scholarly journals A novel framework for efficient identification of brain cancer region from brain MRI

2019 ◽  
Vol 9 (2) ◽  
pp. 1410 ◽  
Author(s):  
Parvathi Angadi ◽  
M Nagendra ◽  
Hanumanthappa M
Keyword(s):  
Brain Cancer ◽  
Directed Acyclic Graph ◽  
Imaging Techniques ◽  
Brain Mri ◽  
Cost Effective ◽  
Input Image ◽  
Accuracy Level ◽  
Significant Research ◽  
Magnetic Resonance Imaging Mri ◽  
Detection Schemes

Diagnosis of brain cancer using existing imaging techniques, e.g., Magnetic Resonance Imaging (MRI) is shrouded with various degrees of challenges. At present, there are very few significant research models focusing on introducing some novel and unique solutions towards such problems of detection. Moreover, existing techniques are found to have lesser accuracy as compared to other detection schemes. Therefore, the proposed paper presents a framework that introduces a series of simple and computationally cost-effective techniques that have assisted in leveraging the accuracy level to a very higher degree. The proposed framework takes the input image and subjects it to non-conventional segmentation mechanism followed by optimizing the performance using directed acyclic graph, Bayesian Network, and neural network. The study outcome of the proposed system shows the significantly higher degree of accuracy in detection performance as compared to frequently existing approaches.

EEG Based Detection of Alcoholics

2019 ◽  
pp. 141-160
Author(s):  
T. K. Padma Shri ◽  
N. Sriraam
Keyword(s):  
Human Brain ◽  
Imaging Techniques ◽  
Cost Effective ◽  
The Body ◽  
Long Term Effects ◽  
Non Invasive ◽  
Positron Emission ◽  
Signal Processing Algorithms ◽  
Magnetic Resonance Imaging Mri ◽  
Neuro Imaging

The short term and long term effects of alcohol on various organs of the body, especially on the human brain is well established by numerous studies. Invasive methods such as Transcranial Magnetic Stimulation (TMS) and non invasive imaging techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and functional MRI activated electro-encephalogram (EEG) have been used to study the changes in EEG activity due to alcoholism. Even with the advent of neuro imaging techniques, EEG happens to be an important tool for brain study providing a non- invasive and cost effective method to detect the effects of alcohol on the human brain. This paper discusses the harmful effects of alcohol on different organs of the body. The advances in the development of EEG signal processing algorithms over the past decade for alcoholic detection are reviewed and their limitations are reported. Further the use of EEG for mass screening of alcoholics and biometric application is discussed in detail.

EEG Based Detection of Alcoholics

2012 ◽  
Vol 1 (1) ◽  
pp. 59-76 ◽  
Author(s):  
T. K. Padma Shri ◽  
N. Sriraam
Keyword(s):  
Human Brain ◽  
Imaging Techniques ◽  
Cost Effective ◽  
The Body ◽  
Long Term Effects ◽  
Non Invasive ◽  
Positron Emission ◽  
Cost Effective Method ◽  
Magnetic Resonance Imaging Mri ◽  
Neuro Imaging

The short term and long term effects of alcohol on various organs of the body, especially on the human brain is well established by numerous studies. Invasive methods such as Transcranial Magnetic Stimulation (TMS) and non invasive imaging techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and functional MRI activated electro-encephalogram (EEG) have been used to study the changes in EEG activity due to alcoholism. Even with the advent of neuro imaging techniques, EEG happens to be an important tool for brain study providing a non- invasive and cost effective method to detect the effects of alcohol on the human brain. This paper discusses the harmful effects of alcohol on different organs of the body. The advances in the development of EEG signal processing algorithms over the past decade for alcoholic detection are reviewed and their limitations are reported. Further the use of EEG for mass screening of alcoholics and biometric application is discussed in detail.

scholarly journals A Review on a Deep Learning Perspective in Brain Cancer Classification

