Detection of protein conformation defects from fluorescence microscopy images

• Published in: Engineering applications of artificial intelligence Vol. 26; no. 8; pp. 1936 - 1941
• Main Authors: Guo, Peifang, Bhattacharya, Prabir
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2013
• Subjects: Algorithms; Computer-aided diagnosis; Histogram; Microscopic images; Pattern classification; Protein conformational diseases; Texture analysis; Histogram; Texture analysis; Pattern classification; Computer-aided diagnosis; Microscopic images; Protein conformational diseases
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2013.05.007

A diagnostic method for protein conformational diseases (PCD) from microscopy images is proposed when such conformational conflicts involve muscular intranuclear inclusions (INIs) indicative of oculopharyngeal muscular dystrophy (OPMD), one variety of PCD. The method combines two techniques: (1) the Histogram Region of Interest Fixed by Thresholds (HRIFT) is designed to capture the color information of INIs for basic feature extraction; (2) an automated feature synthesis, based on the HRIFT features, is designed to identify OPMD by means of Genetic Programming and the Expectation Maximization algorithm (GP-EM) for classification improvement. With variations in size, shape, and background structure, a total of 600 microscopic images are analyzed for the binary classes of healthy and sick conditions of OPMD. The integrated technique of the approach reveals a sensitivity of 0.9 and an area of 0.961 under the receiver operating characteristic (ROC) at a specificity of 0.95. Furthermore, significant improvements in classification accuracy and computational time are demonstrated by comparison with other methods.