An Effective Approach for Blood Vessel Tree Extraction from Retinal Images with Adaptive Weighted Contrast Adjustment Approach

Astrocytes, the predominant glial cell type in the brain, were traditionally considered as merely passive supportive cells without any important roles in synaptic information processing. In contrast, the contemporary view was given rise to show that astrocytes play active roles in synaptic neurotransmission and information processing. Hence, recently two terms have been emerged, tripartite synapse, to describe the communication between an astrocyte and two neurons, and the term astrocytic syncytium or astrocytic network to describe the communication among the astrocytes by gap junction. Therefore, we propose mathematical models for tripartite synapseand astrocytic syncytium based on twostate kinetics models, several probabilistic methods and Spiking Neural Network (SNN) to introduce new Artificial Astrocytic Syncytium (AAS) model. The simulation results have shown that proposed model could represent the cellular intrinsic properties of astrocyte based on the spatial and temporal aspects to emulate the astrocytic network functions related to cognitive, learning and memory.

Section: Astrocytic Network Model Based On Probabilisticmentioning

confidence: 99%

Two Suggested Probabilistic and Kinetic Models for Astrocytic Network in Spiking Neural Networks

Abed¹,

Ayyoub²,

Ismail³

et al. 2020

“…The rate of severe sight threatening retinopathy at the successive screening session was low in type -2 Diabetic without retinopathy patients Shivananda, Dr.Girish.A.Attimarad [46] proposed a system for detection of microaneurysms and exudates in the retina.for detecting it used digital image processing techniques and signal processing. S. Shafiulla Basha, Dr. K. Venkata Ramanaiah [47] in this paper author mainly focus on the blood vascular analysis,it is very crucial to diagnose diabetic retinopathy.…”

Section: Related Workmentioning

confidence: 99%

Complications of Sight Threatening Diabetic Retinopathy: A Review

Malik¹

2019

A disease mainly known as diabetes is causing several side effects on the human body. The most affected part of the human body is the human eye retina. This disease of the retina that can result in loss of vision. This phenomenon is known as Diabetic Retinopathy (DR.)Basically Daibetis reduces the capability of the human body to store and regulate the blood sugar , as a result of this, the retina of human eye gets effected. In the early stages DR is showing the typical result of damaging the human eye retina and further it leads to the Sight-threatening diabetic retinopathy (SDTR) i.e. complete loss of vision.Computer assisted diagnosis is one of the tool which helps to diagnose the diabetic patient by using the results of medical investigations. i.e assisting images, reports, etc. for initializing the treatment. In the screening of diabetes sight-threatening DR plays a vital role. These are some major techniques available till date for machine-driven analysis of STDR, to detect hemorrhages and microaneurysms. With limitations for analysis of STDR, the survey mainly focuses on both the qualitative as well as a quantitative comparison of the existing study.While considering the future work , it is a try to encapsulate the convenient algorithms for easy understanding and informative to the experimenters working in this field so that to understand the previous algorithms in a better way and try to develop the most effective one .

“…For analyzing heart sound, Phonocardiography is a popular term among medical researchers and this work is generally employed in the detection of heart sound murmurs originating from valvular diseases. Although, analysis of heart signals such as ECG was classical idea and ample literature is available in this domain with continuously evolving signal and image processing techniques [5][6][7][8][9], the low accuracy of ECG itself is an issue. The detection of coronary artery diseases from heart sounds is quite a popular topic among researchers during the last decade.…”

Section: Introductionmentioning

confidence: 99%

Preliminary Diagnosis of Coronary Artery Disease from Human Heart Sounds: A Signal Processing Prospective

Khan¹

2019

Coronary artery disease (CAD) is becoming the top cause of mortality worldwide. The major concern over CAD is its minimal preliminary symptoms arriving out of blocked coronary arteries. To enable early detection of underlying CAD heart sounds could be considered as potential indicators. Recent past studies give strong evidence for the same. This paper focuses on exploring the possibility of using spectral and Spectro temporal techniques to analyze heart sounds to detect coronary artery disease. Recording of heart sounds is done using electronic stethoscope 3M Littmann 3200. After verifying the fidelity of electronic stethoscope, spectral and Spectro temporal techniques are used to extract the features of heart sounds. The results from spectral analysis of heart sounds were utilized as a commencement point for Spectro-temporal feature analysis. Band wise spectral kurtosis is computed for after taking Stockwell transform for 10-400 Hz range. After experimentation, it was observed that band 3 (169-253 Hz) discriminated CAD and non-CAD heart sounds with linear separability. Minimum distance classifier has provided the classification accuracy of 95.64%. Therefore, preliminary diagnosis of Coronary artery Disease can be carried even in remote areas without costly equipment using this approach and provides major breakthrough for early diagnosis and prevention of mortalities for this dreaded ailment.