Cancers ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 111 ◽  
Author(s):  
Gopal S. Tandel ◽  
Mainak Biswas ◽  
Omprakash G. Kakde ◽  
Ashish Tiwari ◽  
Harman S. Suri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Cancer ◽  
Cost Effective ◽  
World Health ◽  
Imaging Modalities ◽  
Computer Assisted ◽  
Learning Paradigm ◽  
Brain Scans ◽  
Magnetic Resonance Imaging Mri ◽  
Health Organization

A World Health Organization (WHO) Feb 2018 report has recently shown that mortality rate due to brain or central nervous system (CNS) cancer is the highest in the Asian continent. It is of critical importance that cancer be detected earlier so that many of these lives can be saved. Cancer grading is an important aspect for targeted therapy. As cancer diagnosis is highly invasive, time consuming and expensive, there is an immediate requirement to develop a non-invasive, cost-effective and efficient tools for brain cancer characterization and grade estimation. Brain scans using magnetic resonance imaging (MRI), computed tomography (CT), as well as other imaging modalities, are fast and safer methods for tumor detection. In this paper, we tried to summarize the pathophysiology of brain cancer, imaging modalities of brain cancer and automatic computer assisted methods for brain cancer characterization in a machine and deep learning paradigm. Another objective of this paper is to find the current issues in existing engineering methods and also project a future paradigm. Further, we have highlighted the relationship between brain cancer and other brain disorders like stroke, Alzheimer’s, Parkinson’s, and Wilson’s disease, leukoriaosis, and other neurological disorders in the context of machine learning and the deep learning paradigm.

scholarly journals Imaging Modalities in Gynecology

2010 ◽  
Vol 4 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Raydeen M Busse
Keyword(s):  
Computer Technology ◽  
Imaging Technique ◽  
Imaging Modality ◽  
Imaging Techniques ◽  
Cost Effective ◽  
Imaging Modalities ◽  
List Type ◽  
Imaging Procedures ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri

Abstract Although ultrasound is the primary imaging modality for most gynecologic diagnoses and conditions, knowledge of other diagnostic imaging procedures is important to gynecologists, emergency room physicians and radiologists who care for women of all ages. Since the early 1960s when ultrasound was introduced for the use in obstetrics and gynecology, other imaging techniques have rapidly come into play due to the tremendous advances in computer technology and in the field of engineering. It behooves us to become familiar and knowledgeable about the differences in these imaging techniques in order to gather the most information in the shortest amount of time to care for patients in the most efficient and cost-effective way. This review is meant for the use of most practicing physicians that are exposed to common as well as uncommon gynecologic conditions; therefore the primary imaging modalities discussed in this paper are limited to ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI). Objectives Understanding of the strengths and limitations of ultrasound, MRI and CT Obtaining knowledge of when to apply the most appropriate imaging technique for a certain clinical situations

scholarly journals De-Noising of Uterus Fibroid Ultrasound Image Using Gaussismooth Convolution Filter (GSCF)

2020 ◽  
Vol 5 (7) ◽  
pp. 1033-1037
Author(s):  
Dr. M. Renuka Devi ◽  
V. Sindhu
Keyword(s):  
Image Processing ◽  
Imaging Techniques ◽  
Ultrasound Image ◽  
Cost Effective ◽  
Initial Step ◽  
Threshold Value ◽  
Input Image ◽  
Local Maxima ◽  
Convolution Filter ◽  
Processing Techniques

This paper discusses the methods to detect the presence of uterus fibroid in woman by implementing various image processing techniques. The input image is an ultrasound image as it is cost effective when compared to other imaging techniques like CT, MRI. The initial step in image processing is to remove noise by applying filters. Application of filters smoothen the image without blurring the image. Gradient of the processed image is calculated and the image is enhanced by sharpening the edges of the image are achieved by calculating the local maxima of the gradient. Then, the edges are decided by calculating the threshold value of the processed image. The proposed Gaussismooth Convolution Filter gives better results when compared with other existing filter with PSNR value of 94%.

