Development of MEXA(Multi Energy X-ray Absorptiometry) Image System for whole body
 - Multi Energy X-ray Absorptiometry(MEXA) Image Processing and Enhancement

2008. 5 ~ current

 

 

1. Osteoporosis

Over the 50 percents of women who are older than 45 years have osteoporosis

People hardly recognize this disease by themselves

Measuring BMD(Bone Mineral Density) have been done widely to detect osteoporosis in the early stage

To measure BMD correctly, it is important to segment bone area and measure de-noised biomedical signal

 

2. Mammography

X-ray image is obtained from absorption or scattering effect of photons that is emitted from X-ray energy source based on characteristic or distribution of materials that consist of the object

DEXA is the method based on two different X-ray energy levels obtained high energy and low energy level

 DEXA is an effective tool to minimize anatomic noise which is main problem in medical X-ray analysis by two district X-ray energy images

 

Fig 1. Principle of DEXA Image

 

3. Body DEXA Image Decomposition based on Attenuation Coefficient

Materials composing human body roughly divide into bone and soft tissue region which have the unique attenuation coefficient

Attenuation in permeation of X-ray can be represented by combination of bone and soft tissue

 

Fig 2. DEXA Image Decomposition

4. DEXA Image Noise Modeling and Reduction

A) DEXA image noise modeling

DEXA image noise modeling based on the analysis of characteristics of system input/output noise

DEXA image noise model

            

 

1)Source noise characteristics of DEXA image

           

2)Detector noise characteristics of DEXA image

           

 

B) DEXA Image Denoising

Noise exists multiplicative and linear additive noise in DEXA image

Denoising method consists of two : Linear noise reduction and multiplicative noise reduction using logarithm and wavelet shrinkage method

                

Fig 3. DEXA Image Noise Reduction Method

Example of Denoised DEXA Image

                                   

5. Segmentation for distal radius bone

 

Active Shape Model (ASM) is effective technique in bone segmentation

 

Chamfer matching find the initial rotation, scale, and translation of ASM segmentation

 

Using DXA decomposition, we eliminate effect of the soft tissue

 

Weight based on decomposition provide the solution for multi edge problem for ASM in distal radius bone

Fig 4. ASM segmentation system in multi edge environment

Segmemtation Result for example image

  

Fig 6. Segmentation result

 

 
 
 
 

* Contact Person: Prof. Yong Man Ro (ymro@kaist.ac.kr)
 

권주원, 조선일, 안영복, 노용만, “DEXA 차분 영상에서 생체조직의 감쇠상수 특성을 이용한 영역 분할,” 대한의용생체공학회 추계학술대회, 2008 대한전자공학회 추계학술대회, pp.703-704

권주원, 조선일, 안영복, 노용만, “시스템 입출력 잡음 특성 분석을 통한 DEXA 영상의 잡음 모델링,” 2009 한국멀티미디어학회 춘계학술발표대회 논문집, 12 1, pp. 16

   
   
   
 
 
 
 
 
 
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