Tsai, F., Chang, C.-K., Rau, J.-Y., Lin, T.-H. and Liu, G.-R. (2007) 3D Computation of Gray Level Co-Occurrence in Hyperspectral Image Cubes. In: Yuille, A., Zhu, S., Cremers, D. and Wang, Y., Eds., Energy Mini-mization Methods in Computer Vision and Pattern Recognition, 4679, Springer, Berlin Heidelberg, 429-440. http://dx.doi.org/10.1007/978-3-540-74198-5_33

has been cited by the following article:

• 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.