A Study on Glaucoma Disease Detection with Image Processing Methods

2020 ◽  
Vol 37 ◽  
pp. 25-35
Author(s):  
Shashilata Rawat ◽  
Uma Shankar Kurmi
Keyword(s):  
Image Processing ◽  
Optic Nerve ◽  
Imaging Techniques ◽  
Advanced Imaging ◽  
Detection Techniques ◽  
Glaucoma Detection ◽  
Mode Decomposition ◽  
Processing Techniques ◽  
The Brain ◽  
Aided Diagnosis

The glaucoma is a developing slow eye that effects optic nerve damage in its most common form. Once the optic nerve has been impaired, visual data is not passed to the brain and permanently visual impairment is caused. Glaucoma computer-aided diagnosis (CAD) is a rising area in which medical imaging is analyzed. The CAD is a more precise approach for glaucoma detection, inspired by recent advanced imaging techniques and high-velocity computers. Laser ophthalmoscope scanning, tomography with optical coherence, and retina tomography of Heidelberg have widely used imaging techniques for detecting glaucoma. In this paper, we provide a study of glaucoma disease with its types and detection techniques. Moreover, this paper tells about image processing techniques to detect glaucoma. Variational mode decomposition has also discussed here.

A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

2020 ◽  
pp. 967-973
Author(s):  
V. Deepika ◽  
T. Rajasenbagam
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Soft Tissues ◽  
Tumor Detection ◽  
Tumor Classification ◽  
Normal Brain ◽  
Image Processing Techniques ◽  
Normal Brain Function ◽  
Processing Techniques ◽  
The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

A Review of Various Machine Learning Techniques for Brain Tumor Detection from MRI Images

2020 ◽  
Vol 16 (8) ◽  
pp. 937-945
Author(s):  
Aaishwarya Sanjay Bajaj ◽  
Usha Chouhan
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Ct Scan ◽  
Imaging Techniques ◽  
Critical Evaluation ◽  
Tumor Detection ◽  
Machine Learning Techniques ◽  
X Rays ◽  
Detection Techniques ◽  
The Brain

Background: This paper endeavors to identify an expedient approach for the detection of the brain tumor in MRI images. The detection of tumor is based on i) review of the machine learning approach for the identification of brain tumor and ii) review of a suitable approach for brain tumor detection. Discussion: This review focuses on different imaging techniques such as X-rays, PET, CT- Scan, and MRI. This survey identifies a different approach with better accuracy for tumor detection. This further includes the image processing method. In most applications, machine learning shows better performance than manual segmentation of the brain tumors from MRI images as it is a difficult and time-consuming task. For fast and better computational results, radiology used a different approach with MRI, CT-scan, X-ray, and PET. Furthermore, summarizing the literature, this paper also provides a critical evaluation of the surveyed literature which reveals new facets of research. Conclusion: The problem faced by the researchers during brain tumor detection techniques and machine learning applications for clinical settings have also been discussed.

scholarly journals A Review of Neurotransmitters Sensing Methods for Neuro-Engineering Research

2019 ◽  
Vol 9 (21) ◽  
pp. 4719 ◽  
Author(s):  
Shimwe Dominique Niyonambaza ◽  
Praveen Kumar ◽  
Paul Xing ◽  
Jessy Mathault ◽  
Paul De Koninck ◽  
...  
Keyword(s):  
Imaging Techniques ◽  
Ongoing Research ◽  
Engineering Research ◽  
Detection Techniques ◽  
In Vivo Detection ◽  
Low Concentrations ◽  
The Subject ◽  
The Brain

Neurotransmitters as electrochemical signaling molecules are essential for proper brain function and their dysfunction is involved in several mental disorders. Therefore, the accurate detection and monitoring of these substances are crucial in brain studies. Neurotransmitters are present in the nervous system at very low concentrations, and they mixed with many other biochemical molecules and minerals, thus making their selective detection and measurement difficult. Although numerous techniques to do so have been proposed in the literature, neurotransmitter monitoring in the brain is still a challenge and the subject of ongoing research. This article reviews the current advances and trends in neurotransmitters detection techniques, including in vivo sampling and imaging techniques, electrochemical and nano-object sensing techniques for in vitro and in vivo detection, as well as spectrometric, analytical and derivatization-based methods mainly used for in vitro research. The document analyzes the strengths and weaknesses of each method, with the aim to offer selection guidelines for neuro-engineering research.

scholarly journals A Research: Hyperspectral Image Processing Techniques

2019 ◽  
Vol 8 (9S2) ◽  
pp. 577-581
Keyword(s):  
Image Processing ◽  
Dimension Reduction ◽  
Target Detection ◽  
Hyperspectral Image ◽  
Target Identification ◽  
Target Classification ◽  
Detection Techniques ◽  
Hyperspectral Image Processing ◽  
Complicated Process ◽  
Processing Techniques

