This paper shows the possibility to improve the accuracy of medical diagnosis on contaminated radiographic image with salt and pepper noise using FPGA-based filters. Investigation is carried out using filters described widely in the literature, such as Blur, Smooth and Gaussian linear filters and nonlinear median filter implemented on FPGA in an attempt to eliminate the noise. Implementations are checked on a development board with Spartan 6slx100fgg484-3 FPGA and a low-power optimized version Spartan 6slx100lfgg484-1l. The results are verified through the filtered images, resources consumption and the lowest response time. The implementation methodology uses model-based engineering involving MATLAB/Simulink and System Generator software using the XSGlmgLib library to perform co-simulation. The results show that the FPGA implementation of the median filter has a good tradeoff between resource consumption and noise removal efficiency.

Performance Analysis of Algorithms over FPGA for Removing Salt and Pepper Noise

An FPGA Implementation of Reconfigurable Real-Time Vision Architecture

Image processing is used in every sphere of life such as agriculture, remote sensing, wireless, medical etc. Biomedical imaging plays a vital role in the detection of diseases. Without image processing, it is not possible to detect diseases such as cancer, tumors etc. Medical equipment such as ultrasound, MRI, CT scan machine is totally dependent on image processing algorithms. Radiologists utilize these image processing algorithms to detect diseases and abnormalities. Matlab is a proprietary tool which is used by image architects in order to design these algorithms. Image processing algorithms designed using Matlab provides efficiency, accuracy, flexibility and timing constraints. The present paper addresses various image processing algorithms designed using Matlab. The performance of these algorithms is also analyzed visually as well as statistically in order to check the quality of images.

/pdf/performance-analysis-of-image-processing-algorithms-using-3cfamb87il.pdf

Performance Analysis of Image Processing Algorithms using Matlab for Biomedical Applications

Coastlines have hosted numerous civilizations since the earliest times of mankind due to the advantages they offer such as natural resources, transportation, arable areas, seafood, trade, and biodiversity. Coastal regions should be monitored vigilantly by planners and control mechanisms, and any changes in these regions should be detected with its human or natural origin, and future plans and possible interventions should be formed in these aspects to maintain ecological balance, sustainable development, and planned

http://www.pjoes.com/pdf-64160-23893?filename=Automatic Coastline.pdf

Automatic Coastline Detection Using ImageEnhancement and Segmentation Algorithms

Lately, medical imaging has evolved as it represents an essential support for the patients' diagnosis and monitoring. Early recognition of diseases such as “Glaucoma” is fundamental to the assumption. Indeed, the earlier the treatment is instituted, the better the chances of success are. Since the shape of the optic nerve's excavation is the most important feature in its identification, correct detection techniques are necessary to highlight their forms. This paper presents a comparative study of different edge detection's methods applied on a set of ophthalmologic images of the optic nerve.

Study of contour detection methods as applied on optic nerve's images for glaucoma diagnosis

This paper compares various methodologies for the design of Sobel Edge Detection Algorithm on Field Programmable Gate Arrays (FPGAs). We show some characteristics to design a computer vision algorithm to suitable hardware platforms. We evaluate hardware resources and power consumption of Sobel Edge Detection on two studies: Xilinx system generator (XSG) and Vivado_HLS tools which both are very useful tools for developing computer vision algorithms. The comparison the hardware resources and power consumption among FPGA platforms (Zynq-7000 AP SoC, Spartan 3A DSP) are analyzed. The hardware resources by using Vivado_HLS on both platforms are used less 9 times with BRAM_18K, 7 times with DSP48E, 2 times with FFs, and approximately with LUTs comparing with XSG. In addition, the power consumption on Zynq-7000 AP SoC spends more 30% by using Vivado_HLS than by using XSG tool and for Spartan 3A DSP consumes a half of power comparing with by using XSG tool. In the study by using Vivado_HLS shows that power consumption depends on frequency.

/pdf/performance-and-evaluation-sobel-edge-detection-on-various-h5ymeogqzi.pdf

Nguyen.T.K. Hong

Papers

Performance and Evaluation Sobel Edge Detection on Various Methodologies