Abstract: Breast cancer is the most common form of cancer in women. An intelligent computer-aided diagnosis system can be very helpful for radiologist in detecting and diagnosing micro calcifications patterns earlier and faster than typical screening programs. In this paper, we present a system based on gabor filter based enhancement technique and feature extraction techniques using texture based segmentation and SOM(Self Organization Map) which is a form of Artificial Neural Network(ANN) used to analyze the texture features extracted. SOM determines which texture feature has the ability to classify benign, malignant and normal cases. Watershed segmentation technique is used to classify cancerous region from the non cancerous region. We have investigated and analyzed a number of feature extraction techniques and found that a combination of ten features, such as Cor-relation, Cluster Prominence, Energy, Entropy, Homogeneity, Difference variance, Difference Entropy, Information Measure, and Normalized are calculated. These features gives the distribution of tonality information and was found to be the best combination to distinguish a benign micro calcification pattern from one that is malignant and normal. The system was developed on a Windows platform. It is an easy to use intelligent system that gives the user options to diagnose, detect, enlarge, zoom, and measure distances of areas in digital mammograms. Further Using Linear Filtering Technique and the Texture Features as Mask are convolved with the segmented image .The tumor is detected using the above method and using watershed segmentation, a fair segmentation is obtained The artificial neural network with unsupervised learning together with texture based approach leads to the accuracy and positive predictive value of each algorithm were used as the evaluation indicators. 121 records acquired from the breast cancer patients at the MIAS database. The results revealed that the accuracies of texture based unsupervised learning has 0.9534 (sensitivity 0.98716 and specificity 0.9582 which was detected thorough the ROC. The results showed that the gabor based unsupervised learning described in the present study was able to produce accurate results in the classification of breast cancer data and the classification rule identified was more acceptable and comprehensible.

S.Pitchuman Angayarkanni M.C.A, M.Phil and V.Saravanan, “SOM Based Visualization Technique For Detection Of Cancerous Masses In Mammogram” International Journal of Advanced Computer Science and Applications(IJACSA), Special Issue on Artificial Intelligence, 2011. http://dx.doi.org/10.14569/SpecialIssue.2011.010305

@article{M.C.A2011,
title = {SOM Based Visualization Technique For Detection Of Cancerous Masses In Mammogram},
journal = {International Journal of Advanced Computer Science and Applications(IJACSA), Special Issue on Artificial Intelligence}
doi = {10.14569/SpecialIssue.2011.010305},
url = {http://dx.doi.org/10.14569/SpecialIssue.2011.010305},
year = {2011},
publisher = {The Science and Information Organization},
volume = {1},
number = {3},
author = {S.Pitchuman Angayarkanni M.C.A and M.Phil and V.Saravanan},
}