/pdf/unit-1-introduction-to-digital-image-processing-51w5c04kml.pdf

Unit 1. Introduction to Digital Image Processing

The article proposes new modified Multiply-Accumulate (MAC) units called truncated MAC units and using residue number system (RNS) with moduli of a special form. The effectiveness of the proposed blocks is verified by hardware implementation of digital filtering. The paper presents a comparative theoretical analysis of the proposed approach using the RNS and the known methods using the conventional positional number system (PNS) and RNS. Moreover, in the paper, a hardware simulation on FPGA of digital filters using arithmetic of RNS is performed, and comparison with the known implementations are realized. It is shown that using proposed approach based on RNS makes it possible to increase the frequency of digital filters by about 4 times, and reduce the hardware costs by 3 times, in comparison with the use of the traditional positional number system. Comparison proposed method and known methods based on RNS shows that using proposed method allows to increase the frequency by about 2–6 times, and reduce the hardware costs by 1.5-5 times, with increasing power consumption by 23%. Obtained results open up the possibility for efficient hardware implementation of digital filters on modern devices (FPGA, ASIC, etc.) for solving practical problems such as noise reduction, amplification and suppression of frequencies, interpolation, decimation, equalization, and many others.

/pdf/high-performance-digital-filtering-on-truncated-multiply-5bda1in8q3.pdf

High-Performance Digital Filtering on Truncated Multiply-Accumulate Units in the Residue Number System

/pdf/an-improved-canny-algorithm-based-on-median-filtering-and-3gbys2squ8.pdf

An Improved Canny Algorithm Based on Median Filtering and Adaptive Threshold in Robot Training System

The objective of this study is to present a method that aids in the diagnosis of diseases  in brain due to tumors from MRI brain image whereby segmenting the brain tumor is done by using a novel algorithm that depends on the colour of the 2D image. Therefore, this work consists of three main stages, the first one is loading image into memory, and then the segmentation algorithm is applied. Finally, in order to obliterate the noise object, the 2D median algorithm is conducted. After applying the method the results which are obtained show better output to determine the tumor, simultaneously the diameter of the tumor can be calculated.

/pdf/segmentation-of-tumor-brain-based-on-the-colour-1fx8qsogd4.pdf

Segmentation Of Tumor Brain Based On The Colour

Brain Tumor Detection from Brain MRI Using Soft IP Core on FPGA

Image segmentation plays an important role in the processing of medical images. Segmentation of medical images is a challenging task due to the poor image contrast and the diffusion of organs or tissue boundaries. Due to the emergence of new trends in the engineering technologies many medical diagnosis methods are automated using the image processing principles. Hence in this paper hardware implementation of segmentation of brain tumors is carried out using Xilinx System Generator (XSG) and FPGA. It results in greater performance, less design time and cost than the existing programmable Digital Signal Processors (DSPs) or than even PCs with coprocessors due to lot of parallelism and pipelining. Use of XSG for medical image processing applications effectively reduces the intricacies existing in architecture design and also provides additional feature of hardware co-simulation.

T. Sri Rama Bharadwaja

Papers

Novel VLSI architecture for real time medical image segmentation