Siddharth Samsi

Automatic detection of follicular regions in H&E images using iterative shape index

Follicular Lymphoma (FL) accounts for 20-25% of non-Hodgkin lymphomas in the United States. The first step in grading FL is identifying follicles. Our paper discusses a novel technique to segment follicular regions in H&E stained images. The method is based on three successive steps: (1) region-based segmentation, (2) iterative shape index (concavity index) calculation, (3) and recursive watershed. A novel aspect of this method is the use of iterative Concavity Index (CI) to control the follicular splitting process in recursive watershed. CI takes into consideration the convex hull of the object and the closest area surrounding it. The mean Zijbendos similarity index (ZSI) final segmentation score on fifteen cases was 78.33%, with a standard deviation of 2.83.

Octave and Python: High-Level Scripting Languages Productivity and Performance Evaluation

Octave and Python are open source alternatives to MATLAB, which is widely used by the High Performance Computing Modernization Program (HPCMP) community. These languages are two well known examples of high-level scripting languages that promise to increase productivity without compromising performance on HPC systems. In this paper, we report our work and experience with these two non-traditional programming languages at the HPCMP Centers. We used a representative sample of SIP codes for the study, with special emphasis given to the understanding of issues such as portability, degree of complexity, productivity and suitability of Octave and Python to address signal/image processing (SIP) problems on the HPCMP HPC platforms. We implemented a relatively simple two-dimensional (2D) FFT and a more complex image enhancement algorithm in Octave and Python and benchmarked these SIP codes on several HPCMP platforms, paying special attention to usability, productivity and performance aspects. Moreover, we performed a thorough benchmark containing important low level SIP core functions and algorithms and compared the outcome with the corresponding results for MATLAB. We found that the capabilities of these languages are comparable to MATLAB and they are powerful enough to efficiently implement complex SIP algorithms. Productivity and performance results for each language vary depending on the specific task and the availability of high level functions in each system to address such tasks. Therefore, the choice of the best language to use in a particular instance will strongly depend upon the specifics of the SIP application that needs to be addressed. We concluded that Octave and Python look like promising tools that may provide an alternative to MATLAB without compromising performance and productivity. Their syntax and functionality are similar enough to MATLAB to present a very shallow learning curve for experienced MATLAB users

Detection of Follicles From IHC-Stained Slides of Follicular Lymphoma Using Iterative Watershed

Follicular lymphoma (FL) is one of the most common types of nonHodgkin lymphoma in the U.S. Diagnosis of FL is based on tissue biopsy that shows characteristic morphologic and immunohistochemical (IHC) findings. Our groups work focuses on the development of computer-aided image-analysis techniques to improve the FL grading. Since centroblast enumeration needs to be performed in malignant follicles, the development of an automated system to accurately identify follicles on digital images of lymphoid tissue is an important step. In this letter, we describe an automated system to identify follicles in IHC-stained tissue sections. A unique feature of the system described here is the use of texture and color information to mimic the process that a human expert might use to identify follicle regions. Comparison of system-generated results with expert-generated ground truth has shown promising results with a mean similarity score of 87.11%.

An Efficient Computational Framework for the Analysis of Whole Slide Images: Application to Follicular Lymphoma Immunohistochemistry

Follicular Lymphoma (FL) is one of the most common non-Hodgkin Lymphoma in the United States. Diagnosis and grading of FL is based on the review of histopathological tissue sections under a microscope and is influenced by human factors such as fatigue and reader bias. Computer-aided image analysis tools can help improve the accuracy of diagnosis and grading and act as another tool at the pathologists disposal. Our group has been developing algorithms for identifying follicles in immunohistochemical images. These algorithms have been tested and validated on small images extracted from whole slide images. However, the use of these algorithms for analyzing the entire whole slide image requires significant changes to the processing methodology since the images are relatively large (on the order of 100k × 100k pixels). In this paper we discuss the challenges involved in analyzing whole slide images and propose potential computational methodologies for addressing these challenges. We discuss the use of parallel computing tools on commodity clusters and compare performance of the serial and parallel implementations of our approach.

Static graph challenge: Subgraph isomorphism

The rise of graph analytic systems has created a need for ways to measure and compare the capabilities of these systems. Graph analytics present unique scalability difficulties. The machine learning, high performance computing, and visual analytics communities have wrestled with these difficulties for decades and developed methodologies for creating challenges to move these communities forward. The proposed Subgraph Isomorphism Graph Challenge draws upon prior challenges from machine learning, high performance computing, and visual analytics to create a graph challenge that is reflective of many real-world graph analytics processing systems. The Subgraph Isomorphism Graph Challenge is a holistic specification with multiple integrated kernels that can be run together or independently. Each kernel is well defined mathematically and can be implemented in any programming environment. Subgraph isomorphism is amenable to both vertex-centric implementations and array-based implementations (e.g., using the Graph-BLAS.org standard). The computations are simple enough that performance predictions can be made based on simple computing hardware models. The surrounding kernels provide the context for each kernel that allows rigorous definition of both the input and the output for each kernel. Furthermore, since the proposed graph challenge is scalable in both problem size and hardware, it can be used to measure and quantitatively compare a wide range of present day and future systems. Serial implementations in C++, Python, Python with Pandas, Matlab, Octave, and Julia have been implemented and their single threaded performance have been measured. Specifications, data, and software are publicly available at GraphChallenge.org.

