MRI Pictures Research Articles

An efficient medical image classification using deep learning techniques

Objective: Image Processing will be preparing of pictures utilizing numerical activity by utilizing some other sign handling for which the info is a picture. The yield of picture handling might be either a picture or some other structure identified with picture. Method: In therapeutic application Image Fusion is the way toward consolidating pertinent data from at least two pictures into a sign picture. The subsequent melded picture will be more instructive than any of the yield pictures. The division and order are utilized to discover the tumor part in the melded picture. Different existing multi scale change, for example, DWT, ST, DTWT, DCT produces curios in the intertwined picture. Another restorative picture combination plot dependent on a Stationary Wavelet Transform is proposed. Result: The First procedure is to apply the preprocessing technique to expel the clamor from the picture. Stationary Wavelet change is utilized to deteriorate the picture. Morphological work is utilized to extricate the component of the picture. The proposed picture gives the top notch picture when contrasted with past technique. At last, division and grouping strategies are utilized to locate the influenced zone in the melded picture. Conclusion: For Segmentation GMM (Gaussian Mixture Model) is proposed. For arrangement CNN (Convolutional Neural Networks) is proposed. In this proposed technique CT and MRI pictures are utilized as info picture to deliver progressively useful picture.

Brain Tumor Discovery Using Image Preprocessing

The segmentation of attractive reverberation images assumes a critical job in therapeutic fields since it removes the required territory from the picture. Generally, there is no unique methodology for the segmentation of the picture. Tumour division from MRI information is a critical tedious manual undertaking performed by therapeutic specialists. In this paper, the Brain Cancer prediction System has been detailed. The framework utilizes PC based methods to recognize tumor squares and classify the tumour utilizing Artificial Neural Network. The picture preparing strategies, for example, histogram evening out, picture division, picture improvement, and highlight extraction, have been produced for the location of the cerebrum tumor in the MRI pictures of the malignant growth Detected patients. This paper focuses around another and exceptionally acclaimed algorithm for mind tumor division of MRI scan image by ANN and SVM algorithms to analyze precisely the locale of malignant growth as a result of its straightforwardness and computational proficiency. The MATLAB output will be shown in pc and furthermore observe the yield to insert framework utilizing wired communication. To the best of our insight into the zone of therapeutic big data analytics, none of the current work concentrated on the two data types. Contrasted with a few runs of the typical algorithms, the computation precision of our proposed algorithm achieves 94.8% with an assembly speed, which is quicker than that of the Decision tree disease hazard prediction.

Brain Tumor Segmentation Using Deep Learning

Manual recognition of the cerebrum tumor for malignant growth determination from MRI pictures is a troublesome, repetitive and tedious assignment. The precision and the power of cerebrum Tumor discovery in this way, are significant for the determination, treatment arranging, and treatment result assessment. Generally, the programmed cerebrum tumor location techniques use hand structured highlights. Correspondingly, customary strategies for profound learning, for example, ordinary neural systems require a lot of commented on information to learn from, which is frequently hard to acquire in clinical area. Here, we portray another model two-pathway-bunch CNN (Convolutional Neural Network) design for cerebrum tumor recognition, which misuses neighborhood highlights and worldwide relevant highlights at the same time. This model implements equivariance in the two-pathway CNN model to decrease dangers and over fitting parameter sharing. At last, we implant the course engineering into two-pathway-brunch CNN in which the yield of an essential CNN is treated as an extra source and connected at the last year. Approval of the model on BRATS2013 and BRATS2015 information collections uncovered that inserting of a gathering CNN in to a two pathway engineering improved the general execution over the as of now distributed best in class while computational multifaceted nature stays alluring.

