Breast Cancer Detection using Crow Search Optimization based Intuitionistic Fuzzy Clustering with Neighborhood Attraction

Document Type : Research Articles


1 Department of Computer Technology, Kongu Engineering College, Perundurai, Tamilnadu, India.

2 Department of Computer Science, Government Arts and Science College, Kangeyam, Tamilnadu, India.

3 Department of Computer Applications, Kongu Engineering College, Perundurai, Tamilnadu, India.


Objective: Generally, medical images contain lots of noise that may lead to uncertainty in diagnosing the
abnormalities. Computer aided diagnosis systems offer a support to the radiologists in identifying the disease affected
area. In mammographic images, some normal tissues may appear to be similar to masses and it is tedious to differentiate
them. Therefore, this paper presents a novel framework for the detection of mammographic masses that leads to
early diagnosis of breast cancer. Methods: This work proposes a Crow search optimization based Intuitionistic fuzzy
clustering approach with neighborhood attraction (CrSA-IFCM-NA) for identifying the region of interest. First order
moments were extracted from preprocessed images. These features were given as input to the Intuitionistic fuzzy
clustering algorithm. Instead of randomly selecting the initial centroids, crow search optimization technique is applied
to choose the best initial centroid and the masses are separated. Experiments are conducted over the images taken from
the Mammographic Image Analysis Society (mini-MIAS) database. Results: CrSA-IFCM-NA effectively separated
the masses from mammogram images and proved to have good results in terms of cluster validity indices indicating
the clear segmentation of the regions. Conclusion: The experimental results show that the accuracy of the proposed
method proves to be encouraging for detection of masses. Thus, it provides a better assistance to the radiologists in
diagnosing breast cancer at an early stage.


Main Subjects

Volume 20, Issue 1
January 2019
Pages 157-165
  • Receive Date: 09 July 2018
  • Revise Date: 16 November 2018
  • Accept Date: 04 January 2019
  • First Publish Date: 04 January 2019