The document discusses the application of Apache Spark and GPUs in processing functional MRI (fMRI) data to enhance analysis in neuroimaging. It highlights the challenges of managing large datasets generated in healthcare and the improved efficiency offered by a newly developed PySpark-based pipeline, which is four times faster than existing methods. This advancement allows for better management of the complex, high-dimensional data associated with fMRI studies and provides a pathway for scalable data analysis in medical imaging.

1. Functional MRI using Apache Spark in
Big Data Application
Ms. A. SIVASANKARI
ASSISTANT PROFESSOR
DEPARTMENT OF COMPUTER SCIENCE
SHANMUGA INDUSTRIES ARTS AND
SCIENCE COLLEGE,
TIRUVANNAMALAI.
606601.
Email: sivasankaridkm@gmail.com
Online International Conference on Advances in Computing,
Communication and Control” (ICA3C-2020)
(16 th -17 th June, 2020)
IIMT University, Meerut, UP, India.

2. Functional MRI using Apache Spark in
Big Data Application
AUTHORS DETAILS
1. Ms. A. SIVASANKARI
ASSISTANT PROFESSOR
DEPARTMENT OF COMPUTER SCIENCE
SHANMUGA INDUSTRIES ARTS AND SCIENCE COLLEGE,
TIRUVANNMALAI,
606601.
sivasankaridkm@gmail.com
2. Dr. A. DINESH KARTHIK
HEAD OF THE DEPARTMENT
DEPT OF CHEMISTRY
SHANMUGA INDUSTRIES ARTS AND SCIENCE COLLEGE,
TIRUVANNMALAI,
606601.
dineshkarthik2008@gmail.com
3. Mr. D.B. SHANMUGAM
ASSITANT PROFESSOR
DEPT OF COMPUTER APPLICATIONS
RAMAPURAM CAMPUS
SRM INSTITUTE OF SCIENCE AND TECHNOLOGY,
CHENNAI.
dbshanmugam@gmail.com

3. ABSTRACT
Technologies for ascendable investigation of terribly
massive datasets have emerge within the domain of web
computing, however are still seldom utilized in neuroimaging
despite the existence of knowledge and analysis queries in want
of economical computation tools particularly in fMRI.
During this work, we tend to gift computer code tools for
the applying of Apache Spark and Graphics process Units
(GPUs) to neuroimaging datasets, particularly providing
distributed file input for 4D NIfTI fMRI datasets in Scala to be
used in associate Apache Spark atmosphere. Health care sector
grows enormously in previous couple of decades.
Functional MRI using Apache Spark in
Big Data Application
A. SIVASANKARI - SIASC-TVM

4. The health care sector has generated huge amounts of
knowledge that has huge volume, monumental rate and huge
selection. conjointly it comes from a range of latest sources as
hospitals are currently tend to enforced electronic health record
(EHR) systems.
These sources have strained the present capabilities of
existing typical on-line database management systems. In such
state of affairs, massive information solutions supply to harness
these huge, heterogeneous and complicated information sets to
get a lot of meaningful and knowledgeable data.
Functional MRI using Apache Spark in
Big Data Application
A. SIVASANKARI - SIASC-TVM

5. INTRODUCTION
Recently, massive information science has been a hot
topic within the scientific, industrial and also the business worlds.
The care and medical specialty sciences have apace become data-
intensive as investigators are generating and victimization
massive, complex, and high dimensional and various domain
specific datasets.
This paper provides a general survey of recent progress
and advances in massive information science, healthcare, and
medical specialty analysis. Massive information science impacts,
necessary options, infrastructures, and basic and advanced
analytical tools are given very well.
Functional MRI using Apache Spark in
Big Data Application
A. SIVASANKARI - SIASC-TVM

6. Functional MRI using Apache Spark in
Big Data Application
• Imaging modalities produce a significant amount of data. For
example, in functional MRI (fMRI), which is among the most
important neuroimaging methods, blood oxygen-level
dependent (BOLD) signals of the whole brain are captured
across time.
• The focus was more on structured data that fit into tables or
relational databases, such as finance or healthcare data.
• Functional magnetic resonance imaging (fMRI) is a
technique that measures brain and breast activity data.
A. SIVASANKARI - SIASC-TVM

7. Motivation / Thesis Aspect
• The main motivation for this paper points out the
weaknesses of today's cancer detection in the context of
ambient intelligent systems, and outlines the challenges of
interpreting human intentions.
• The work of feature extraction within the computer vision
field is, although not trivial, a well-known task.
• Implementation of Banking Sector.
Functional MRI using Apache Spark in
Big Data Application
A. SIVASANKARI - SIASC-TVM