Classification of Brain Tumor using Convolutional Neural Networks

In medical science, brain tumor is the most common and aggressive disease and is known to be risk factors that have been confirmed by research. A brain tumor is the anomalous development of cell inside the brain. One conventional strategy to separate brain tumors is by reviewing the MRI pictures of the patient's mind. In this paper, we have designed a Convolutional Neural Network (CNN) to perceive whether the image contains tumor or not. We have designed 5 different CNN and examined each design on the basis of convolution layers, max-pooling, and flattening layers and activation functions. In each design we have made some changes on layers i.e. using different pooling layers in design 2 and 4, using different activation functions in design 2 and 3, and adding more Fully Connected layers in design 5. We examine their results and compare it with other designs. After comparing their results we find a best design out of 5 based on their accuracy. Utilizing our Convolutional neural network, we could accomplish a training accuracy and validation accuracy of design 3 at 100 epochs is 99.99% and 92.34%, best case scenario.

Identifying and Grouping Abnormalities in Medical Images using Shortest Path Algorithms

The majority of the patient conclusion rotates around in distinguishing variations from the norm in their particular restorative pictures. These pictures are of different kinds, likely Ultrasound, CT Scan, MRI and infinitesimal pictures like bio-synthetic slides, smaller scale organic slides and neurotic slides. Barely any irregularities are cracks, awful cells in blood, tumors, contagious recognizable proof and so on. Finding the unusual segments, abnormalities in these pictures needs aptitude by the doctor; this adept recognizable proof advances and ensures sound drug by the doctor or specialist to persistent. In medicinal infinitesimal pictures ordinary bits and strange segments are combined. None of the irregular segments are identified with strange and typical parts of picture for example deviations are dissipated among ordinary bits of picture. These deviations are absent in certain bits for explicit region in the pictures. None of these deviations are covered nor can be gathered into a solitary segment physically in the picture. Deviations can be segregated alongside typical segments of pictures. Recognizing such deviations incompletely goes under bunching. This venture recognizes deviations in Medical Microscopic pictures. These deviations can be distinguished outwardly which uncovers about the nearness of deviation however to know the level of deviation in an example picture is basic. So as to accomplish this all deviations must be associated. This task interfaces all deviations utilizing Shortest Path calculation and bunches utilizing Hierarchical Clustering calculations.

Identification of Brain Tumour in Histopathological Images using Neural Networks

Brain tumour is a rare explosion of cells present in brain and it is of two types such as benign and malignant. Mainly tumours occur anywhere in brain irrespective of its size, variance and structure. Without using MRI scan this dangerous brain tumour cannot be identified. The present effective way for brainstorming MRI pictures present in paper. Genuine datasets by dissimilar tumour figures, magnitudes, localities and inner surface are reserved. We take out the significant data (a tumour) from efficient separation utilizing improved convolution neural network (CNN) wherever Elman network is elaborated. So for this reason we determine the improved CNN built method by using MATLAB simulation.

Low Volume Brain Tumor Extraction

The segmentation of magnetic resonance images plays a very important role in medical fields because it extracts the required area from the image. By and large there is no novel methodology for the division of picture. Tumor segmentation from MRI data is an important but time consuming manual task performed by medical experts. This paper focuses on a new and very famous algorithm for brain tumor segmentation of MRI image by ANN algorithm to diagnose accurately the region of cancer because of its simplicity and computational efficiency. The MATLAB output will be displayed in pc and also see the output to embedded system using wired communication. Brain tumor is a life threatening disease. The cerebrum contains in excess of 10 billion working mind cells. The damaged brain cells are diagnosed themselves by splitting to make more cells. This regeneration takes place in an orderly and controlled manner. If the regeneration of the cells gets out of control, the cells will continue to divide developing a lump which is called tumor. In this paper a Brain Tumor Detection and Classification System has been designed and developed. The framework utilizes PC based strategies to recognize tumor squares and order the sort of tumor utilizing Artificial Neural Network in MRI pictures of various patients with astrocytoma kind of mind tumors.

