Submitted by :- Sridev Shyam K.V

Problem Definition

Motivation

A look into Alzheimer's facts

AD Identification - Methods - Volumetric Analysis - Shape Analysis - Structural Analysis Regions of Degradation -- Major Region – Median Temporal Lobe (Bio-Marker), Hippocampus Region. -- Cerebellum posterior Lobe, Para-central Lobe, Inferior semi-lunar lobule, cerebellum anterior Lobe, Anterior cingulate, Frontal Lobe.

Methodology

Data Set and Specification Data Set - ADNI is 10 year old consortium study to observe all the aspects of AD, Normal and MCI MRI brain image. Standards – MRI- T1 - Weighted - 3T MRI Scanner's using protocol - TR= 3000, FOV = 240*240 mm 2, with 256*256*170 mm 3 - acquisition matrix in X Y Z dimensions- slice thickness 1.2 mm, Siemens 3T MR Scanner slices

- Segmenting Tissues like GM, Segmentation Freq [Hz] Segmentation – SPM8 - Raw data - Intensity non-uniformity - Sequence parameters correction - Noise / Inhomogeneities - Brain extraction ( Skull Stripping ) Pre-processing -Inhomogenity correction - Normalization to template Time [$] - Brain Extraction brain ( European Brain Atlas) Normalization to Template Brain WM, CSF. Segmentation WM / GM / CSF Tissues

LGS =LGS = s( x) s( x)   Local Features 1. LGS - EHT 2. M-LGSLGS  1, x  0  0.x  0  g a and g b are the two consecutive gray levels in the structure. k the number of neighboring pixel, s(x) the value of the pixel

Study on LGS – Grey Matter

Study on LGS –White Matter

X-axis – Percentage; Y-axis – Local Patterns Screenshot of SRT - Normal

Screenshot of SRT - AD

Screenshot of SRT - MCI

Overall Accuracy in Classification Pattern WM GM LBP 71% 67% ALBP 63% 57% LGS 69% 75.50% LGS_M 77.50% 77% LGS+LGS_M 78.50% 80% M-LGS+ M-LGS_M 81% 79%

HPCC tool for Alzheimer's  The MRI scans which uses the raw data, and after processing the process called “ segmentation ” the data is sub categorized into White Matter (WM), Grey Matter (GM) and CGF tissues.  These WM and GM are as per standards* are divided into1000+ (1.2mm thickness) layers and this happens for both GM and WM which produces large amount of images.  This requires a high performance computing, but since this also requires a large repository this will also require a cloud which has the capability of high performance computing, thus HPCC.  This will reduce the computational time and also increase the efficiency since the data is being analyzed with large amount of data from the HPC Cloud repository.  The HPCC tool is basically a SAAS(Software as a Service) which can be accessed from anywhere in the world with a internet connectivity.  This enables the repository to be accessed easily and also the data's that are confirmed by the experts can be updated in the repository which will enhance the database and increase the accuracy of analysis compare to the standalone systems. *Standards – MRI- T1 - Weighted - 3T MRI Scanner's using protocol – TR= 3000, FOV =240*240 mm2, with 256*256*170 mm3- acquisition matrix in X Y Z dimensions- slice thickness 1.2 mm, Siemens 3T MR Scanner slices

Brief on HPCC tool for Digital Pathology (Broad perspective)  As per the IEEE CS 2022 report the High Performance Computing (HPC) and Cloud Computing will be one of the important technologies that would reshape the information exchange. This project proposes the collaboration of both the technology for a more powerful technological tool to enhance the medical field as well as all the other areas.  The Tool is a Collaboration of High Performance Computing and Cloud Technology to provide a platform to use the image processing technology to enable a tool to detect and identify diseases in its early stages with the help of Digital pathology data analytics with a global repository that can be accessed from around the world  The HPCC provides the tool as a Software As A Service (SAAS) which will enable the provision to make the service available in a global platform  The SAAS tool provides a platform for the Medical Experts and Doctors to detect and cure the diseases which make use of Digital Pathology.  The tool has been now prototyped for the Early detection of Alzheimer's Disease which is in normal case can only be detected by a Medical Expert in its 5 th stage of disease through naked eye examination of the MRI scan reports.  For a Normal Medical Expert, the person would require a large investment to use this facility in terms of hardware and database compared to a system with high performance computing capabilities and a limited Data Base  The tool helps to reduce the cost effectively for early detection of diseases and research.

Collaborating High Performance Computing and Cloud computing  The cloud being introduced in late and HPC introduced in late 1970’s are two important technologies that will changing the way information is being shared and maintained in the 21 st Century  This project proposes the collaboration of this two powerful technologies together to provide an enhanced tool that could breakthrough the way information can be analyzed, accessed and computed.  The project in its initial phase faced lot of problems mainly in the networking sector. The intensive communication which is still a problem in cloud compared to dedicated HPCs for a institution. OVERCOMING THE INTENSE COMMUNICATION ISSUE IN HPCC This project proposes the use of InfiniBand's which could solve the intense communication issues to a large extend.

HPCC SAAS Repository and its working

Future enhancements and conclusion  The HPCC tool can also be used for the research studies mainly in the field of medicine since most of the research requires faster computation over large amount of data, mainly in one of the technologies mentioned in IEEE CS Report 2022 i.e. Computational biology and bioinformatics, which requires both large amount of data processing and data analysis from large amount of data.  The tool can also utilized for pattern matching study of various diseases across the globe and also for the study of different diseases in which data from the samples are stored for future research etc.  The tool can also be used to study the changing trends of human characteristics through online data analytics mainly from data from social media platforms.