Computer Assisted Diagnosis System Using Morphology Watershed for Breast Carcinoma Tumor – Sri Yulianto Hindriyanto
ISSN 1858-1633 2005 ICTS 137
Figure 2. local minima structure and watershed segmentation line.
4. METHODS
figure 3. the diagram of study phases.
Generally, the study is done through several; phases like the following:
1. Early processing. The image conversion process from color into grayscale. Ti is done with double
approach that is the saving of binary image. Pixel is represented into discrete value 0 and 1. Binary
image is saved as logic array.
2. The calculation of segmentation function, identification every segment nbased on the
grayness of the image.. 3. Identification and signaling the pixel of the object
in front of the image, determine the relation between the front grounds of image structure.
4. Identification and signaling the pixel of the object in the background of the image, determine the
pixel of the object which isunrelated with the other object of the image.
5. Function modification of segmentation to identify minima flat, determine the location and signal the
minima flat on the image. 6. Determining the watershed function from
segmentation modification based on the segmentation calculated from watershed function.
Figure 3 shows the phases of the study.
5. RESULT AND DISCUSION
A
B C
Figure 4 original image of breast carcinoma tumor spiculated margin, cluster microcalsification dan poorly defined mass
A. Original image of spiculated margin B. Original image of cluster microcalsification
C. Original image of poorly defined mass
Image processing study using watershed morphology segmentation is applied to analyze
mmamogaphy image in breast carcinoma tumor speculated margin, cluster micro calcification and
poorly defined mass type . The algorithm implemented using Matlab 7.0 The original image of
the three type of the breast carcinoma tumor are like Figure 4.
Determine the magnitude gradient arithmetically resulted like the following image. Figure 5 gradient
magnitude of the image of breast carcinoma tumor spiculated margin, cluster microcalsification dan
poorly defined mass. Flat minima
watershed line
basin dam
Preprocessing
Compute segmentation function
Identification and Marker of foreground pixel object
Identification and Marker of background pixel object
Segmentation function modificated for flat minima identification
Determine Watershed function from Segmentation modified
Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005
ISSN 1858-1633 2005 ICTS 138
A B
C
It give the sign to the front ground of the object by making minima flat in every image form which can be
placed using imregionalmax. Reconstruct the image using imreconstruct. The result of this process is like
the following Figure.Figure 6.
Figure 6 Image reconstruction of breast carcinoma tumor
spiculated margin, cluster microcalsification dan poorly
defined mass
A Image reconstruction of spiculated margin
B Image reconstruction of cluster microcalsification
C Image reconstruction of poorly defined mass
The arrow shows the tissue cell that change into tumor.
Determining superimposed foreground marker sfm is that the process result of the reconstruction
can be interpreted further. Figure 7 interprets the image based on sfm.
Figure 7 determining sfm on the original brast carcinoma tumor
spiculated margin, cluster microcalsification dan poorly
defined mass
A sfm image of spiculated margin breast carcinoma
tumor B sfm image cluster of breast
carcinoma ancer C sfm image of poorly defined
mass breast carcinoma cancer
The arrow shows the tissue cells which change into tumor.
The visualization of morphology segmentation result can be done with color image or transparently
according to intensity of the original image. In Figure 8, it showed the visualization of morphology
segmentation of breast carcinoma tumor image.
Figure 8. Visualization of morphology segmentation result of breast carcinoma tumor image spiculated margin, cluster
microcalsification dan poorly defined mass A Visualization of morphology segmentation result of breast
carcinoma tumor spiculated margin B Visualization of morphology segmentation result of breast
carcinoma tumor cluster C Visualization of morphology segmentation result of breast
carcinoma tumor poorly defined mass The arrow shows the tissue cell which change into tumor.
6. CONCLUSION
