medical image analysis and its application
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Medical ImageAnalysis and
Its Application PRESENTED BY : SUBARNO PAL DEPARTMENT : CSE ROLL NO. : 105 UNDER GUIDANCE OF : PROF SUDIPTA ROY SEMINAR : CS681
ACADEMY OF TECHNOLOGY
What is Medical Imaging? Process of creating visual representations
of the interior of a body Usefull for clinical analysis and medical
intervention Visual representation of the function of
some organs or tissues
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Medical Imaging Modalities:
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MRI RADIOGRAPHY USG
CT-PET X-RAY
Fig 1. Different forms of Medical Image
Motive of Medical Image Analysis:
To develop computational methods and algorithms to analyze and quantify biomedical data
Image-based patient-specific modelling Understanding of disease processes
1. The history of disease evolution 2. The influence on the course of a disease of pharmacological and interventional therapeutic procedures
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Overview of the steps involved in medical image processing
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Fig 2. Overview of steps involved in Medical Image Analysis(2)
Medical Image Data Forms :
Uniformly sampled data images Regular x-y-z spatial spacing Particular meaning of the data at the
sample point depends on modality
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3D Reconstruction of 2D Image :
Source http://goo.gl/s3tq41 Source medigo.com
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Fig 3: 2D MRI with 3D reconstruction of Brain
Segmentation of Medical Image:
Atlas-Based Segmentation Shape-Based Segmentation Image-Based Segmentation Interactive Segmentation
Partitioning into different segments of biologically relevant structures.
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Segmented view of MRI Image:
gray matter in Blue
white matter in Yellow
cerebrospinal fluid in Red
Source http://goo.gl/D8zAOZ
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Fig 4. Segmentation applied on MRI of Brain
Atlases: Specific model for a population of images Parameters that are learned from a
training dataset. Single template: Model medical images
as deformed versions of a single template image
Multiple templates: Template divided for a healthy population & diseased population
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Statistical Analysis:
This includes modern topics :Computer VisionMachine LearningPattern Reconition“Mine and detect subtle changes” in the images to address clinical questions on larger data sets.
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Satistical Methods to answer Clinical Questions(1):
Group Analysis: Objective is to detect and quantize abnormalities
Shape Analysis: Analysis of geometrical properties of structures obtained from different images.
Longitudinal Studies: Studies the same person is imaged repeatedly.
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Satistical Methods to answer Clinical Questions(2):
Classification: Interested in early diagnosis of the pathology, done using texture analysis, neural network methods and data mining techniques etc.
Clustering: Analysis of Heterogeneous disorders, methodological alternatives to pattern classification.
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Image-based Physiological Modelling
Provides a predictive tool based on a physical and biological understanding of the underlying processes.
Develops a 3D, patient-specific, elasticity-based model that describes the deformation of the organ.
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CONCLUSIONS
Prompt and Rapid Actions can be taken Accuracy Increased on Medical Analysis Clustering help identifying heterogeneous
cases Early Detection Possible Efficient Algorithms are still being worked out Large Data –set handling always a concern
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References:-1) Wikipedia Medical Image Computing https://goo.gl/P23SrO 27-04-20161) Smitha P. , Shaji L. , Dr. Mini M. G. (2011) 'A Review of Medical
Image Classification Techniques', International Conference on VLSI, Communication & Instrumentation (ICVCI) , International Journal of Computer Applications® (IJCA), pp. 35-40
2) Youtube https://goo.gl/8TY2Ts 27-04-20163) Bonnie J. Nagel, Christopher D. Kroenke, 'THE USE OF
MAGNETIC RESONANCE SPECTROSCOPY AND MAGNETIC RESONANCE IMAGING IN ALCOHOL RESEARCH', National Institute on Alcohol Abuse and Alcoholism (NIAAA) http://goo.gl/D8zAOZ 27-04-2016
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Thank You
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