Mass Detection in Digital Mammograms Using Optimized Gabor Filter Bank
Breast cancer is the second major type of cancer that causes mortality among women. This can be reduced if the cancer is detected at its early stage but the existing methods result in a large number of false positives/negatives. Detection of masses is more challenging. A new method for mass detection is proposed that uses textural properties of masses. A Gabor filter bank is used for this purpose. The decision of how many Gabor filters must be there in the bank and the selection of the appropriate parameters of each individual Gabor filter is critical. Particle swarm optimization (PSO) and a clustering technique are used to design and select the optimal Gabor filter bank. Support vector machine (SVM) is used as an application oriented fitness criteria. The empirical evaluation of the method over 512 ROIs from DDSM database depicts that it yields better performance (99.41%) than the traditional Gabor filter bank and other state-of-the-art methods that exploit texture properties of masses.
KeywordsSupport Vector Machine Particle Swarm Optimization Gabor Filter Mass Detection Gabor Feature
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