinal fluid, and enhancement MRI showed heterogeneous contrast signal on dumbbell shape tumor. We executed an incomplete microsurgery for dumbbell shape lesion in left auditory nerve distribution area and the fourth ventricle for differential diagnosis and facilitating further treatment.Outcomes:The patient did not recover well postoperatively and suffered from severe pulmonary infection. Refusing further intervention in view of poor prognosis of ATRT, the patient was transferred to another hospital for rehabilitation care. The patient died from progressive tumor and respiratory failure after 2 months.Lessons:The diagnosis of ATRT can be challenging, in our case due to the disturbance of bilateral auditory nerve distribution area tumors. Under MRI, Irregular frontier, obvious mutual high and low signal on T2-weighted image, growing along cerebrospinal fluid pathway, and heterogeneous contrast enhancement should lead the clinician to strongly consider ATRT.

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.

Nowadays, sparse representation has been widely used in Magnetic Resonance Imaging (MRI). The commonly used sparse representation methods are based on symmetrical partition, which have not considered the complex structure of MRI image. In this paper, we proposed a sparse representation method for the brain MRI image, called GNAMlet transform, which is based on the gradient information and the non-symmetry and anti-packing model. The proposed sparse representation method can reduce the lost detail information, improving the reconstruction accuracy. The experiment results show the superiority of the proposed transform for the brain MRI image representation in comparison with some state-of-the-art sparse representation methods.

The magnetic resonance imaging (MRI) is very effective, yet non-invasive medical imaging technique for clinical diagnosis and for monitoring the abnormalities in neurological order. Currently, the analysis of microbleeds is performed by experienced neurologist based on their knowledge that is by scanning the image, detecting the black dots and identifying whether black dots are micro-bleeds or mimics. Medical image processing has made a great impact on medicine, diagnosis, and treatment. The most important part of image processing in medical is image segmentation. Watershed segmentation is an example of a robust technique for image segmentation. The common watershed transformation used for segmentation is the marker controlled segmentation. This conventional watershed algorithms for medical images is widespread because of its advantage to completely segment the medical images. However, the common drawback of watershed segmentation which is over-segmentation and its sensitivity to false edges segmentation. This paper introduces a new scheme of process to overcome the listed drawback by first applying clustering technique to produce the primary segmentation before applying the improved watershed algorithm. The uses of k-means algorithms which is unsupervised learning algorithms will help watershed segmentation to improve segmentation process. Then, the improved watershed segmentation used the level set thresholding based on gradient magnitude map to reduce the number of false edges and over segmentations. This proposed method demonstrates a significant improvement that may serves as a computer aided tools for radiologists in detecting microbleeds in MRI images.

In recent years, brain health has received increasing attention, but conventional acquisition of brain MRI (magnetic resonance imaging) images still suffer from issues such as missing data, artifacts, and high costs, which hinders research and diagnosis. With the application of deep learning in medical image synthesis, low-cost, efficient, and high-quality medical MRI synthesis techniques have become a prominent research focus and have gradually matured. However, traditional methods for synthesizing magnetic resonance imaging (MRI) mostly rely on generative adversarial networks, which require fine-tuning of parameters and learning rates to achieve stringent Nash equilibrium conditions, leading to problems such as gradient explosions and mode collapse. Building upon the latest research in synthetic models DDPM (denoising diffusion probabilistic model), we propose a novel approach for 2D brain MRI image synthesis based on a lightweight denoising diffusion probabilistic model. This method improves the attention module in the denoising diffusion probabilistic model to make it more lightweight. Additionally, we adopt the smooth L1 loss function as a replacement for the traditional mean absolute error (L1 loss) by comparing the error between the 2D brain MRI images with added noise and the real noise for training the model. Finally, we validate the proposed model on the MRI Brain Tumor Classification dataset, demonstrating that it achieves high-quality synthesis results while significantly reducing the parameter count of the DDPM model.

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.

For the problem of low accuracy using K-means clustering algorithm to segment noisy brain magnetic resonance imaging (MRI) images, this paper proposed a strategy to improve segmentation accuracy. Firstly, the strategy uses wavelet transform to brain MRI image denoising, secondly, brain MRI image is segmented by k-means clustering algorithm. Experimental results show that the proposed strategy can effectively improve the segmentation accuracy of the noisy MRI brain image and is universal to some extent.

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.