TITLE:
3D Gray Level Co-Occurrence Matrix Based Classification of Favor Benign and Borderline Types in Follicular Neoplasm Images
AUTHORS:
Oranit Boonsiri, Kiyotada Washiya, Kota Aoki, Hiroshi Nagahashi
KEYWORDS:
Thyroid Follicular Lesion, 3D Gray Level Co-Occurrence Matrix, Random Ferest Classifier
JOURNAL NAME:
Journal of Biosciences and Medicines,
Vol.4 No.3,
March
17,
2016
ABSTRACT:
Since the efficiency of treatment of
thyroid disorder depends on the risk of malignancy, indeterminate follicular
neoplasm (FN) images should be classified. The diagnosis process has been done
by visual interpretation of experienced pathologists. However, it is difficult
to separate the favor benign from borderline types. Thus, this paper presents a
classification approach based on 3D nuclei model to classify favor benign and
borderline types of follicular thyroid adenoma (FTA) in cytological specimens. The
proposed method utilized 3D gray level co-occurrence matrix (GLCM) and random
forest classifier. It was applied to 22 data sets of FN images. Furthermore,
the use of 3D GLCM was compared with 2D GLCM to evaluate the classification
results. From experimental results, the proposed system achieved 95.45% of the classification.
The use of 3D GLCM was better than 2D GLCM according to the accuracy of
classification. Consequently, the proposed method probably helps a pathologist
as a prescreening tool.