Spatial Regularization and Adaptive Distance Metric Methods through DST for Tumor Segmentation

The process of segmentation in MRI pictures is turning into a significant assignment to be considered in clinical oncology applications, in view of the noise and blur that is available normally in MRI images. To minimize this natural disadvantages in the MRI images an imaging tool called belief theory is taken as the base alongside the proposed evidential clustering algorithm (ECM-MS) .This proposed technique joins the adaptive distance metric in so as to limit the clustering distortions and the comparability that happen between the voxels. The local homogeneity is measured by the spatial regularization dependent on the belief theory called Dempster Shafer Theory (DST). To get definite division the surface highlights are extricated from the data picture and is incorporated with the force of the voxels in the proposed strategy, thusly giving a decent presentation contrasted with different strategies.

Relationship between MRI findings after laparoscopic-assisted anorectal pull-through for anorectal malformations and the functional outcomes

Objective To explore the relationship between MRI findings and defecation function after laparoscopic-assisted anorectal pull-through for anorectal malformations. Methods A retrospective cohort study focused on the findings of MRI performed after laparoscopic-assisted anorectal pull-through for anorectal malformations and no spinal deformity was present in all patients who are currently older than 3 years. Forty-two patients aged from 3 to 16 years (median age was 4 years), whom accepted operation at 3 months to 13 years old(median age was 5 months), and MRI was carried out in 2 to 5 months after operation(median time was 3 months). The MRI manifestations including the relationship between the anorect and the high muscle complex(RAHMC), anorectal angle(AA);the relationship between the anorect and the low muscle complex(RALMC), the rectal maximum diameter(RMD), the fat tissue interposition(FTI) and the development of pelvic floor muscle(PFM) were reviewed and summarized. Statistically, the differences of MRI manifestations in different defecation function index, including the degree of voluntary bowel movements, soiling, constipation and Krickenbeck score were evaluated respectively with Chi-square test or rank sum test. Results statistically, There was significant differences among the groups with different degree of voluntary bowel movements, soiling and Krickenbeck score in the RAHMC (P 0.05) . Conclusions The deviation of the anorect from the high muscle complex which showed in the postoperative MRI pictures suggested the poor prognosis, and well-developed pelvic floor muscles which indicated in the MRI was not the sufficient predictor for good outcome. Postoperative anorectal angle obtained in the MRI can be used as an objective indicator of prognosis. Key words: Rectum; Anus diseases; Laparoscopes; Magnetic resonance imaging

Online histological atlas of the Göttingen minipig brain

BackgroundThe cytoarchitecture of the Göttingen minipig telencephalon has recently been elucidated in the published article (Bjarkam et al., 2017). The aim of the current paper is to describe how such data can be presented in an online histological atlas of the Gottingen minipig brain and how this atlas was constructed. MethodsTwo sets of histological sections were used. One set was photographed in high resolution and labelled, the other set in low resolution (resized first set) was used for reference on the computer screen. The two sets of microphotographs enable, using the freely available JQuery Image Zoom Plugin, the subsequent construction of a simple HTML-based atlas web page with a “virtual microscope like” style, which allowed magnifying of the base image (low-resolution image) up to the maximum resolution of the high-resolution image. In addition, we describe how the established histological atlas can be accompanied by a set of similar T1-weighted MRI pictures. Results and conclusionHistological and MRI pictures are presented in atlas form on www.cense.dk/minipig_atlas/index.html. The described pipeline represent a cheap and freely available way to present histological images, in online virtual microscopic atlas form, and may thus be of general interest to anyone who would like to present histological data accordingly.

The Dilemma of Cure and Damage in Oligodendroglioma: Ways to Tip the Balance Away from the Damage.

Current treatments for oligodendrogliomas are powerful but have a negative impact on the rest of the body. The bone marrow is damaged by the chemotherapeutics, but other parts of the body are also affected. In this paper, the current treatment method and its collateral damage is described. Therefore, therapies are needed that are more effective against the tumor while having less negative effects on the patient’s quality of life. Some potential therapies include optimal removal of the tumor by fluorescent-guided surgery (FGS), intraoperative desorption electrospray ionization-mass spectrometry (DESI-MS), better monitoring of the effects of therapy by pseudo-coloring shades of gray of MRI pictures, and using recent data from RNA sequencing of single cells and immunotherapy. These are all open new ways of treating this tumor. The RNA sequencing of single tumor cells unravels specific tumor antigens present in the differentiation status of the cancer cell. Stem cell antigens were expressed in dividing cells, while hypoxia inducible factor-α (HIF-1α) is expressed in all tumor cells. Cancer stem cell antigens can be loaded on dendritic cells to induce cytotoxic T-cells directed to cancer stem cells. These recent discoveries suggest a better quality of life with the same overall survival.