8. FUNCTIONAL MRI
Physicians use fMRI to assess however risky surgical
procedure or similar invasive treatment is for a patient and to be
told however a traditional, morbid or contused brain is
functioning. They map the brain with fMRI to spot regions joined
to essential functions like speaking, moving, sensing, or coming
up with. This is often helpful to setup for surgery and irradiation
of the brain.
Example
1.Brain network
2.Cancer detection
Functional MRI using Apache Spark in
Big Data Application
A. SIVASANKARI - SIASC-TVM

9. • Mistreatment head restraints or bite bars could injure epileptics United
Nations agency have a seizure within the scanner; bite bars might also
discomfort those with dental prostheses.
• Despite these difficulties, fMRI has been used clinically to map useful
areas, check left-right subfigure imbalance in language and memory
regions.
1. Function MRI
FMRI
Construct the 3d View Point
Functional MRI using Apache Spark in
Big Data Application
A. SIVASANKARI - SIASC-TVM

10. 2. Cancer detection
Preprocessing Engine
Filtering the MRI using
High pass filtering
Functional MRI using Apache Spark in
Big Data Application
A. SIVASANKARI - SIASC-TVM

11. Cancer Image Detection Timings
Image size Segmentation Average time
640x480 Sobel edge detection 1 ms
640x480 Canny edge detection 7 ms
640x480 Otsu threshold 2 ms
640x480 Image subtraction 1 ms
640x480 HOG detector 517 ms
Functional MRI using Apache Spark in
Big Data Application
A. SIVASANKARI - SIASC-TVM

12. Establishment of functional MRI
• The brand new fMRI informatics structures are mainly designed for
specific fMRI periods or research of which the facts size is not virtually
massive, and thus has difficulty in dealing with fMRI ‘massive records’.
• Clinicians additionally use fMRI to anatomically map the brain and find the
consequences of tumours, stroke, head and brain injury. Useful resonance
imaging (fMRI) could be a technique that measures brain activity by police
investigation associated changes in blood flow.
Functional MRI using Apache Spark in Big
Data Application
A. SIVASANKARI - SIASC-TVM

13. Working of fMRI
Functional MRI using Apache Spark in
Big Data Application
A. SIVASANKARI - SIASC-TVM

14. Experimental result
As such, these regions kind a complex integrative system during
which data is unceasingly processed and transferred between structurally
and functionally linked brain regions: the brain network. The info that are
collected in fMRI modality area unit four dimensional (4-D), and volumes of
pictures area unit non heritable across time. The pre-processing and analysis
of this immense volume of knowledge is usually time consuming and pricey,
requiring a parallelized infrastructure
Functional MRI using Apache Spark in
Big Data Application
A. SIVASANKARI - SIASC-TVM

15. Image Neuron Analysis
To merge big data analytics and medical imaging, we proposed and
developed a pipeline that can be used on a single node PC or large clusters
and that is able to process data much faster than the current methods. This
method also enables users to store the pre/post processed data in a different
data format that is compatible with big data plat-forms, especially Spark.
Functional MRI using Apache Spark in Big Data
Application
A. SIVASANKARI - SIASC-TVM

16. Validation Result
Measurement Tracking
Traceability
Validation and verification
The Elements within the ASPLA version are mapped to
components of relevant and present day automotive area standards. A
whole list of all issues can be discovered in. The focus on the specific
problems of (1) Measurement and tracking, (2) Traceability, and (3)
Verification and validation.
Functional MRI using Apache Spark in Big Data
Application
A. SIVASANKARI - SIASC-TVM

17. CONCLUSION
 I actually have categorized the modern-day computational
efforts of neuroscience experts for in dealing with the bigdata
demanding situations in 6 corporations of records
management, information visualization, Cloud garage,
computing systems, processing pipelines and processing
engines. In this Paper, I added my endeavors to address every
of the above categories, drastically for fMRI facts types.
 We have developed and successfully tested our new
PySpark-based pipeline on a single node to analyse functional
MRI data for extracting brain networks.
Functional MRI using Apache Spark in
Big Data Application
A. SIVASANKARI - SIASC-TVM

18.  The new pipeline improved the processing time,
proving itself four times faster than previous works, while
accuracy remained at the same value.
 Furthermore, ease of use, in-memory data processing,
and storage results in different data structures are important
features of this pipeline.
 Additionally, this pipeline can easily expand to several
nodes and high performance computing clusters for
massive data analysis on large datasets, which will
definitely improve the processing time and the performance
of the pipeline much more than a single node.
Functional MRI using Apache Spark in
Big Data Application
A. SIVASANKARI - SIASC-TVM