medical image analysis and its application

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

04/27/16 2Medical Image Analysis and Its Application

Medical Imaging Modalities:


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


Overview of the steps involved in medical image processing


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


3D Reconstruction of 2D Image :

Source http://goo.gl/s3tq41 Source medigo.com


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.


Segmented view of MRI Image:

gray matter in Blue

white matter in Yellow

cerebrospinal fluid in Red

Source http://goo.gl/D8zAOZ


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


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.


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.


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.


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.


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


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


Thank You


medical image analysis and its application

Technology