Use of texture-based neural network classifier in classified recognition of liver cirrhosis MRI

Abstract

Objective To explore the use of neural network classifier as a method for classified MRI diagnosis of liver cirrhosis. Methods MR images of 18 patients with confirmed diagnosis based on clinical and laboratory investigations in the Second Affiliated Hospital of Dalian Medical University were included in the study. These patients comprised 10 with cirrhosis and 8 with normal liver. Manual segmentation of these MRIs yielded 170 regions of interest (ROIs) which included 88 of liver cirrhosis and 82 of normal liver. Each of 14 texture eigenvalues (56 in total) in four directions (0°, 45°, 90° and 135°) of these two groups of ROIs were extracted with grayscale co-occurrence matrices. Performance of the 56 texture eigenvalues in discrimination of cirrhotic from normal liver tissues was evaluated by box plot, thereby 24 of 56 texture eigenvalues with good discrimination performance were obtained. A neural network BP classifier was trained with the whole set of 56 eigenvalues (Eigen set A), or a 24-value random subset (Eigen set B),or a subset containing the 24 well-discriminating eigenvalues (Eigen set C). Training set included 110 ROIs and the testing set involved 60 ROIs. Results The box plot evaluation revealed 24 texture eigenvalues in directions 0°, 45°, 90° and 135° (energy, resolution, relevance, sum-of-square difference, mean deviation,etc) that best discriminated MRI of cirrhotic liver from normal. Eigen set C yielded the highest rate of correct recognition (95.00%,57/60) as compared with eigen sets A (78.33%,47/60) and B (88.33%, 53/60, P＜0.05).Conclusion The texture-based neural netword classifier seems ideal for classifying MR imaging of liver cirrhosis from normal. Key words: Liver cirrhosis; Magnetic resonance spectroscopy; Nerve net; Signal recognition particle; Grayscale co-occurrence matrix