A supervised segmentation scheme based on multilayer neural network and color active contour model for breast cancer nuclei detection

Breast cancer nuclei detection is an impressive challenge in surgeries and medical treatments. In the microscopic image of immunohistologically stained breast tissue, cancer nuclei present a large variety in their characteristics that bring various difficulties for traditional segmentation algorithms. In this paper, we propose an efficient supervised segmentation method using a multilayer neural network (MNN) combined with a modified geometric active contour model based on Bayes error energy functional for nuclear stained breast tissue images. First, a discrimination function is constructed from color information of the desired nuclei using Fisher Linear Discriminant (FLD) analysis and a trained MNN in order to get a preliminary classification of cancer nuclei. This function is then included in the region term of the energy functional and the stopping function of the model to improve the segmentation accuracy of the detected cancer nuclei. Furthermore, the initial curve and the controlling parameters of the proposed model are estimated directly from the initial segmentation by the FLD-MNN method. The proposed segmentation scheme is tested on different microscopic breast tissue images recorded from real patients located in the Tunisian Salah Azaiez Cancer Center. The experimental results show the superiority of the proposed method when compared with other existing segmentation methods.