Imaging mass spectrometry analysis for follicular lymphoma grading

Follicular lymphoma (FL) is the second most common non-Hodgkins lymphoma in the United States. While the current diagnosis depends heavily on the review of H&E-stained tissues, additional sources of information such as IHC are occasionally needed. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) can be used to generate protein profiles from localized tissue regions, thus making it possible to relate changes in tissue histology to the changes in the protein signature of the tissue. It may be possible to determine potential biomarkers that can indicate disease state and prognosis based on the protein profile. This research aims to combine two different but related types of data in order to develop a unique diagnosis methodology that can potentially improve the accuracy of diagnosis. Preliminary analysis has shown promising results for distinguishing intrafollicle regions from the mantle and follicle zones in normal tissue.

Survey of Parallel MATLAB Techniques and Applications to Signal and Image Processing

We present a survey of modern parallel MATLAB techniques. We concentrate on the most promising and well supported techniques with an emphasis in SIP applications. Some of these methods require writing explicit code to perform inter-processor communication while others hide the complexities of communication and computation by using higher level programming interfaces. We cover each approach with special emphasis given to performance and productivity issues.

A computational science IDE for HPC systems: design and applications

Software engineering studies have shown that programmer productivity is improved through the use of computational science integrated development environments (or CSIDE, pronounced “sea side”) such as MATLAB. Scientists often desire to use high-performance computing (HPC) systems to run their existing CSIDE scripts with large data sets. ParaM is a CSIDE distribution that provides parallel execution of MATLAB scripts on HPC systems at large shared computer centers. ParaM runs on a range of processor architectures (e.g., x86, x64, Itanium, PowerPC) and its MPI binding, known as bcMPI, supports a number of interconnect architectures (e.g., Myrinet and InfiniBand). On a cluster at Ohio Supercomputer Center, bcMPI with blocking communication has achieved 60% of the bandwidth of an equivalent C/MPI benchmark. In this paper, we describe goals and status for the ParaM project and the development of applications in signal and image processing that use ParaM.

Interfacing PC-based MATLAB Directly to HPC Resources

Many DoD HPC users, particularly in the SIP area, run codes developed with MATLAB and related applications (MatlabMPI, StarP, pMatlab, etc.). There is a desire to run codes from a desktop instance of MATLAB and connect to and interact with codes running on HPC resources. The PET SIP team has developed and demonstrated technology that makes this possible. The SSH toolbox for MATLAB enables users to connect to and use HPC resources using SSH without leaving the MATLAB environment. The toolbox uses a freely available implementation of SSH, a modified version of which is also used by the DoD HPCMP. The SSH toolbox consists of a Windows DLL written in C, which is used by MATLAB to communicate with the SSH client. The toolbox provides simple MATLAB commands for users to connect to remote resources, run code, retrieve results and end the SSH session. The complexity of the DLL interface and most of the security needs are hidden from the user, making this a very easy to use and powerful toolbox. Since the main component of the toolbox is written is C and packaged as a DLL, the toolbox can also be extended to work with other programming languages such as Java, Python and Octave. MATLAB-style documentation for the toolbox also makes it easy to obtain help on various aspects of the toolbox and a GUI-based installer makes distribution easier. This technology provides a revolutionary way of providing support to the DoD. Software developers are now able to provide all the hooks to a complicated HPC environment, thus removing the burden of end users

Parallel MATLAB Techniques

In this chapter, we show why parallel MATLAB is useful, provide a comparison of the different parallel MATLAB choices, and describe a number of applications in Signal and Image Processing: Audio Signal Processing, Synthetic Aperture Radar (SAR) Processing and Superconducting Quantum Interference Filters (SQIFs). Each of these applications have been parallelized using different methods (Task parallel and Data parallel techniques). The applications presented may be considered representative of type of problems faced by signal and image processing researchers. This chapter will also strive to serve as a guide to new signal and image processing parallel programmers, by suggesting a parallelization strategy that can be employed when developing a general parallel algorithm. The objective of this chapter is to help signal and image processing algorithm developers understand the advantages of using parallel MATLAB to tackle larger problems while staying within the powerful environment of MATLAB.