Hyperspectral image contains more information which are gathered from numerous narrow wavebands from one or more regions, and large amount of data are huddled. An basic problems in hyperspectral image processing are dimension reduction, target detection, target identification, and target classification. In this document, we reviewed the latest activities of target classification, most frequently used techniques for dimension reduction, target detection. Hyperspectral image processing is a complicated process which rely on mixed agents. Here we also recognized and reviewed problems faced by some methods and to overcome the problems, current techniques are discussed and highlighted good methods. To improving correctness, genuine classification techniques and Detection Techniques analysis are recommended

scholarly journals POSSIBILITIES OF MODERN IMAGING TECHNIQUES IN DIAGNOSTICS OF OPTIC NERVE DRUSEN (LITERATURE REVIEW)

2016 ◽  
Vol 1 (6) ◽  
pp. 206-209
Author(s):  
Кабанова ◽  
Evgeniya Kabanova ◽  
Иойлева ◽  
Elena Ioyleva ◽  
Котова ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Computed Tomography ◽  
Optic Nerve ◽  
Visual Analysis ◽  
Imaging Techniques ◽  
Diagnostic Techniques ◽  
Resonance Imaging ◽  
Diagnostic Standards ◽  
The Brain ◽  
Optic Nerve Drusen

With the introduction and development of new diagnostic techniques, the relevance of drusen of the optic nerve di-agnostics is increasing. Existing imaging techniques alone or in their various combinations cannot allow to confirm optic nerve drusen in all clinical cases. The diagnosis of optic nerve drusen causes some difficulties because of absence of clear diagnostic standards and classification. Since the advent of new ophthalmological methods of structural and topographic visual analysis evaluation, such as spectral-domain optical coherence tomography, Heidelberg retina tomography, videooculography, B-scan ultrasonography of the orbits and optic nerve, fluorescent angiography of the retina, computed tomography and magnetic resonance imaging of the brain and orbits, the diagnostics of optic nerve drusen becomes more informative. In this article we review the main current imaging techniques in the diagnostics of the optic nerve drusen.

scholarly journals Breast Cancer Detection using Image Processing Techniques

2017 ◽  
Vol 10 (2) ◽  
pp. 391-399 ◽  
Author(s):  
Prannoy Giri ◽  
K. Saravanakumar
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Back Propagation ◽  
Texture Features ◽  
Screening Mammography ◽  
Breast Cancers ◽  
Back Propagation Algorithm ◽  
Digital Database ◽  
Processing Techniques ◽  
Aided Diagnosis

Breast Cancer is one of the significant reasons for death among ladies. Many research has been done on the diagnosis and detection of breast cancer using various image processing and classification techniques. Nonetheless, the disease remains as one of the deadliest disease. Having conceive one out of six women in her lifetime. Since the cause of breast cancer stays obscure, prevention becomes impossible. Thus, early detection of tumour in breast is the only way to cure breast cancer. Using CAD (Computer Aided Diagnosis) on mammographic image is the most efficient and easiest way to diagnosis for breast cancer. Accurate discovery can effectively reduce the mortality rate brought about by using mamma cancer. Masses and microcalcifications clusters are an important early symptoms of possible breast cancers. They can help predict breast cancer at it’s infant state. The image for this work is being used from the DDSM Database (Digital Database for Screening Mammography) which contains approximately 3000 cases and is being used worldwide for cancer research. This paper quantitatively depicts the analysis methods used for texture features for detection of cancer. These texture featuresare extracted from the ROI of the mammogram to characterize the microcalcifications into harmless, ordinary or threatening. These features are further decreased using Principle Component Analysis(PCA) for better identification of Masses. These features are further compared and passed through Back Propagation algorithm (Neural Network) for better understanding of the cancer pattern in the mammography image.

scholarly journals CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sara P. Oliveira ◽  
Pedro C. Neto ◽  
João Fraga ◽  
Diana Montezuma ◽  
Ana Monteiro ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Imaging Techniques ◽  
Digital Pathology ◽  
Incidence Rates ◽  
Cancer Type ◽  
Tissue Samples ◽  
Digital Era ◽  
Processing Techniques ◽  
Aided Diagnosis ◽  
Whole Slide Images

AbstractMost oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples. In recent years, the pathology field has evolved to a digital era where tissue samples are digitised and evaluated on screen. As a result, digital pathology opened up many research opportunities, allowing the development of more advanced image processing techniques, as well as artificial intelligence (AI) methodologies. Nevertheless, despite colorectal cancer (CRC) being the second deadliest cancer type worldwide, with increasing incidence rates, the application of AI for CRC diagnosis, particularly on whole-slide images (WSI), is still a young field. In this review, we analyse some relevant works published on this particular task and highlight the limitations that hinder the application of these works in clinical practice. We also empirically investigate the feasibility of using weakly annotated datasets to support the development of computer-aided diagnosis systems for CRC from WSI. Our study underscores the need for large datasets in this field and the use of an appropriate learning methodology to gain the most benefit from partially annotated datasets. The CRC WSI dataset used in this study, containing 1,133 colorectal biopsy and polypectomy samples, is available upon reasonable request.

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