The recent development of high-resolution satellite sensors with hyperspectral and multitemporal capabilities provides the landscape researcher with unique possibilities to analyze the earth surface. This overall increase in data resolution is a problem for many of the traditional methods in remote sensing, especially for those methods based in per-pixel theory.
The problem domain that we have is about delineating landscape objects in multiple scales. This involves producing multiple scale data sets, delineating and extracting grey-scale objects and labelling those with spectral information. When mowing through scale into domains that are not visually familiar to us we need to have robust and non-parametric methods to produce reliable results. The SDC Morphology Toolbox provides that type of methods. Also the learning environment that SDC Information Systems provides is encouraging and effective for new users of morphological tools. Thus we thank the developers and wish them good luck with forthcoming versions of the SDC Morphology Toolbox.
We here at the Siemens Med Computed Tomography applications predevelopment group work on sophisticated CT applications for the future.
Since the technology in computed tomography is moving from a rather two dimensional approach to true volume scanning, it becomes obvious that the data should be visualized and processed in its true three dimensions as well. Thus sophisticated and automated three-dimensional "post-processing" becomes standart "processing".
One of the most demanding techniques, is the anatomical correct segmentation of organs, vessels, lesions, etc.. Conventional thresholding or region growing techniques often fail to provide convincing results. The key to success is to use anatomical knowledge and the morphological features of the objects in order to yield excellent segmentation results.
Up to now there have been only few toolboxes supporting mathematical morphology and nearly all of them provide only two-dimensional functionalities. The development of advanced and three-dimensional morphological functions was very time consuming and prone to errors.
The SDC morphology toolbox covers the whole field of three-dimensional mathematical morphology and it has been implemented using mex files with algorithms proven to be fast and efficient. Thus it enables us to concentrate more on the development of new segmentation approaches, rather than reinventing the wheel by reprogramming already known morphological functions.
I would like to thank SDC for their great job and encourage them to keep on bringing their product to perfection at the same impressive speed they did so far.
It is increasingly recognised by researchers in image processing and computer vision that simple algorithms deriving from mathematical morphology can be extremely useful in all kind of applications. Now that MatLab is becoming increasingly important as a programming environment for all of us working in image processing, the availablity of the MORPHOLOGY TOOLBOX for MatLab is a welcome, and in fact necessary, addition to existing toolboxes such as the Image Processing toolbox.
During the use of this toolbox in our research group, we have found that the algorithms are very reliable, and that the developers have put a great deal of effort in making their implementations compatible with the underlying mathematical theory of complete lattices.
Thus we thank the MORPHOLOGY TOOLBOX developers for their excellent job and encourage them to keep the good work going ...
SDC Morphology Toolbox for MATLAB is a highly sophisticated mathematical morphology toolbox for a popular prototyping environment (for UNIX and Windows NT/98/95 systems).
All simulations have been done by using MatLab 5.3 and the SDC Morphology Toolbox for MatLab, version 0.9.
From the chapter: J. Goutsias and S. Batman "Morphological Methods for Biomedical Image Analysis." in Handbook of Medical Imaging: Volume 2. Medical Image Processing and Analysis. M. Sonka and J. M. Fitzpatrick (Eds.), pp. 175-272. SPIE Optical Engineering Press, 2000.
Morphological Image Processing is a very powerful tool suited for most applications. We at Computing Devices Canada, Canada's largest defense contractor, use Morphological Image Processing in most of our advanced applications, e.g. automatic detection and tracking of ground targets for armored fighting vehicles, automatic detection of mines, automatic detection of submarines, etc..
Today most Image Processing Libraries include Morphological Image Processing functions. Unfortunately, these are quite often limited to some basic functions. SDC Morphology Toolbox goes all the way down the road and provides the most advanced state of the art Morphological Image Processing functions. I am very impressed with the great choice of functions and your quick response time to the latest trends in Morphological Image Processing.
The use of Morphological Image Processing functions may have been slowed down by its complex theory, compared to other image processing disciplines. But you provide Morphological Image Processing as a complete TOOL for engineers - this will reduce the learning curve and makes Morphological Image Processing easily accessible to everybody.
The integration of SDC Morphology Toolbox into MatLab makes it the ideal tool for fast prototyping. In a recent proposal activity, SDC Morphology Toolbox allowed me to develop a prototype with excellent results in 1 day. SDC Morphology Toolbox has become my preferred tool for fast prototyping.
Thanks for supplying this excellent toolbox.
We have been looking for a morphology toolbox for a while. Since we did not find any package in the market that satisfies our research requirements, we were planning to design such a toolbox from scratch, until we came across SDC Morphology Toolbox.
SDC Morphology Toolbox offers a well-designed and comprehensive set of tools covering elementary to advanced morphologic algorithms. Using this toolbox, in the Matlab environment, we have been able to implement and test research ideas in diagnostic imaging very rapidly and efficiently.
I would like to congratulate SDC scientists and programmers for the job well done.
The SDC Morphology Toolbox has provided me with a cost effective way of incorporating the functionality of morphological image processing functionality in the interactive Matlab environment. It has provided a broad range of standard building blocks with which I can experiment, enabling me to concentrate on my application and any non-standard operations required.
Some of my colleagues, having seen the functionality this toolbox gave me, have also started using this toolbox.
As well as being pleased with the product, I have also been impressed with the rapid response of SDC staff.
We have been solving segmentation problems, in Medical Image, entirely using the SDC Morphology Toolbox. Actually, the toolbox shaped our segmentation task to a robust and fast approach. Enclosed we show the screen of the above mentioned application that was fully developed with the Morphology Toolbox. Partial results of the research being conducted have been disclosed in the following events: