Using neutrosophic graph cut segmentation algorithm for qualified rendering image selection in thyroid elastography video

• Published in: Health information science and systems Vol. 5; no. 1; p. 8
• Main Authors: Guo, Yanhui, Jiang, Shuang-Quan, Sun, Baiqing, Siuly, Siuly, Şengür, Abdulkadir, Tian, Jia-Wei
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 27.10.2017; BioMed Central Ltd
• Subjects: Bioinformatics; Care and treatment; Computational Biology/Bioinformatics; Computer Science; Diagnosis; Diagnostic imaging; Health Informatics; Information Systems and Communication Service; Special Issue on Artificial Intelligence in Health and Medicine; Thyroid cancer; Thyroid ultrasound; Neutrosophic set; Indeterminate filtering; Elastogram; Graph cut
• ISSN: 2047-2501; 2047-2501
• DOI: 10.1007/s13755-017-0032-y

Recently, elastography has become very popular in clinical investigation for thyroid cancer detection and diagnosis. In elastogram, the stress results of the thyroid are displayed using pseudo colors. Due to variation of the rendering results in different frames, it is difficult for radiologists to manually select the qualified frame image quickly and efficiently. The purpose of this study is to find the qualified rendering result in the thyroid elastogram. This paper employs an efficient thyroid ultrasound image segmentation algorithm based on neutrosophic graph cut to find the qualified rendering images. Firstly, a thyroid ultrasound image is mapped into neutrosophic set, and an indeterminacy filter is constructed to reduce the indeterminacy of the spatial and intensity information in the image. A graph is defined on the image and the weight for each pixel is represented using the value after indeterminacy filtering. The segmentation results are obtained using a maximum-flow algorithm on the graph. Then the anatomic structure is identified in thyroid ultrasound image. Finally the rendering colors on these anatomic regions are extracted and validated to find the frames which satisfy the selection criteria. To test the performance of the proposed method, a thyroid elastogram dataset is built and totally 33 cases were collected. An experienced radiologist manually evaluates the selection results of the proposed method. Experimental results demonstrate that the proposed method finds the qualified rendering frame with 100% accuracy. The proposed scheme assists the radiologists to diagnose the thyroid diseases using the qualified rendering images.