Datamining Approach for Automation of Diagnosis of Breast Cancer in Immunohistochemically Stained Tissue Microarray Images

Keerthana Prasad*, 1, Bernhard Zimmermann2, Gopalakrishna Prabhu3, Muktha Pai4
1 Manipal Centre for Information Science, Manipal University, Manipal 576104, India
2 Biomedical Optics Research Lab, University Hospital, Zurich 8091, Switzerland
3 Biomedical Engineering Department, M.I.T, Manipal University, Manipal 576104, India
4 Pathology Department, Kasturba Medical College, Manipal University, Mangalore 575001, India

Article Metrics

CrossRef Citations:
Total Statistics:

Full-Text HTML Views: 2981
Abstract HTML Views: 2241
PDF Downloads: 259
Total Views/Downloads: 5481
Unique Statistics:

Full-Text HTML Views: 1416
Abstract HTML Views: 1167
PDF Downloads: 188
Total Views/Downloads: 2771

© Prasad et al.; Licensee Bentham Open

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Manipal Centre for Information Science, Manipal University, Manipal 576104, India; Tel: 91-98453-08499; Fax: 91-820-2925033; E-mail:


Cancer of the breast is the second most common human neoplasm, accounting for approximately one quarter of all cancers in females after cervical carcinoma. Estrogen receptor (ER), Progesteron receptor and human epidermal growth factor receptor (HER-2/neu) expressions play an important role in diagnosis and prognosis of breast carcinoma. Tissue microarray (TMA) technique is a high throughput technique which provides a standardized set of images which are uniformly stained, facilitating effective automation of the evaluation of the specimen images. TMA technique is widely used to evaluate hormone expression for diagnosis of breast cancer. If one considers the time taken for each of the steps in the tissue microarray process workflow, it can be observed that the maximum amount of time is taken by the analysis step. Hence, automated analysis will significantly reduce the overall time required to complete the study. Many tools are available for automated digital acquisition of images of the spots from the microarray slide. Each of these images needs to be evaluated by a pathologist to assign a score based on the staining intensity to represent the hormone expression, to classify them into negative or positive cases. Our work aims to develop a system for automated evaluation of sets of images generated through tissue microarray technique, representing the ER expression images and HER-2/neu expression images. Our study is based on the Tissue Microarray Database portal of Stanford university at, which has made huge number of images available to researchers. We used 171 images corresponding to ER expression and 214 images corresponding to HER-2/neu expression of breast carcinoma. Out of the 171 images corresponding to ER expression, 104 were negative and 67 were representing positive cases. Out of the 214 images corresponding to HER-2/neu expression, 112 were negative and 102 were representing positive cases. Our method has 92.31% sensitivity and 93.18% specificity for ER expression image classification and 96.67% sensitivity and 88.24% specificity for HER-2/neu expression image classification.

Keywords: Automated breast cancer diagnosis, Decision support systems, Image analysis, Tissue microarray, staining expression quantification.