A hybrid approach for oestrogen receptor status evaluation

Abstract

The aim of this work is to assist pathologists in the evaluation of tumour cells in microscopic breast images where we distinguish three kinds of cells: positive tumour cells for oestrogen receptor, negative tumour cells for oestrogen receptor, and non-tumour cells. This work has proven to be very difficult because of the variability of cells’ size, shape (morphology) and distribution. Conventional methods for segmentation like thresholding and edge detection are unable to resolve this problem. The herein proposed method is a hybrid approach combining segmentation and classification to ensure better results. While the morphological processes are used for artefact elimination and cell segmentation, the classification algorithm is used to automatically classify all existing cells in the image. The paper contains also a comparative study between fuzzy c-means clustering algorithm and neural network-based classification. The proposed approach was applied on several microscopic breast cancer cells images corresponding to eight patients. The experimental results are efficient and the found values are near to those announced by experts. To better interpret these results, we performed a statistical analysis in terms of sensitivity, specificity and accuracy of detected tumour cells. The statistics proved the efficacy of the proposed approach since a percentage exceeding 90% was recorded for sensitivity, specificity and accuracy for the totality of the studied images. When using neural networks, the statistics are slightly above those gathered with fuzzy c-means. We recorded over 97% for sensitivity, specificity and accuracy of detected cells, reaching an error rate below 3%. Furthermore, it should be kept in mind that analysing breast cells images using the proposed approach gives us important information such as number of tumour cells, and number and percentage of positive tumour cells. Moreover, it is so much less time-consuming than experts’ evaluation.

Keywords:

• cells classification
• fuzzy c-means clustering
• neural network-based clustering
• mathematical morphology

The authors would like to thank Dr Khaled Ben Romdhane, Professor and Chief of Pathology Department in Salah Azaiez Institute, Tunis, Tunisia, for providing the microscopic breast cancer cells images used in this paper and also for help with statistical analysis.