Performance Identification Using Morphological Approach on Digital Mammographic Images

Title:Performance Identification Using Morphological Approach on Digital Mammographic Images

Volume: 11 Issue: 2

Author(s): Karthick Subramanian and Sathiyasekar Kumarasamy

Affiliation:

Keywords: Breast cancer, region growing algorithm, mammography, image segmentation, back propagation, feed forward network, neural network.

Abstract: Background: Digital Mammography is the most vital and successful imaging modality used by radio diagnosis method to find out breast cancer. Breast cancer is the most significant and common cause of cancer death in women. The main problem is to find the accurate and efficient method for breast cancer segmentation.

Method: The morphological method is the most important approach in image segmentation method. There are various new methods available for breast cancer image segmentation but those methods are not upto the mark. They fall behind the image segmentation.

Results: On comparing both the algorithms for segmenting the mammographic images, applying the Neural Networks algorithm will be a better option rather than applying Region Growing Algorithm. The accuracy of the segmentation is higher in the morphological image segmentation approach.

Conclusion: The results show that, the performance of morphological approach is more efficient than other methods.

Cite this article as:

Subramanian Karthick and Kumarasamy Sathiyasekar, Performance Identification Using Morphological Approach on Digital Mammographic Images, Current Signal Transduction Therapy 2016; 11 (2) . https://dx.doi.org/10.2174/1574362411666160617102315