scholarly journals Image Segmentation of Brain MRI Based on LTriDP and Superpixels of Improved SLIC

2020 ◽  
Vol 10 (2) ◽  
pp. 116
Author(s):  
Yu Wang ◽  
Qi Qi ◽  
Xuanjing Shen
Keyword(s):  
Brain Mri ◽  
Texture Features ◽  
Directional Pattern ◽  
Input Image ◽  
Segmentation Algorithm ◽  
Brain Atlas ◽  
Superpixel Segmentation ◽  
Simple Linear Iterative Clustering ◽  
Fuzzy Regions ◽  
Magnetic Resonance Imaging Mri

Non-uniform gray distribution and blurred edges often result in bias during the superpixel segmentation of medical images of magnetic resonance imaging (MRI). To this end, we propose a novel superpixel segmentation algorithm by integrating texture features and improved simple linear iterative clustering (SLIC). First, a 3D histogram reconstruction model is used to reconstruct the input image, which is further enhanced by gamma transformation. Next, the local tri-directional pattern descriptor is used to extract texture features of the image; this is followed by an improved SLIC superpixel segmentation. Finally, a novel clustering-center updating rule is proposed, using pixels with gray difference with original clustering centers smaller than a predefined threshold. The experiments on the Whole Brain Atlas (WBA) image database showed that, compared to existing state-of-the-art methods, our superpixel segmentation algorithm generated significantly more uniform superpixels, and demonstrated the performance accuracy of the superpixel segmentation in both fuzzy boundaries and fuzzy regions.

Imaging Modalities in Gynecology

2009 ◽  
Vol 3 (2) ◽  
pp. 74-85
Author(s):  
Raydeen M Busse
Keyword(s):  
Computer Technology ◽  
Imaging Technique ◽  
Imaging Modality ◽  
Imaging Techniques ◽  
Cost Effective ◽  
Imaging Modalities ◽  
List Type ◽  
Imaging Procedures ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri

Abstract Although ultrasound is the primary imaging modality for most gynecologic diagnoses and conditions, knowledge of other diagnostic imaging procedures is important to gynecologists, emergency room physicians and radiologists who care for women of all ages. Since the early 1960s when ultrasound was introduced for the use in obstetrics and gynecology, other imaging techniques have rapidly come into play due to the tremendous advances in computer technology and in the field of engineering. It behooves us to become familiar and knowledgeable about the differences in these imaging techniques in order to gather the most information in the shortest amount of time to care for patients in the most efficient and cost-effective way. This review is meant for the use of most practicing physicians that are exposed to common as well as uncommon gynecologic conditions; therefore the primary imaging modalities discussed in this paper are limited to ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI). Objectives Understanding of the strengths and limitations of ultrasound, MRI and CT Obtaining knowledge of when to apply the most appropriate imaging technique for a certain clinical situations

A Novel Secure and Robust encryption scheme for medical images

2020 ◽  
Vol 16 ◽  
Author(s):  
Siyamol Chirakkarottu ◽  
Sheena Mathew
Keyword(s):  
Medical Images ◽  
Technological Development ◽  
Imaging Techniques ◽  
Chaotic Map ◽  
Two Phase ◽  
Positron Emission ◽  
Medical Diagnostic ◽  
Wireless Medium ◽  
Health Care Applications ◽  
Magnetic Resonance Imaging Mri

Background: Medical imaging encloses different imaging techniques and processes to image the human body for medical diagnostic and treatment purposes. Hence it plays an important role to improve public health. The technological development in biomedical imaging specifically in X-ray, Computed Tomography (CT), nuclear ultrasound including Positron Emission Tomography (PET), optical and Magnetic Resonance Imaging (MRI) can provide valuable information unique to a person. Objective: In health care applications, the images are needed to be exchanged mostly over wireless medium. The diagnostic images with confidential information of a patient need to be protected from unauthorized access during transmission. In this paper, a novel encryption method is proposed to improve the security and integrity of medical images. Methods: Chaotic map along with DNA cryptography is used for encryption. The proposed method describes a two phase encryption of medical images. Results: Performance of the proposed method is also tested by various analysis metrics. Robustness of the method against different noises and attacks is analyzed. Conclusion: The results show that the method is efficient and well suitable to medical images.

