Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Similarity measurement method for the classification of architecturally differentiated images
Computers and Biomedical Research
Distance measure and induced fuzzy entropy
Fuzzy Sets and Systems
Medical Image Analysis: Progress over Two Decades and the Challenges Ahead
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fitting nature's basic functions part I: polynomials and linear least squares
Computing in Science and Engineering
Gradient Vector Flow: A New External Force for Snakes
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Segmentation using fuzzy divergence
Pattern Recognition Letters
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A simple method for fitting of bounding rectangle to closed regions
Pattern Recognition
Computers in Biology and Medicine
Effective segmentation and classification for HCC biopsy images
Pattern Recognition
Fuzzy Image Processing and Applications with MATLAB
Fuzzy Image Processing and Applications with MATLAB
Learning histopathological microscopy
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Hi-index | 12.05 |
This work presents a quantitative microscopic approach for discriminating oral submucous fibrosis (OSF) from normal oral mucosa (NOM) in respect to morphological and textural properties of the basal cell nuclei. Practically, basal cells constitute the proliferative compartment (called basal layer) of the epithelium. In the context of histopathological evaluation, the morphometry and texture of basal nuclei are assumed to vary during malignant transformation according to onco-pathologists. In order to automate the pathological understanding, the basal layer is initially extracted from histopathological images of NOM (n=341) and OSF (n=429) samples using fuzzy divergence, morphological operations and parabola fitting followed by median filter-based noise reduction. Next, the nuclei are segmented from the layer using color deconvolution, marker-controlled watershed transform and gradient vector flow (GVF) active contour method. Eighteen morphological, 4 gray-level co-occurrence matrix (GLCM) based texture features and 1 intensity feature are quantized from five types of basal nuclei characteristics. Afterwards, unsupervised feature selection method is used to evaluate significant features and hence 18 are obtained as most discriminative out of 23. Finally, supervised and unsupervised classifiers are trained and tested with 18 features for the classification between normal and OSF samples. Experimental results are obtained and compared. It is observed that linear kernel based support vector machine (SVM) leads to 99.66% accuracy in comparison with Bayesian classifier (96.56%) and Gaussian mixture model (90.37%).