Computer aided decision support system for cervical cancer classification

Conventional analysis of a cervical histology image, such a pap smear or a biopsy sample, is performed by an expert pathologist manually. This involves inspecting the sample for cellular level abnormalities and determining the spread of the abnormalities. Cancer is graded based on the spread of the abnormal cells. This is a tedious, subjective and time-consuming process with considerable variations in diagnosis between the experts. This paper presents a computer aided decision support system (CADSS) tool to help the pathologists in their examination of the cervical cancer biopsies. The main aim of the proposed CADSS system is to identify abnormalities and quantify cancer grading in a systematic and repeatable manner. The paper proposes three different methods which presents and compares the results using 475 images of cervical biopsies which include normal, three stages of pre cancer, and malignant cases. This paper will explore various components of an effective CADSS; image acquisition, pre-processing, segmentation, feature extraction, classification, grading and disease identification. Cervical histological images are captured using a digital microscope. The images are captured in sufficient resolution to retain enough information for effective classification. Histology images of cervical biopsies consist of three major sections; background, stroma and squamous epithelium. Most diagnostic information are contained within the epithelium region. This paper will present two levels of segmentations; global (macro) and local (micro). At the global level the squamous epithelium is separated from the background and stroma. At the local or cellular level, the nuclei and cytoplasm are segmented for further analysis. Image features that influence the pathologists’ decision during the analysis and classification of a cervical biopsy are the nuclei’s shape and spread; the ratio of the areas of nuclei and cytoplasm as well as the texture and spread of the abnormalities. Similar features are extracted towards the automated classification process. This paper will present various feature extraction methods including colour, shape and texture using Gabor wavelet as well as various quantative metrics. Generated features are used to classify cells or regions into normal and abnormal categories. Following the classification process, the cancer is graded based on the spread of the abnormal cells. This paper will present the results of the grading process with five stages of the cancer spectrum.

[1]  D. Cruickshank,et al.  Cervical Cancer Screening in Developing Countries , 2005 .

[2]  Doo Heon Song,et al.  Nucleus Segmentation and Recognition of Uterine Cervical Pap-Smears , 2009, RSFDGrC.

[3]  H. Averette,et al.  Cervical intraepithelial neoplasia (dysplasia and carcinoma in situ) and early invasive cervical carcinoma , 1989, CA: a cancer journal for clinicians.

[4]  Marie-Pierre Jolly,et al.  Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images , 2001, ICCV.

[5]  Henry C Kitchener,et al.  Efficacy of a prophylactic adjuvanted bivalent L1 virus-like-particle vaccine against infection with human papillomavirus types 16 and 18 in young women: an interim analysis of a phase III double-blind, randomised controlled trial , 2007, The Lancet.

[6]  Deborah B. Thompson,et al.  An automated machine vision system for the histological grading of cervical intraepithelial neoplasia (CIN) , 2000, The Journal of pathology.

[7]  L. Koss.  Diagnostic cytology and its histopathologic bases , 1968 .

[8]  B. Yener,et al.  Automated cancer diagnosis based on histopathological images : a systematic survey , 2005 .

[9]  Borivoj Vojnovic,et al.  An image analysis‐based approach for automated counting of cancer cell nuclei in tissue sections , 2003, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[10]  M. Arif,et al.  Classification of potential nuclei in prostate histology images using shape manifold learning , 2007, 2007 International Conference on Machine Vision.

[11]  Alan C. Bovik,et al.  SEGMENTING CERVICAL EPITHELIAL NUCLEI FROM CONFOCAL IMAGES USING GAUSSIAN MARKOV RANDOM FIELDS , 2003 .

[12]  L. Rodney Long,et al.  Histology image analysis for carcinoma detection and grading , 2012, Comput. Methods Programs Biomed..

[13]  Anna Fabijańska.  Normalized cuts and watersheds for image segmentation , 2012 .

[14]  Montserrat Ros,et al.  Morphological Characteristics of Cervical Cells for Cervical Cancer Diagnosis , 2012 .

[15]  Anant Madabhushi,et al.  Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[16]  Marie-Pierre Jolly,et al.  Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[17]  HPV and cervical cancer in the 2007 report. , 2007, Vaccine.

[18]  Rangaraj M. Rangayyan,et al.  Application of shape analysis to mammographic calcifications , 1994, IEEE Trans. Medical Imaging.

[19]  Adel Hafiane,et al.  Fuzzy Clustering and Active Contours for Histopathology Image Segmentation and Nuclei Detection , 2008, ACIVS.

[20]  Dan I. Belc.  Hybrid Wavelet Filter for Medical Image Compression , 2006 .

[21]  S. Moss,et al.  Cervical cancer screening in developing countries: why is it ineffective? The case of Mexico. , 1999, Archives of medical research.

[22]  Rodney Long,et al.  Computer-assisted diagnosis in cervical histopathology , 2010 .

[23]  Rebecca R. Richards-Kortum,et al.  Segmenting cervical epithelial nuclei from confocal images Gaussian Markov random fields , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[24]  Montserrat Ros,et al.  Cervical Cancer Classification Using Gabor Filters , 2011, 2011 IEEE First International Conference on Healthcare Informatics, Imaging and Systems Biology.

[25]  Zhong Li,et al.  Biomedical Image Segmentation Based on Shape Stability , 2007, 2007 IEEE International Conference on Image Processing.

[26]  Catherine Todd,et al.  Classification Cervical Cancer Using Histology Images , 2010, 2010 Second International Conference on Computer Engineering and Applications.

[27]  Cynthia Washam.  Targeting teens and adolescents for HPV vaccine could draw fire. , 2005, Journal of the National Cancer Institute.

[28]  Jung-Hua Wang,et al.  Image segmentation based on region growing and edge detection , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[29]  Anant Madabhushi,et al.  AUTOMATED GRADING OF PROSTATE CANCER USING ARCHITECTURAL AND TEXTURAL IMAGE FEATURES , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[30]  Lipi B. Mahanta,et al.  Cervix Cancer Diagnosis from Pap Smear Images Using Structure Based Segmentation and Shape Analysis , 2012 .

[31]  L. Rodney Long,et al.  Local and global Gaussian mixture models for hematoxylin and eosin stained histology image segmentation , 2010, 2010 10th International Conference on Hybrid Intelligent Systems.

[32]  Michel Jondet,et al.  Automatic measurement of epithelium differentiation and classification of cervical intraneoplasia by computerized image analysis , 2010, Diagnostic pathology.

[33]  P. Bamford.  Segmentation of Cell Images with Application to Cervical Cancer Screening , 1999 .

[34]  P H Bartels,et al.  Computerized diagnostic decision support system for the classification of preinvasive cervical squamous lesions. , 2003, Human pathology.

[35]  Paul F. Whelan,et al.  Color image segmentation using a spatial k-means clustering algorithm , 2006 .

[36]  D. Crookes,et al.  Investigation of Methodologies for the Segmentation of Squamous Epithelium from Cervical Histological Virtual Slides , 2007, International Machine Vision and Image Processing Conference (IMVIP 2007).

[37]  Ahmed Bouridane,et al.  An evolutionary snake algorithm for the segmentation of nuclei in histopathological images , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[38]  C. Chow,et al.  Automatic boundary detection of the left ventricle from cineangiograms. , 1972, Computers and biomedical research, an international journal.