title: Automated characterization and counting of Ki-67 protein for breast cancer prognosis: A quantitative immunohistochemistry approach.
abstract: Ki-67 protein expression plays an important role in predicting the proliferative status of tumour cells and deciding the future course of therapy in breast cancer. Immunohistochemical (IHC) determination of Ki-67 score or labelling index, by estimating the fraction of Ki67 positively stained tumour cells, is the most widely practiced method to assess tumour proliferation (Dowsett et al. 2011). Accurate manual counting of these cells (specifically nuclei) due to complex and dense distribution of cells, therefore, becomes critical and presents a major challenge to pathologists. In this paper, we suggest a hybrid clustering algorithm to quantify the proliferative index of breast cancer cells based on automated counting of Ki-67 nuclei. The proposed methodology initially pre-processes the IHC images of Ki-67 stained slides of breast cancer. The RGB images are converted to grey, L*a*b*, HSI, YCbCr, YIQ and XYZ colour space. All the stained cells are then characterized by two stage segmentation process. Fuzzy C-means quantifies all the stained cells as one cluster. The blue channel of the first stage output is given as input to k-means algorithm, which provides separate cluster for Ki-67 positive and negative cells. The count of positive and negative nuclei is used to calculate the F-measure for each colour space. A comparative study of our work with the expert opinion is studied to evaluate the error rate. The positive and negative nuclei detection results for all colour spaces are compared with the ground truth for validation and F-measure is calculated. The F-measure for L*a*b* colour space (0.8847) provides the best statistical result as compared to grey, HSI, YCbCr, YIQ and XYZ colour space. Further, a study is carried out to count nuclei manually and automatically from the proposed algorithm with an average error rate of 6.84% which is significant. The study provides an automated count of positive and negative nuclei using L*a*b*colour space and hybrid segmentation technique. Computerized evaluation of proliferation index can aid pathologist in assessing breast cancer severity. The proposed methodology, further, has the potential advantage of saving time and assisting in decision making over the present manual procedure and could evolve as an assistive pathological decision support system.
authors.affiliation: School of Medical Science & Technology, IIT Kharagpur, Kharagpur, West Bengal, India.
authors.forename: Tushar
authors.initials: T
authors.lastname: Mungle
authors.affiliation: School of Medical Science & Technology, IIT Kharagpur, Kharagpur, West Bengal, India.
authors.forename: Suman
authors.initials: S
authors.lastname: Tewary
authors.affiliation: Tata Medical Center, New Town, Rajarhat, Kolkata, West Bengal, India.
authors.forename: Indu
authors.initials: I
authors.lastname: Arun
authors.affiliation: Tata Medical Center, New Town, Rajarhat, Kolkata, West Bengal, India.
authors.forename: Bijan
authors.initials: B
authors.lastname: Basak
authors.affiliation: Tata Medical Center, New Town, Rajarhat, Kolkata, West Bengal, India.
authors.forename: Sanjit
authors.initials: S
authors.lastname: Agarwal
authors.affiliation: Tata Medical Center, New Town, Rajarhat, Kolkata, West Bengal, India.
authors.forename: Rosina
authors.initials: R
authors.lastname: Ahmed
authors.affiliation: Tata Medical Center, New Town, Rajarhat, Kolkata, West Bengal, India.
authors.forename: Sanjoy
authors.initials: S
authors.lastname: Chatterjee
authors.affiliation: Midnapur Medical College and Hospital, Midnapur, West Bengal, India.
authors.forename: Asok Kumar
authors.initials: AK
authors.lastname: Maity
authors.affiliation: School of Medical Science & Technology, IIT Kharagpur, Kharagpur, West Bengal, India. Electronic address: chandanc@smst.iitkgp.ernet.in.
authors.forename: Chandan
authors.initials: C
authors.lastname: Chakraborty
mesh_terms: D000465:Algorithms; D001331:Automation; D001943:Breast Neoplasms / Q000378:metabolism*; D005260:Female; D006801:Humans; D007150:Immunohistochemistry; D019394:Ki-67 Antigen / Q000378:metabolism*; D008962:Models, Theoretical; D011379:Prognosis
chemical_list: D019394:Ki-67 Antigen
keywords: Breast cancer; Fuzzy C-means; Immunohistochemistry; Ki-67 protein; Proliferation index; k-means