With development in technology, medicine has been greatly benefited and new avenues for research opened up, one such field being the real time medical image processing whose applications have allowed medical practitioners worldwide to better diagnosis abilities. It consists of the implementation of various image processing algorithms like edge detection using mask filters, edge enhancement, interpolation etc resulting in better images suitable for diagnosis. The algorithmic computations in real-time may have high level of time based complexity and hence the use of digital signal processors (DSPs) for the implementation of such algorithms is proposed here. We employed TI 320 DM642 DSP for the purpose of image enhancement and edge detection purposes in our work. The problem required us to develop algorithms that are time and memory efficient and worked in agreement with real time specifications. This application desires that the DSPs be highly computationally efficient while working on low power. We thus discuss further the approach we followed while working on the above said platform and its applications in the field of medical research. 1.Introduction
Real time medical imaging in todays world has become a field which requires ultra high speed processing. In order to satisfy this we have inculcated image processing algorithms on TI 320 DM642 DSP processor where the source of images has been considered from BOSCH camera and Sonline Adara Ultrasound Image system. Medical images enhancement and edge detection is an important work for object recognition of the human organs and it is an essential pre-processing requirement in medical imaging. The effect of the edge detection decides the result of the final processed image. Conventionally, edge is detected according to some early brought forward algorithms like Sobel algorithm, Canny algorithm and Laplacian of Gaussian operator(zero crossing method in our case) , but in theory they belong to the high pass filtering, which are not fit for noise medical image edge detection because noise and edge belong to the scope of high frequency. In practical applications, medical images contain object boundaries and object shadows and noise. Therefore, without enhancement operations there is difficulty in distinguishing the exact edge from noise or undesirable geometric features.
2. Image Enhancement Procedure
Image enhancement is an important requirement in pre processing of the images to employ them further. We employ specific filters which when applied to the original image and convolution is performed result in enhanced images with reduced noise, sharper edges & better contrast .The algorithms developed must be thus highly efficient as time constraints require. Further more efforts are to be made for reducing the number of calculations to be performed by the DSPs and simultaneously consider the fact that we have limited on board memory (256 Kb in this case).
(a) Original Input Image Depicting Human Spine
(b) Enhanced Image After Processing
As seen in the [a] and [b] we can clearly observe the enhancement brought about after the convolution operation.[a] on one hand is not clear and blurred unlike [b] which depicts much better contrast and hence better comprehensive ability. The enhanced image has a more even intensity distribution unlike the input. However, the enhancement can be further more increased but is limited by the DSPs processing power, time and memory constraints. Hence it can be termed as application and hardware specific. 3. Edge Detection Procedure
The primary goal of edge detection is to extract information about the two dimensional projection of the input image for use in higher level processing. However there are many types of physical events that cause intensity changes. Some of these physical events are 1. Object boundary - discontinuity in depth and/or surface color and texture. 2. Surface boundary -...