Increased glutamate and deep brain atrophy can predict the severity of multiple sclerosis.

In multiple sclerosis (MS), deep grey matter (DGM) atrophy has been recognised as a crucial component of the disease that presents early and it has been associated with disability. Although the precise mechanism underlying grey matter atrophy is unknown, several hypotheses have been postulated. Our previous research pointed to correlations of hypothalamic metabolic alterations with clinical outcomes of MS, therefore we decided to further test the relationship of these alterations with DGM atrophy. We used 1H-Magnetic Resonance spectroscopy (1H-MRS) of the hypothalamus to test its metabolites in 26 patients with RRMS and 22 healthy age-matched controls. DGM atrophy was evaluated by simple planimetry of third ventricular width on the hypothalamic level (3VW) in T1 weighted MRI pictures. Metabolite ratios of N-acetyl aspartate (NAA), choline (Cho), glutamate and glutamine (Glx), myo-inositol (mIns) and creatine (Cr) were correlated with Multiple Sclerosis Severity Scale (MSSS) and 3VW. Metabolite concentrations were compared between patients and controls using multiple regression models allowing for age, 3VW and metabolites. It revealed that the only relevant predictors of MSSS were 3VW and Glx/NAA. At a significance level of P<0.05, a unit increase of 3VW was associated with a 0.35 increase of MSSS, for a typical value of Glx/NAA; P value 0.0039. A unit increase of Glx/NAA was associated with a 0.93 increase of MSSS, for a typical value of atrophy; P value 0.090. There were significant linear correlations between Glx/Cr and MSSS, Glx/NAA and MSSS, and between mIns/NAA and 3VW. The results suggest that both NAA and Glx are associated with neurodegeneration of hypothalamic DGM and severe disease course. Glx related 1H-MRS parameters seem to be superior to other metabolites in determining disease burden, independently of otherwise powerful 3VW planimetry. Significantly increased mIns/NAA in MS patients compared to controls point to gliosis, which parallels the atrophy of hypothalamic DGM.

A new approach for image segmentation using improved k-means and ROI saliency map

The very crucial part of image processing system is Image Segmentation (IS). In the IS process, the accuracy is dependent on segmented image. In this paper, a new method for image segmentation is shown using improved K-means and region of interest (ROI) saliency map. The proposed system is classified the brain MRI and breast MRI pictures. In this process, detect the portion of tumor from MRI image. By the use of ROI saliency map algorithm, detect only circle section of the picture on the basis of connected component. The experimental dataset contains brain MRI and breast MRI images. The outcome is calculated on the mean (M), standard deviation (STD), entropy (E) and accuracy (ACC). This algorithm is matched with adaptive K means approach. The proposed algorithm gives enhanced outputs as compared to existing approach.

The Combination of Mammography and MRI for Diagnosing Breast Cancer Using Fuzzy NN and SVM

Breast cancer is one of the common cancers among women so that early diagnosing of it can effectively help its treatment in this study, considering combination of Mammography and MRI pictures, we will try to recognize glands in existing pictures which identify all around of gland complete and precisely and separate it completely. In this method using artificial intelligence algorithm such as Affine transformation, Gabor filter, neural network, and support vector machine, image analysis will be carried out. The accuracy of proposed method is 98.14. In this work a special framework is presented which simplifies cancer diagnosis. The algorithm of proposed method is tested on z16 images. High speed and lack of human error are the most important factors in proposed intelligent system.

