My new textbook and job

So many things have happened in the last few months and I have not had time to add new entries to my blog. The first is the textbook that I co-authored with Sridevi Pudipeddi which was released at the end of February.  It was marathon run to complete and get the book ready for publishing.  I also moved to a new job in California in April.


During my work as image processing consultant at the Minnesota Supercomputing Institute, I have worked with students in various disciplines of science.  In all these cases, images were acquired using x-ray, CT, MRI, Electron microscope and Optical microscope.  It is important that the students have knowledge of both the physical methods of obtaining images and the analytical processing methods to understand the science behind the images. Thus, a course in image acquisition and processing has broad appeal across the STEM disciplines and is useful for transforming undergraduate and graduate curriculum to better prepare students for their future.

There are books that discusses image acquisition alone and there are books on image processing alone.  The image processing algorithms depend on the image acquisition method. We wrote a book that discusses both, so that students can learn from one source. You can check out sample chapter of the book at reedwith.us. You can buy the book at Amazon by clicking on the image below.

I also changed job.  I started working as Senior Engineer at Elekta in Sunnyvale, CA. I will be focussing mostly on x-ray and CT during my tenure at Elekta.

Comparing Matlab and Python for large image processing problems

Matlab is a popular scripting language for numerical computing.  It is popular and powerful due to its various toolbox.  Since Matlab is developed commercially, dedicated programmers are working in adding new features and enhancing the existing ones all the time.  Large image data sets created using latest imaging modalities typically take a long time to process.  The typical approach is to run the processing in parallel on many cores either on a desktop, a cluster or a supercomputer.  Commercial software like Matlab require dedicated license such as Distributed Computing Server for running such parallel jobs, which has an added cost.

Graphical Processing Unit (GPU) programming is becoming popular and easier than ever.  The license cost for GPU programming in Matlab is typically lower than that for parallel programming.  GPU programming is useful, if there is large amount of computation and fewer data transfer.  This limit exists because of smaller bandwidth between the CPU and GPU, the typical path that data has to take for processing in the GPU.  Large data set image processing typically involves large amount of I/O and data transfer between CPU and GPU and hence may not provide enough scalability.

Advantage Python

Python is a free and open source scripting language.  It can be scaled to large number of processors.  It has been shown that there is not a significant difference in computational time for a program written in Matlab vs the one written in python using numpy.   The author found that numpy run time is in the same order of magnitude as Fortan and C programs with optimization enabled.  Although, the processing time are for slightly older versions of the various languages, my experience has shown that the range of processing time remains similar.  The processing time will also vary depending on the nature of the algorithm and the expertise of the programmer.

Python has parallel processing modules like mpi4py, that allow scaling the application to large number of cores. In a supercomputing environment with large number of cores, python can be run on many of the cores.  Matlab on the other hand can only scale to the extent of the number of license. 

Python also has GPU programming capability through pycuda, in case if GPU programming suits the application.

Python is a more general purpose language compared to Matlab.  Hence integrating various databases, servers, file handling, string handling in to the program is easy.

Disadvantage Python

Since Python is an open-source package, it does not have a wide variety of canned functions and toolboxes as Matlab.  Hence, the user has to work on developing some of them.  I hope that over time this issue will be resolved.

Under-graduate education in Image processing

SURVEY URL:  http://goo.gl/ORDDz

Image acquisition and processing have become a standard method for qualifying and quantifying experimental measurements in various Science Technology Engineering and Mathematics (STEM) disciplines.  Discoveries have been made possible in medical sciences by advances in diagnostic imaging such as x-ray based computed tomography (CT) and magnetic resonance imaging (MRI).  Biological and cellular functions have been revealed with new imaging techniques in light based microscopy.  Advancements in material sciences have been aided by electron microscopy analysis of nanoparticles.    All these examples and many more require both knowledge of the physical methods to obtain images and the analytical processing methods to understand the science behind the images. 

Imaging technology continues to advance with new modalities and methods available to students and researchers in STEM disciplines.  Thus, a course in image acquisition and processing would have broad appeal across the STEM disciplines and be useful for transforming undergraduate and graduate curriculum to better prepare students for their future.

Image analysis is an extraordinarily practical technique that need not be limited to highly analytical individuals with a math and engineering background.  Since researchers in biology, medicine, and chemistry along with students and scientists from mathematics, physics and various engineering fields use these techniques regularly; there is a need for a course that provides a gradual introduction to both acquisition and processing. 

Such a course will prepare students in the common image acquisition techniques like CT, MRI, light  microscope and electron microscope.   It will also introduce the practical aspects of image acquisition such as noise, resolution etc.  The students will also be programming image processing using Python as a part of the curriculum.  They will be introduced to python modules such as numpy and scipy.   They will learn the various image processing operations like segmentation, morphological operations, measurements and visualization.

We wanted our understanding of this need with the data collected from surveying people interested in image processing or people who wish that they had such a course during their senior year in under-graduate or in graduate school.   We created a survey to obtain your feedback.    It will take only a minute of your time.   We request that you fill as much information as you can.  Please forward this URL or this blogpost to your friends as well.