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COMPUTERS IN RADIOLOGY Table of Contents   
Year : 2000  |  Volume : 10  |  Issue : 3  |  Page : 169-171
PC based diagnostic console for medical imaging


1 Dept of Health Physics, Institute of Nuclear Medicine and Allied Science, Lucknow Road, Timarpur, Delhi-110 054, India
2 NMR Center, Institute of Nuclear Medicine and Allied Science, Lucknow Road, Timarpur, Delhi-110 054, India

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Keywords: PC, computer, console, medical imaging

How to cite this article:
Mehta S B, Narayanan R V, Khushu S, Tripathi R P. PC based diagnostic console for medical imaging. Indian J Radiol Imaging 2000;10:169-71

How to cite this URL:
Mehta S B, Narayanan R V, Khushu S, Tripathi R P. PC based diagnostic console for medical imaging. Indian J Radiol Imaging [serial online] 2000 [cited 2014 Jul 24];10:169-71. Available from: http://www.ijri.org/text.asp?2000/10/3/169/30604
AII the medical image modalities come with dedicated workstation based computer systems and with executable software codes to run the program leaving no scope for improving the software depending on specific research needs. This software development was taken to display image data on personal computers (PCs) and perform image processing depending on our research needs. The PCs have standard configuration with open architecture, economical and efficient data transfer using floppy drives, CD ROMs etc. The software has been tested using the data acquired from MRI system and Gamma camera system.

The image data refers to a two-dimensional light intensity function, denoted by f(x,y) where the value of f at spatial co-ordinate (x,y) gives the intensity (brightness) of the image at that point. Since the light is a form of energy, (x,y) must be non zero and finite, that is 0 < f(x,y) < infinity. In medical images, whether it is from MRI, CT or ultrasound, all images are digitized both spatially and in amplitude. Digitization of the spatial coordinates (x,y) is reffered to as image sampling while amplitude digitization is called gray-level quantization. The matrix size (N) as well as number of discrete gray levels (G) allowed for each pixel are taken as integer value & power of two.

N = 2 n

G = 2 m

Where n, m and G denote the number of samples, the numbers of bits and number of grey level respectively. Numbers of bits required to store digital image is given by

b = N*N*m

In medical image modalities image data can be acquired in different matrix sizes, say 64 X 64, 128 X 128, 256 X 256 or higher matrix but grey level in each matrix can be 8,10 or 12 bits (i.e., in case of 12 bit grey levels from 0 to 4095 integer value).


   Materials & Methods Top


The medical images were acquired using MR Magnetom Vision (Siemens) operating at 1.5 Tesla and Gamma Camera (EC IL). The MR image data were transferred via Ethernet ports to an IBM compatible PC. The data file of an MRI image has two parts: header area and pixel area. The header area is grouped in mainly three parts - the patient's personal information, sequence details, and machine details. The information in header area contains the name of the patient, age, sex, region of interest, TE, TR, flip angle, FOV, matrix size, model of machine etc. The pixel area contains the actual image data, which is in the digital form, and the intensity values vary from 0 to 4095.

1. Computer Hardware/Software

The software is developed on IBM compatible Pentium based PC and requires the following hardware/ software configuration to run the program.

Hardware Configuration:

CPU Pentium based system -

133 MHz or above

RAM Minimum 16 MB

Hard disk 2.1 GB

Floppy drive 1.44 MB

SVGA SVGA Graphics card with

32

colour mode

Monitor 14" or larger, colour monitor CD ROM drive

Optional CD writer for long time archiving

Software Configuration:

Operating System Windows 95

Software packages Microsoft Photo Editor, Corel

Draw, Adobe PhotoShop etc.

2. Flowchart and Algorithm

Visual C++ version 5.0 compiler (32bit with debug and release mode of operation) on IBM compatible personal computer was used to develop the system. The image data file is first read through the open file option available. The data length is calculated to check the identity of data, i.e., Siemens format, DICOM format, or Gamma camera data. If the data is invalid, the program will print the message "invalid data". If the data is correct, the header part will be separated from the pixel data to create a bitmap structure. From the header, the software calculates the matrix size, converts pixel data to RGB values and stores it in bitmap format to display the data on client area as shown in [Figure - 1]. Visual C++ has features to build required menus, dialog boxes, bitmaps, etc., in the window programming. First a graphical user interface menu is created using VC++ features. This menu shows opening menus with the options - File, Edit, Image, Help etc. Each option also has a pop-up option for other functions such as saving data, reading data and other image processing options. The data is read using CArchiving in to Cdocument, which is a graphical user Interface. Two methods for painting the pixel data were tried. One is the Setpixel method in which the pixel intensity in RGB mode is used to draw the image in the window. The second method used in our program is the bitmap method, which is used to map the pixel intensity in RGB colour mode.

