scholarly journals COMPUTING THE EFFICIENCY OF IMAGE SEGMENTATION TECHNIQUES IN FMRI ANALYSIS

2017 ◽  
Vol 5 (3) ◽  
pp. 223-237
Author(s):  
Arthi C ◽  
Savithri
Keyword(s):  
Image Processing ◽  
Functional Magnetic Resonance ◽  
Research Papers ◽  
Exponential Increase ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Fmri Analysis ◽  
Medical Analysis ◽  
The Brain ◽  
Extract Information

Functional magnetic resonance imaging has become a very popular tool in neurological and medical analysis over the years.  According to collated data, in the year 1993, as few as 20 papers were presented on the topic of fmri analysis; However, a decade later, as many as 1800 research papers talk about fmri analysis – an exponential increase. An analysis of the activated regions within the brain can be used to detect the its reactions to various stimuli with greater confidence compared to other methods but the success of accurately identifying brain stimuli however lies in the efficiency of the image processing algorithms applied to extract information from the fMRI scans. This paper analyzes the effectiveness of commonly used image processing algorithms in fMRI studies by statistically analyzing their effectiveness in extracting ROI’s in various images (sample size = 17) and tries to project the efficiency of these systems in fMRI scanning.

scholarly journals Identification of Brain Tumor using MATLAB

2020 ◽  
Vol 6 (6) ◽  
pp. 46-49
Author(s):  
G Aswani ◽  
I N V V A M S N Murthy ◽  
K Durga Devi ◽  
N Veerababu ◽  
M L N Swamy
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Soft Tissues ◽  
Tumor Detection ◽  
Horizontal Edge ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
The Brain

Brain Tumor detection and removal is one medical issue that still remains challenging in the field of biomedicine. MRI is most often used for the detection of tumors, lesions, and other abnormalities in soft tissues, such as the brain. This project is about detecting Brain tumors from MRI images using an interface of GUI in Mat lab. Using the GUI, this program can use various combinations of segmentation, filters, and other image processing algorithms to realize the simplest results. Here We start the process by filtering the image with the help of Prewitt horizontal edge- emphasizing filter. The next step for detecting tumors is "watershed pixels." The most important part of this project is that all the Mat lab programs work with GUI “Matlab guide”

Otoconia perfect/imperfect crystals

1994 ◽  
Vol 52 ◽  
pp. 42-43
Author(s):  
César D. Fermin ◽  
Dale Martin
Keyword(s):  
Image Processing ◽  
Room Temperature ◽  
Phosphotungstic Acid ◽  
Gallus Domesticus ◽  
Crystal Matrix ◽  
Organic Templates ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Diffraction Patterns

Otoconia of higher vertebrates are interesting biological crystals that display the diffraction patterns of perfect crystals (e.g., calcite for birds and mammal) when intact, but fail to produce a regular crystallographic pattern when fixed. Image processing of the fixed crystal matrix, which resembles the organic templates of teeth and bone, failed to clarify a paradox of biomineralization described by Mann. Recently, we suggested that inner ear otoconia crystals contain growth plates that run in different directions, and that the arrangement of the plates may contribute to the turning angles seen at the hexagonal faces of the crystals.Using image processing algorithms described earlier, and Fourier Transform function (2FFT) of BioScan Optimas®, we evaluated the patterns in the packing of the otoconia fibrils of newly hatched chicks (Gallus domesticus) inner ears. Animals were fixed in situ by perfusion of 1% phosphotungstic acid (PTA) at room temperature through the left ventricle, after intraperitoneal Nembutal (35mg/Kg) deep anesthesia. Negatives were made with a Hitachi H-7100 TEM at 50K-400K magnifications. The negatives were then placed on a light box, where images were filtered and transferred to a 35 mm camera as described.

Elastic Graph-based Semi-supervised Embedding with Adaptive Loss Regression

2020 ◽  
Keyword(s):  
Image Processing ◽  
International Symposium ◽  
Fast Track ◽  
Electronic Imaging ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Fast track article for IS&T International Symposium on Electronic Imaging 2020: Image Processing: Algorithms and Systems proceedings.

scholarly journals Fractional-Order Colour Image Processing

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 457
Author(s):  
Manuel Henriques ◽  
Duarte Valério ◽  
Paulo Gordo ◽  
Rui Melicio
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Fractional Derivative ◽  
Fractional Order ◽  
Fractional Derivatives ◽  
Grey Scale ◽  
Colour Image ◽  
Colour Image Processing ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Many image processing algorithms make use of derivatives. In such cases, fractional derivatives allow an extra degree of freedom, which can be used to obtain better results in applications such as edge detection. Published literature concentrates on grey-scale images; in this paper, algorithms of six fractional detectors for colour images are implemented, and their performance is illustrated. The algorithms are: Canny, Sobel, Roberts, Laplacian of Gaussian, CRONE, and fractional derivative.

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