P119 – 2597: Neuroimaging findings of congenital CMV

Objective This report describes a case in which MRI pictures lead to diagnosis of Congenital CMV. Case 1: 8 months old infant was admitted to the Child Neurology department with muscle hypotonia, poor visual contact and developmental delay. She is 2nd child from 3rd pregnancy, child was born at term, with low birth weight. She had microcephaly, axial hypotonia and slight spasticity of the extremities with high tendon reflexes, and she was underweight. Bilateral sensorineural hearing loss was revealed by audiometry. MRI was done and delayed myelination, bilateral perisylvian polymicrogyria, and diffuse white matter changes were found in it. It was suspected CMV and then performed brain CT. Brain CTshowed periventricular calcifications. Conclusion The presence of the described MRI findings is an indication for CMV investigation. And intracranial calcification in addition to with such changes can be pattern recognition for Congenital CMV.

The radiographic method for evaluation of axial vertebral rotation - presentation of the new method.

The objective of this study is to present a new radiographic method for the assessment of vertebral rotation from an antero-posterior view of conventional X-rays which is sufficiently precise in comparison with radiographic methods presently used in clinical practice (methods of Nash-Moe and Perdriolle). This method is based on the properties of the geometric shape of vertebrae and their shared dimensional proportions. It means that the relation between vertebral body width and height doesn’t change significantly within the entire thoracic and lumbar sections of the spine. In order to verify the method, we have constructed a special device for vertebral fixation. Subsequently, the X-ray pictures of individual human vertebrae with predefined rotation values (ranging from 0 degrees to 45 degrees by steps of 3 degrees) were radio-graphically measured and then compared with their actual axial rotation on the vertebral rotation device. All arithmetic averages correlate very closely with the actual values. The verification of axial vertebral rotation with the assistance of CT and MRI pictures of six scoliotic patients (in supine position) and the evaluation of axial vertebral rotation by both the new radiographic method and with the Perdriolle method proved the satisfactory accuracy of our method. The main advantage of the newly presented radiographic method is the uncomplicated measurement of vertebral rotation from AP projection of conventional X-ray pictures or from its printed copies. The gold standard of the new radiographic method is the evaluation of axial rotation of vertebrae to 30 degrees approximately and the shape of vertebral bodies without severe structural deformities. The new radiographic method seems to be suitable for use in clinical practice.

Advantages of gadolinium based ultrasmall nanoparticles vs molecular gadolinium chelates for radiotherapy guided by MRI for glioma treatment.

AGuIX nanoparticles are formed of a polysiloxane network surrounded by gadolinium chelates. They present several characteristics. They are easy to produce, they present very small hydrodynamic diameters (<5 nm) and they are biodegradable through hydrolysis of siloxane bonds. Such degradation was evaluated in diluted conditions at physiological pH by dynamic light scattering and relaxometry. AGuIX nanoparticles are also known as positive contrast agents and efficient radiosensitizers. The aim of this paper is to compare their efficiency for magnetic resonance imaging and radiosensitization to those of the commercial gadolinium based molecular agent: DOTAREM®. An experiment with healthy animals was conducted and the MRI pictures we obtained show a better contrast with the AguIX compared to the DOTAREM® for the same amount of injected gadolinium in the animal. The better contrast obtained after injection of Aguix than DOTAREM® is due to a higher longitudinal relaxivity and a residential time in the blood circulation that is two times higher. A fast and large increase in the contrast is also observed by MRI after an intravenous injection of the AGuIX in 9 L gliosarcoma bearing rats, and a plateau is reached seven minutes after the injection.We established a radiotherapy protocol consisting of an irradiation by microbeam radiation therapy 20 minutes after the injection of a specific quantity of gadolinium. After microbeam radiation therapy, no notable difference in median survival time was observed in the presence or absence of gadolinium chelates (38 and 44 days respectively). In comparison, the median survival time is increased to 102.5 days with AGuIX particles showing their interest in this nanomedicine protocol. This remarkable radiosensitizing effect could be explained by the persistent tumor uptake of the particles, inducing a significant nanoscale dose deposition under irradiation.

