Rahmadwati, Golshah Naghdy, Montse Ros, Catherine Todd
This paper investigates cervical cancer diagnosis based on the morphological characteristics of cervical cells. The developed algorithms cover several steps: pre-processing, image segmentation, nuclei and cytoplasm detection, feature calculation, and classification. The K-means clustering algorithm based on colour segmentation is used to segment cervical biopsy images into five regions: background, nuclei, red blood cell, stroma and cytoplasm. The morphological characteristics of cervical cells are used for feature extraction of cervical histopathology images. The cervical histopathology images are classified using four well known discriminatory features: 1) the ratio of nuclei to cytoplasm, 2) the diameter of nuclei, 3) the shape factor and 4) the compactness of nuclei. Finally, the images are analysed and classified into appropriate classes. This method is utilised to classify the cervical biopsy images into normal, pre-cancer (Cervical Intraepithelial Neoplasia (CIN)1, CIN2, CIN3) and malignant. © 2012 Springer-Verlag GmbH.
Department of Electrical Engineering, Brawijaya University, Malang, Indonesia; School of Electrical, Computer and Telecommunication Engineering, University of Wollongong, NSW, Australia; Faculty of Computer Science and Engineering, University of Wollongong in Dubai, Dubai, United Arab Emirates
