The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
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tolerance value determined before, algorithm is continued 
otherwise it is stopped. In other words, segmentation is carried 
out according to color differences. 
After completing the determination of the feature wanted to be 
digitized and marking process it is needed to get the feature as 
vector data. Thus the data can be used in a Geographic 
Information System (GIS). This was the third problem. Raster 
to vector transformation was carried out to solve the problem. 
Semi-automatic digitizing approach that solves those three 
problems mentioned above was configured in five main steps as 
the integrity of processes. This process steps are: 
• The selection of starting point or pixel of the feature wanted 
to be digitized by the operator. 
• Performing the image segmentation of the selected pixel by 
making use of color difference with neighboring pixels. 
• Propagation of the segmentation by using the level set 
method and storing as a heap sort. 
• Getting 1 bit (1 color) masked image from those structured 
pixels. 
• Getting features’ vector data in a known format after raster 
to vector conversion from masked image. 
First four process steps were compiled using Borland C++ 
programming language and using no library files except for 
system libraries. Fifth step was carried out by the help of a 
raster to vector conversion open code that is shared in the 
internet. This code was developed and compiled by using 
Visual C++ programming language. Some functions like weed 
tolerance and coordinated transformation tools were added to 
this code. 
2.1 Image Segmentation Algorithm 
Image segmentation has been based on color difference and this 
algorithm had been made flexible by adding a tolerance value 
that could be set by operator. Thus operator can detect and 
digitize large areas at one click of the mouse by giving high 
tolerance value in high contrasted images. Segmentation 
algorithm threshold value was determined as two times of 
multiplied value of image width and length. 
Segmentation is started from the selected feature point. Value 
of neighboring pixels and their average value are computed as 
the reference, according to pixel value and/or neighboring level 
of the feature point. If neighboring value is zero, pixel’s color 
value is taken on the reference neighboring level is one, color 
value computed by averaging with the eight pixels around it is 
taken as the reference value (Eker, 2006). 
Here, color differences of the pixels in three bands (red, green, 
blue) are computed one by one and searched whether each is 
under the threshold or not. 
2.2 Storing and Propagating With Level Set Algorithm 
Starting value needed for propagation with level set or going 
along with the feature is coped with by means of the semi 
automatic nature of developed method. Propagation of the 
surface would start from the pixel which is selected by the 
operator and zero level set would be defined by the location of 
starting pixel. 
Another component that is needed for level set algorithm is the 
limit value providing the control of propagation. Solution for 
the limit was carried out by computing the color differences as 
mentioned above. 
Latter there had been one problem to carry out that was which 
neighboring pixel would be chosen to continue fitting the limit 
value. As a matter of fact in this solution of the problem Fast 
Marching method may be used. The function would go on to 
propagate hence the pixel that has the least value (Sethian, 
1998). However, what would the least value be? 
In the developed method for the question of what would the 
least value represent to, the first pixel selected by the operator 
is admitted as zero level set function and each neighboring pixel 
(the first pixels in east, west, north, south direction) is checked 
by the color difference value algorithm mentioned above and 
the distance from the zero level set (first selected pixel) is 
computed for the pixels fit the condition, thus diffusing is 
carried on hence the pixel having the least distance. 
Completing the propagation updating process should be carried 
out (Sethian, 1998). For updating process quadratic equations 
were used required for computing the difference (Sethian, 1998). 
Minimum heap sort had been used in order to store and access 
the pixels. In the structure of minimum heap sort, the image cell 
having the least distance from level 0 would be at the top. 
Certain structure is required for preserving the heap sort. 
Adding the image cell to the heap, fixing the image cell in the 
heap, being removed the image cell having the least distance 
from the heap and updating the structure on the event of every 
new image cell is added is required. 
By means of this storing structure, effectiveness has been 
increased in large volume processes such as attaining to the 
image cells, testing and computing the propagation, marking the 
image cells and storing the marked image cells (Eker, 2006). 
2.3 Raster To Vector Conversion 
Raster to vector conversion is valid for 1 bit image files which 
include two types of data (0 or 1), (ESRI, 1997). By the help of 
the developed software, extracted pixels from the image are 
marked as colored and the others are black. This new image is 
saved as a masked image in bmp format. 
Open Visual C++ code shared in the internet, was developed 
and compiled with adding additional functions to convert the 
acquired mask image file to vector data. The center and the 
border lines of the features are being converted from raster data 
to vector data by the help of an interactive interface by setting 
the connection with the main interface. A coordinated vector 
data is gained by entering the lower left comer coordinates and 
image resolution in both two dimensions (x, y) on the main 
interface. Besides, entering the weed tolerance with an 
interactive interface, it is possible to get vector data in desired 
smoothness (Eker, 2006). 
If weed tolerance is 0 all the pixels are included in calculation 
without making smoothness. When the weed tolerance is 
increased, pixels with increasing intervals are taken into 
consideration instead of all pixels and vector data becomes