Image De-noising &amp; Detection of Brain Tumour and Cancer Cells by using Clustering Methods

Image process is one among most growing analysis space these days and currently it's greatly integrated with the medical and biotechnology field. Image process will be used to analyze different medical and MRI pictures to induce the abnormality within the image. In medical image processing, medical images are corrupted by different type of noises. It is very important to obtain precise images to facilitate accurate observations for the given application. Removing of noise from medical images is now a very challenging issue in the field of medical image processing. Most well known noise reduction methods, which are usually based on the local statistics of a medical image, are not efficient for medical image noise reduction. This paper presents an efficient and simple method for noise reduction from medical images and experimental results are also compared with the other three image filtering algorithms. The quality of the output images is measured by the statistical quantity measures: peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR) and root mean square error (RMSE) proposes associate economical K-means clump algorithm underneath Morphological Image process (MIP). Medical Image segmentation deals with segmentation of tumour in CT and MR pictures for improved quality in diagnosis. It is an important method and a difficult drawback because of noise presence in input pictures throughout image analysis. It's required for applications involving estimation of the boundary of associate object, classification of tissue abnormalities, form analysis, contour detection. Segmentation determines because the method of dividing associate image into disjoint consistent regions of a medical image. The amount of resources needed to explain massive set of information is simplified and is chosen for tissue segmentation. In our paper, this segmentation is administrated victimization K-means clump algorithm for higher performance. This enhances the growth boundaries more and is extremely fast in comparison to several other clustering algorithms. This paper produces the reliable results that area unit less sensitive to error. Keywords: Magnetic resonance image, Morphological Image processing, Image segmentation, K-means, Morphological operations, Fuzzy, PSNR, RMSE.

A Treatable Neurometabolic Disorder: Glutaric Aciduria Type 1

Glutaric aciduria type 1 (GA-1) is an autosomal recessive disorder of lysine, hydroxylysine, and tryptophan metabolism caused by deficiency of glutaryl-CoA dehydrogenase. It results in the accumulation of 3-hydroxyglutaric and glutaric acid. Affected patients can present with brain atrophy and macrocephaly and with acute dystonia secondary to striatal degeneration in most cases triggered by an intercurrent childhood infection with fever between 6 and 18 months of age. We report two such cases with macrocephaly, typical MRI pictures, and tandem mass spectrometry suggestive of glutaric aciduria type 1.

Correlation between MRI appearances and serum alkaline phosphatase levels in Osteogenic sarcoma.

Magnetic resonance imaging is a pictorial depiction of the patho-anatomy of a bony lesion. As different parts of the tumor in Osteogenic sarcoma concurrently undergo various biological processes i.e. osteoblastic new bone formation, cell death, necrosis, bony destruction and revascularization etc., we hypothesized that the image seen in MRI could be used to assess the tumor behavior at that time. This study was done as a preliminary study with the aim to find whether the MRI pictures can have identifiable patterns and if present whether they can be linked to biological behavior. We could identify 2 distinct patterns in T2 weighted images which correlated well with serum alkaline phosphatase a serum marker and the duration of symptoms and so we are reporting our observations. T2 weighted MRI pictures of 15 cases of Osteogenic sarcoma were studied for identifiable patterns in matrix. These patterns if found were to be linked to biological behavior in the form of serum alkaline phosphatase levels and duration of symptoms. We could identify 2 unique patterns named by us as Group 1 Heterogeneous type (4cases) which had a raised serum alkaline phosphatase level and had a history at presentation of less than 3 months duration. In Group 2 homogenous type (5 cases) the serum alkaline phosphatase levels were low and the cases presented after 6 months. As we could not identify any logical pattern in rest of the cases we labeled them as miscellaneous. MRI patterns can be used as markers of disease activity as there are 2 clear poles correlating well with serum alkaline phosphatase levels (high or low). Intermediate patterns may be the natural biological behavior and waxing and waning of the tumor disease activity.