3. Features of Software

1. Display of images on PCs

a) MRI images in Siemens format b) MRI images in DICOM format c) ECIL gamma camera images

2. Processing of medical data images a) pseudo coloring b) pixel value in x-y plane c) image zoom d) contrast and width adjustment

3. Converting image to standard format a) BMP FORMAT b) JPEG FORMAT


   Results Top


A few images have been downloaded from the Siemens SUN workstation connected with MRI and Gamma camera for testing the program. The images were viewed and processed on the PC using the above mentioned software. The software programme is Window 95 compatible. [Figure - 2] shows the images of the brain and spine in Siemens and DICOM format on the PC. The left bottom corner (within the image) of the image shows the contrast and width intensity value of the image. The width can be changed with the "click and drag" action of the mouse cursor horizontally, and the vertical movement of the cursor varies the contrast. The text below the image (within the window) represents the intensity value and the corresponding (x, y) co-ordinates (at a particular pixel) of the image. [Figure - 3] shows a rabbit bone image using 99sub TC MDP acquired using the Gamma Camera and the same image in the enlarged window (using the zoom facility). In [Figure - 4], pseudo colouring option is used to give red colour to all pixels having pixels intensity from 300 to 500.


   Discussion Top


The software is capable of transferring the visualised images into BMP format, which can be further converted into JPEG format using available standard packages such as Corel Draw, Microsoft Photo Editor, etc.

Medical image file data length in different format (for matrix size of 256x256)

Original file Data length BMPformat JPEGformat

Siemens (format) 134 KB 193 KB 14 KB

DICOM (format) 134 KB 193 KB 14 KB

Gamma Camera Image 129 KB 193 KB 14 KB

This software package enables one to view the medical images from different modalities on a PC, thus making a distant PC a diagnostic console. The images can be easily ported to other PCs using floppies or a modem and can be displayed and processed on any Window 95 based PC, having packages such as paint PaintShop Pro, CorelDraw, etc.. Using this, 100 MRI images in JPEG format can be stored on a 1.44MB floppy disk, as compared to 8 images of the original format. The software can be used as a tool for surgical planning and radiation therapy planning in hospitals since images from the main workstation can be easily ported to any Pentium based personal computer. The system is extremely useful for teleradiology i.e. for sending these images to other centres for quick a radiological opinion[6].

 
   References Top

1.Giger M, MacMohan H. Image processing and computer-aided diagnosis. Radiologic Clinics of North America 1996; 34: 565-596.  Back to cited text no. 1    
2.Arenson RL, Chakraborty DP, Seshadri SB, Kundel HL. The digital imaging workstation. Radiology 1990; 176: 303-315.  Back to cited text no. 2  [PUBMED]  
3.Steven C. Horii. Image Acquisition. Radiologic Clinics of North America 1996; 34: 469-494.  Back to cited text no. 3    
4.Young, SJ, et al. Three-dimensional reconstructions from serial micrographs using the IBM PC. Journal of Electron Microscopy Technique 1987; 6: 207-217.  Back to cited text no. 4    
5.Prothero JS, Prothero JW. Three-dimensional reconstruction from serial sections IV: the reassembly problem. Computers and Biomedical Research 1986; 19: 361-373.  Back to cited text no. 5  [PUBMED]  
6.Mehta SB. Development of object oriented system for Medical Image Data: project report of M.E. New Delhi: Delhi University, 1997  Back to cited text no. 6    

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Correspondence Address:
S B Mehta
Dept of Health Physics, Institute of Nuclear Medicine and Allied Science, Lucknow Road, Timarpur, Delhi-110 054
India
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    Figures

[Figure - 1], [Figure - 2], [Figure - 3], [Figure - 4]

This article has been cited by
1 Soft computing techniques for medical image analysis
Mehta, S.B., Chaudhury, S., Bhattacharyya, A., Mathew, L.
IETE Technical Review (Institution of Electronics and Telecommunication Engineers, India). 2003; 20(1): 47-56
[Pubmed]



 

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