Rahmadwati, Golshah Naghdy, Montserrat Ros, Catherine Todd, Eviana Norahmawati
This paper presents a novel algorithm for computer-assisted classification of cervical cancers using digitized histology images of biopsies. Texture analysis of the nuclei structure is very important for classification of cervical cancer histology. In this paper we present a two-tier classification strategy using Gabor filter banks for local classification and abnormality spread for global taxonomy. The test data used in this work are digitized histology images of cervical biopsies acquired from the pathology laboratories in the Saiful An war Hospital in Indonesia. The images from over 500 subjects are categorized by the pathologists into five grades, benign, pre-cancer (CIN1, CIN2, CIN3) and malignant. In the algorithm developed in this work, a texture classification method using Gabor filter banks is implemented to segment the image into five possible regions: of background, normal, abnormal, basal and stroma cells. The global classification algorithm uses the segmented image for the final prognosis of the degree of malignancies from benign to malignant. The process of texture segmentation using the Gabor filter bank involves the application of filters for several spatial frequencies and orientations. The Gabor filter bank is applied to cervical histology images with six frequencies and four orientations. Feature vectors are formed, comprising the response of each pixel and its neighboring pixels to each filter. The feature vectors are then used to classify each pixel and its immediate neighbor pixels into five categories. Based on the spread of abnormalities on the epithelium layer, the cervical histology image is then classified. The classification results are then used to further classify the image into: 1) normal, 2) pre-cancer and 3) malignant. The pre-cancer is divided into: a) CIN 1, b) CIN 2 and c) CIN 3. The final system will take as input a biopsy image of the cervix containing the epithelium layer and provide the classification using our new approach, to assist the pathologist in cervical cancer diagnosis. © 2011 IEEE.
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; Pathology Anatomy, School of Medical, Brawijaya University, Malang, Indonesia
