Summary:
The purpose of the present thesis is the study of classification algorithms for
the design of a pattern recognition system to characterize thyroid gland cancer
to benign or malignant categories, based on the processing and analysis of
cytological images. The process includes the digitization of microscopy images
from the cytological material, in specially prepared specimens, processed with
Hematoxylin-Eosin stain. The material includes twenty (20) thyroid gland biopsy
samples from corresponding cases, diagnosed by an experienced histopathologist
physician. For the design and the implementation of the system, ten (10) benign
and ten (10) malignant images of the thyroid gland were used. Initially, the
system embraces pre-processing and segmentation algorithms of cytological
images for finding the regions of interest (nuclei). Specifically, segmentation
algorithms for microscopy images were studied (thresholding with global
threshold, thresholding with adaptive threshold, Otsu method) for finding the
optimal solution and were compared with corresponding commercial packages, such
as ImPro. The resulted images of this package were distinctive at very large
degree, without overlapping of cellular nuclei. Based on ImPro segmentation,
217 segmented nuclei were obtained from benign images and 328 segmented nuclei
were found from malignant images. Next, five (5) morphological features and
twelve (12) textural features were extracted from the nuclei regions. Then, the
Minimum Distance, the Nearest Neighbor, the Bayesian and the Probabilistic
Neural Network classifiers were implemented for the classification of nuclei in
two classes. For each classifier, the optimal combination of features was
found, using the Sequential Backward Selection and the Exhaustive Search as
feature selection methods. The proposed system was evaluated by Leave-One-Out
method. The accuracy of the system in 'new' data was evaluated by External
Cross Validation method. Eight (8) features were presented statistically
significant differences (p<0.001) according to Wilcoxon statistical test. The
system classified with 95% overall accuracy the nuclei of the thyroid gland in
the two classes (benign/malignant), using the 3 Nearest Neighbor classifier.
The proposed system is capable to classify 'an unkown' nuclei with 93%
accuracy.
Keywords:
Segmentation, Feature extraction, Pattern recognition, Thyroid cancer, Microscopy