State-of-the-art MR Imaging of Fat-containing Focal Lesions of the Liver

2019 ◽  
Vol 15 (5) ◽  
pp. 435-442
Author(s):  
Joanna Podgorska ◽  
Agnieszka Anysz-Grodzicka ◽  
Andrzej Cieszanowski
Keyword(s):  
Differential Diagnosis ◽  
Imaging Techniques ◽  
Hepatobiliary Phase ◽  
Focal Lesions ◽  
Dysplastic Nodules ◽  
Nodular Hyperplasia ◽  
Specific Finding ◽  
Magnetic Resonance Imaging Mri ◽  
Adrenal Rest ◽  
Mri Characteristics

Background: Fat can be identified in numerous liver lesions, and usually is not a specific finding. Distinguishing between different kinds of fatty deposits is an important part of differential diagnosis. Magnetic Resonance Imaging (MRI) is superior to other imaging techniques because it allows distinguishing intracellular from macroscopic fat. Discussion: Intracellular lipid may be found in focal hepatic steatosis, hepatic adenoma, hepatocellular carcinoma and, less commonly, in focal nodular hyperplasia as well as regenerative and dysplastic nodules. Macroscopic fat is seen in angiomyolipoma, lipoma, metastases from fatcontaining neoplasms, primary or metastatic liposarcoma, hydatid cyst, pseudolipoma of the Glisson capsule, pericaval fat collection, lipopeliosis, hepatic teratoma, focal hepatic extramedullary haematopoiesis and adrenal rest tumour. Conclusion: Liver nodules should be characterised with regard to underlying liver condition, MRI characteristics and contrast enhancement pattern, including hepatobiliary phase. In many cases, identification of fatty content may help narrowing the differential diagnosis.

scholarly journals ctDNA-Based Liquid Biopsy of Cerebrospinal Fluid in Brain Cancer

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1989
Author(s):  
Laura Escudero ◽  
Francisco Martínez-Ricarte ◽  
Joan Seoane
Keyword(s):  
Cerebrospinal Fluid ◽  
Brain Cancer ◽  
Liquid Biopsy ◽  
Therapeutic Strategy ◽  
Residual Disease ◽  
Imaging Techniques ◽  
Prognostic Information ◽  
Tumour Heterogeneity ◽  
Diagnosis And Prognosis ◽  
Cns Malignancies

The correct characterisation of central nervous system (CNS) malignancies is crucial for accurate diagnosis and prognosis and also the identification of actionable genomic alterations that can guide the therapeutic strategy. Surgical biopsies are performed to characterise the tumour; however, these procedures are invasive and are not always feasible for all patients. Moreover, they only provide a static snapshot and can miss tumour heterogeneity. Currently, monitoring of CNS cancer is performed by conventional imaging techniques and, in some cases, cytology analysis of the cerebrospinal fluid (CSF); however, these techniques have limited sensitivity. To overcome these limitations, a liquid biopsy of the CSF can be used to obtain information about the tumour in a less invasive manner. The CSF is a source of cell-free circulating tumour DNA (ctDNA), and the analysis of this biomarker can characterise and monitor brain cancer. Recent studies have shown that ctDNA is more abundant in the CSF than plasma for CNS malignancies and that it can be sequenced to reveal tumour heterogeneity and provide diagnostic and prognostic information. Furthermore, analysis of longitudinal samples can aid patient monitoring by detecting residual disease or even tracking tumour evolution at relapse and, therefore, tailoring the therapeutic strategy. In this review, we provide an overview of the potential clinical applications of the analysis of CSF ctDNA and the challenges that need to be overcome in order to translate research findings into a tool for clinical practice.

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